The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)
As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change.
Make no mistake, AI is here to stay!
Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.
These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!
The influence of AI in scientific and academic research is an exciting development, opening the doors to more efficient, comprehensive, and rigorous exploration.
This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.
I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!
Here is everything you need to know about AI for academic research and the ones I have personally trialed on my Youtube channel.
Best ChatGPT interface – Chat with PDFs/websites and more
I get more out of ChatGPT with HeyGPT . It can do things that ChatGPT cannot which makes it really valuable for researchers.
Use your own OpenAI API key ( h e re ). No login required. Access ChatGPT anytime, including peak periods. Faster response time. Unlock advanced functionalities with HeyGPT Ultra for a one-time lifetime subscription
AI literature search and mapping – best AI tools for a literature review – elicit and more
Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!
AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.
They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.
With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.
- Elicit – https://elicit.org
- Supersymmetry.ai: https://www.supersymmetry.ai
- Semantic Scholar: https://www.semanticscholar.org
- Connected Papers – https://www.connectedpapers.com/
- Research rabbit – https://www.researchrabbit.ai/
- Laser AI – https://laser.ai/
- Litmaps – https://www.litmaps.com
- Inciteful – https://inciteful.xyz/
- Scite – https://scite.ai/
- System – https://www.system.com
If you like AI tools you may want to check out this article:
- How to get ChatGPT to write an essay [The prompts you need]
AI-powered research tools and AI for academic research
AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research.
These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from.
Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.
The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!
- Bit AI – https://bit.ai/
- Consensus – https://consensus.app/
- Exper AI – https://www.experai.com/
- Hey Science (in development) – https://www.heyscience.ai/
- Iris AI – https://iris.ai/
- PapersGPT (currently in development) – https://jessezhang.org/llmdemo
- Research Buddy – https://researchbuddy.app/
- Mirror Think – https://mirrorthink.ai
AI for reading peer-reviewed papers easily
Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in.
These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!
They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.
With AI, deciphering complex citations and accelerating research has never been easier.
- Open Read – https://www.openread.academy
- Chat PDF – https://www.chatpdf.com
- Explain Paper – https://www.explainpaper.com
- Humata – https://www.humata.ai/
- Lateral AI – https://www.lateral.io/
- Paper Brain – https://www.paperbrain.study/
- Scholarcy – https://www.scholarcy.com/
- SciSpace Copilot – https://typeset.io/
- Unriddle – https://www.unriddle.ai/
- Sharly.ai – https://www.sharly.ai/
AI for scientific writing and research papers
In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.
Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.
Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.
- Paper Wizard – https://paperwizard.ai/
- Jenny.AI https://jenni.ai/ (20% off with code ANDY20)
- Wisio – https://www.wisio.app
AI academic editing tools
In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.
Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.
- Paper Pal – https://paperpal.com/
- Writefull – https://www.writefull.com/
- Trinka – https://www.trinka.ai/
AI tools for grant writing
In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.
These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.
Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.
Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.
- Granted AI – https://grantedai.com/
- Grantable – https://grantable.co/
Free AI research tools
There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.
The best free ones at time of writing are:
- Elicit – https://elicit.org
- Connected Papers – https://www.connectedpapers.com/
- Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
- Consensus – https://consensus.app/
The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.
With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.
The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.
These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.
They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.
Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.
And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.
We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.
Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!
Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!
Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.
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How to Write a Research Paper (+Free AI Research Paper Writer)
Over the years, I've managed to vastly improve how I write research papers.
The three major game-changers for me, in terms of quality of the finished piece, have been:
- Following the research paper checklist (see below)
- Developing the thesis before starting to write
- And, more recently, using AI to improve my research paper draft
Let's break down each of these elements and produce the kind of research papers that get cited in magazines.
FREE AI research paper writer > FREE AI research paper writer >
What is a research paper, and how is it written differently?
Research papers are longer and more in-depth than essays. They require extensive research and evidence-based arguments. Research papers also typically have a more formal structure and require citations and references.When academics want to find a balanced and comprehensive view on a given topic, they usually seek a research paper.
Like most writing assignments, a research paper can be broken down into simple steps. Research papers follow the same basic writing process as explanatory or persuasive essays — but instead of making an argument or drawing greater meaning from the topic, the research paper is primarily concerned with concrete facts that may be analyzed, examined, or interpreted to better understand the paper’s central topic.
This is good news if you enjoy research: you’ll be doing a lot of it. The ultimate quality of your paper depends on you conducting thorough, complete research — and relying on reputable sources.
1. Make a checklist based on the assignment description, and fill it out with AI
Your professor has likely specified some criteria for your research paper:
- Length (in pages or words)
- Type of topic (the War of 1812, ancient Greece, agriculture, etc.)
- Elements that must be included, such as analysis, discussion, and comparison.
- Types of sources you must draw from (academic papers, encyclopedias, etc.)
- Source attribution style
- Formatting style
Go through the assignment description and create a checklist of those criteria. You can use this checklist throughout the research and writing process as well:
AI can really help you get some traction with your research paper in the preperation stage. This includes two main steps:
- Brainstorming paper topic idea
- Outlining based on your topic, basing the prompt on the assignment
2. Choose a topic you’re curious about, or use AI to help you with that
A sure way to write a boring research paper is to pick a topic you have no interest in, like summer temperatures in the desert or the life cycle of a flea. (Though someone’s probably interested in those things.)
Instead, follow your curiosity.
If your paper is for a writing class, you may have a lot of freedom to choose what you write about, so tap into your interests. Are you intrigued by the history of roller skating or the invention of the soccer cleat? Or how teen social dynamics have changed with evolving technology (think: home phones → online instant messaging → flip phones → smartphones)?
If you’re writing for a class in a subject like history, art, or science, you’ll probably have more restrictions on what you can write about — like a time period or type of art or science — but you can still use your curiosity to pick an interesting topic.
If you’re having a tough time, try brainstorming a list of things you’ve wondered about. Ask “ what’s up with… ” and see what comes to mind.
What’s up with traffic circles and why are they supposedly better for traffic patterns than a light or four-way stop?
What’s up with country music sounding more and more like hip-hop?
What’s up with people who have gluten allergies being able to eat bread in Europe but not the US?
Once you have a list, choose the topic you find most interesting (and appropriate for the assignment).
If your mind draws a blank, you can utilize AI to help you choose a topic. Let's say your course is about mid century art. You can go to a tool like Wotdtune and ask it to give you ideas for creative mid century art essays. See example below.
3. Develop your thesis (and guide your research) by asking a research question
Even though a research paper may not necessarily take a side on a topic, it still needs a thesis, aka a central idea or focus that drives the piece from beginning to end.
We wrote a whole guide on writing thesis statements , so here we’ll just give you this tip:
Use a research question to develop your thesis
A research question is a variation on the “What’s up with…” questions from the last tip — but it will zoom in more specifically on the aspect of your topic that you’re investigating.
Why were the Irish so dependent on potatoes?
Did any women in ancient Greece enjoy relative freedom and autonomy?
You may already know the answer to these questions, or you may not. Either way, they give you a place to start in your research. Once you have your question, set out to:
- Find the initial answer.
- Gather more context (the who, what, when, where, why, how) around that answer.
- Revise your research question and turn it into your thesis.
This process helps tighten your focus from a broad topic that could fill books to a specific angle that can be meaningfully explored in the few pages of your paper.
Instead of the potato famine , write about why England was to blame for the potato famine’s devastating effects on the Irish.
Instead of ancient Greece or women in ancient Greece , write about how Spartan women’s lives differed from the lives of women in Athens.
4. Skim sources and use AI to perform research for your paper
Your research question can help you quickly determine whether information is relevant to your paper. As you gather initial sources, skim them — and then use your research question to decide whether to keep or discard the source.
Does the source cover information relevant to my research question?
Yes: Keep to read later.
No: Discard and move on to the next source.
This approach will save you precious research time. You won’t waste limited hours reading sources that don’t have a single helpful fact.
If skimming is hard for you (as a deep reader, I get it), Wordtune Read can help. Paste the link to your online source, upload a scanned PDF, or copy the text, and the tool will scan and summarize for you. You can always come back later and closely read the most useful sources.
5. Make note of the most interesting facts you find.
Along with taking detailed notes of your research (complete with all the source info you need to make proper citations), highlight the most interesting facts you come across. You could stick these in a section together or mark them in a way that makes them stand out.
Why should you do this?
Because later on, one of these fascinating factoids could have a direct connection to your thesis — and make a great hook for the start of your paper. Instead of digging through all of your notes to try to remember what that interesting tidbit was, you’ll be able to find it easily.
6. Organize your research.
There are plenty of ways to organize your notes, but I suggest breaking them up into subtopics and categories.
- Subtopic: A topic related to your main topic or thesis that needs to be explained and understood by readers in order to understand your main topic or thesis. For example: Land ownership in Ireland under British rule.
- Category: An overarching concept that several subtopics fall under. For example: British restrictions on the Irish.
To start, I would focus on the subtopics and then group them into categories.
As you organize, use the formatting tools in your word processor to tag headings and subheadings. For example, all categories would be an H2 (Heading 2), while all subtopics would be an H3 (Heading 3).
Tagging your categories and subtopics this way will help you develop your outline. Just organize your categories and subtopics in a logical order, and you’ll have a skeleton of an outline ready to go.
7. Write with your research document open
No one can remember everything they found while researching — you’ll need to reference your research document throughout the writing process. No question there.
But you can make this easier (and keep your writing process efficient) by:
Keeping your research document open and in clear view.
I like to put my draft document and my research document side by side on my screen, so I can see them both at the same time.
Another approach would be to paste the information you need directly into your draft document — in the order you’ll need it. (Your outline will help you know what you need.)
8. Steal the TK trick from journalists
In the middle of drafting your paper, you find that you’re missing a fact.
You neglected to write down how many Irish people starved due to the potato famine.
You don’t know what age Spartan women were able to own property.
Instead of derailing your writing and searching for that information, write the sentence you want to write and stick a “TK” where the missing fact should go.
“TK” stands for “to come” (don't ask us why) and is a placeholder used by journalists to mark missing information they’ll fill in later. Using TK allows you to keep writing without getting off track every time you discover your research didn’t cover everything.
A whopping TK Irish people starved, thanks to the combination of famine and British oppression.
At age TK , Spartan girls became women who were able to own property, a right that their sisters in Athens did not enjoy.
9. Revise, explain, paraphrase with AI as your research/writing assistant
Using the right researching tools can get you a lot way.
If you’re ever at a loss for words — writing clunky, clumsy sentences, struggling to explain a concept, or having a hard time paraphrasing a source — Wordtune can serve as your AI sidekick.
Simply highlight the sentence in question and browse Wordtune’s suggestion for a better wording.
You can also use Wordtune Spices to come up with examples and counter arguments for whatever you're writing about or even find stats and facts, complete with source citations
Wordtune doesn’t do all of the writing for you, but it can help you sharpen your ideas on the sentence level, so you can hand in a research paper with good writing that’s still very much your own.
P.S. This article was co-written with Wordtune . Wordtune didn’t write the whole piece. Instead, it contributed ideas, suggested rephrasing alternatives, maintained consistency in tone, and of course - made the process much more fun for the writer.
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Using AI tools
AI Writing Tools | Definition, Uses & Implications
AI writing tools are artificial intelligence (AI) software applications like ChatGPT that help to automate or assist the writing process. These tools use machine learning algorithms to generate human-sounding text in response to users’ text-based prompts.
Other AI tools, such as grammar checkers , paraphrasers and summarizers serve more specific functions, like identifying grammar and spelling mistakes or rephrasing text.
Table of contents
How do ai writing tools work, what can ai writing tools be used for, implications of ai writing tools, other interesting articles, frequently asked questions about ai writing tools.
AI writing tools (chatbots, grammar checkers, etc.) use natural language processing (NLP) algorithms, machine learning , and large language models (LLMs) to generate or improve written text. These tools are trained to identify patterns in vast amounts of data. The tools then use these patterns to analyze human inputs (or prompts) and generate natural-sounding outputs (similar to how predictive text works).
Different AI tools may be programmed in different ways. For example, ChatGPT, the popular chatbot developed by OpenAI and released in November 2022, uses generative pre-trained transformers (GPTs) and was trained using reinforcement learning from human feedback (RLHF) , meaning that the tool was given feedback from humans to help guide it towards the most helpful responses.
AI writing tools have a number of helpful applications. Some of these applications are discussed below.
Interactive learning and feedback
Generative AI tools can be helpful resources for conversational learning. By inputting questions, users can engage in interactive learning at any time.
You can also prompt these tools to provide feedback on your work to improve the clarity or consistency of your writing. However, it’s important not to upload sensitive information, as user inputs are generally saved by the tool for the purposes of training future models.
Below is an example of how ChatGPT can be used for interactive learning.
Example: Using ChatGPT for interactive learning
- Tell me three facts about Carthaginian culture
- Carthaginians were known for their maritime trade and commerce, and their navy was one of the strongest in the ancient Mediterranean world. They established colonies and trading posts throughout the Mediterranean, including in Sicily, Sardinia, and North Africa.
- Carthaginians had a polytheistic religion, and their chief deity was Baal Hammon, the god of fertility, agriculture, and the sky. They also worshiped a number of other gods and goddesses, including Tanit, the goddess of love, fertility, and war.
- Carthaginians were skilled metalworkers and produced a variety of metal goods, including weapons, armor, jewelry, and household objects. They were particularly famous for their bronze work, which was highly prized in the ancient world. Carthaginian bronze was so valued that it was used by the Romans to make their famous statues and other works of art.
- Tell me three more facts about the Carthaginian navy
- The Carthaginian navy was one of the most advanced and powerful navies of the ancient world. It was instrumental in establishing Carthage as a dominant power in the Mediterranean region.
- The Carthaginians were known for their skill in shipbuilding and developed a variety of ship types for different purposes, including warships, transport ships, and cargo ships. One of their most famous ship types was the quinquereme, a large warship with five rows of oars.
- The Carthaginian navy was led by a commander called the “admiral.” The admiral was responsible for overseeing the fleet and making strategic decisions about naval warfare. One of the most famous Carthaginian admirals was Hannibal Barca, who led the Carthaginian fleet during the First Punic War against Rome.
Generating research questions and outlines
AI writing tools can be used to generate potential research questions or research paper outlines . You can then refine the tool’s responses by further prompting it or directly editing the output yourself to ensure the result makes sense and fits your requirements.
Below is an example of how ChatGPT can be used to generate potential reseach questions and research method ideas.
Example: Using ChatGPT to generate research questions and methods
- Develop three possible research questions on the following topic: “The influence of technology on second language acquisition.”
- What is the impact of technology-assisted language learning (TALL) on the acquisition of a second language?
- How do different types of technology, such as mobile applications, virtual reality, and online platforms, affect second language acquisition outcomes?
- How do individual learner characteristics, such as age, proficiency level, and motivation, interact with the use of technology in second language acquisition?
- Suggest three quantitative research methods appropriate to the second question.
- Experimental research: This research method involves manipulating an independent variable (in this case, the type of technology used) to observe its effect on a dependent variable (second language acquisition outcomes). Participants would be randomly assigned to different groups using different types of technology to learn a second language, and then their language acquisition outcomes would be measured and compared.
- Survey research: This research method involves using questionnaires to gather data from a large group of participants. In this case, a survey could be designed to gather information on participants’ use of different types of technology to learn a second language, as well as their language acquisition outcomes. This would allow for a large-scale investigation of how different types of technology are being used, and what their impact might be.
- Correlational research: This research method involves examining the relationship between two or more variables. In this case, a correlational study could be conducted to investigate whether there is a relationship between the type of technology used and language acquisition outcomes. Data could be collected from participants using different types of technology to learn a second language, and then the correlation between the type of technology and language acquisition outcomes could be calculated.
AI tools like ChatGPT and Scribbr’s free paraphrasing tool can help you paraphrase text to express your ideas more clearly, avoid repetition, and maintain a consistent tone throughout your writing.
They can also help you incorporate scholarly sources in your writing in a more concise and fluent way, without the need for direct quotations. However, it’s important to correctly cite all sources to avoid accidental plagiarism.
AI writing tools can help condense a text to its most important and relevant ideas. This can help you understand complex information more easily. You can also use summarizer tools on your own work to summarize your central argument, clarify your research question, and form conclusions.
You can do this using generative AI tools or more specialized tools like Scribbr’s free text-summarizer .
AI writing tools can be used to identify spelling, grammar, and punctuation mistakes and suggest corrections. These tools can help to improve the clarity of your writing and avoid common mistakes .
While AI tools like ChatGPT offer useful suggestions, they can also potentially miss some mistakes or even introduce new grammatical errors into your writing.
We advise using Scribbr’s proofreading and editing service or a tool like Scribbr’s free grammar checker , which is designed specifically for this purpose.
AI translation tools like Google Translate can be used to translate text from a source language into various target languages. While the quality of these tools tend to vary depending on the languages used, they’re constantly developing and are increasingly accurate.
While there are many benefits to using AI writing tools, some commentators have emphasized the limitations of AI tools and the potential disadvantages of using them. These drawbacks are discussed below.
Impact on learning
One of the potential pitfalls of using AI writing tools is the effect they might have on a student’s learning and skill set. Using AI tools to generate a paper, thesis , or dissertation , for example, may impact a student’s research, critical thinking, and writing skills.
However, other commentators argue that AI tools can be used to promote critical thinking (e.g., by having a student evaluate a tool’s output and refine it).
Consistency and accuracy
Generative AI tools (such as ChatGPT) are not always trustworthy and sometimes produce results that are inaccurate or factually incorrect. Although these tools are programmed to answer questions, they can’t judge the accuracy of the information they provide and may generate incorrect answers or contradict themselves.
It’s important to verify AI-generated information against a credible source .
While generative AI tools can produce written text, they don’t actually understand what they’re saying and sometimes produce grammar, spelling, and punctuation mistakes.
You can combine the use of generative AI tools with Scribbr’s grammar checker , which is designed to catch these mistakes.
Ethics and plagiarism
As AI writing tools are trained on large sets of data, they may produce content that is similar to existing content (which they usually cannot cite correctly), which can be considered plagiarism.
Furthermore, passing off AI-generated text as your own work is usually considered a form of plagiarism and is likely to be prohibited by your university. This offense may be recognized by your university’s plagiarism checker or AI detector .
If you want more tips on using AI tools , understanding plagiarism , and citing sources , make sure to check out some of our other articles with explanations, examples, and formats.
- Citing ChatGPT
- Best grammar checker
- Best paraphrasing tool
- ChatGPT in your studies
- Is ChatGPT trustworthy?
- Types of plagiarism
- Avoiding plagiarism
- Academic integrity
- Best plagiarism checker
- Citation styles
- In-text citation
- Citation examples
- Annotated bibliography
AI writing tools can be used to perform a variety of tasks.
Generative AI writing tools (like ChatGPT ) generate text based on human inputs and can be used for interactive learning, to provide feedback, or to generate research questions or outlines.
These tools can also be used to paraphrase or summarize text or to identify grammar and punctuation mistakes. Y ou can also use Scribbr’s free paraphrasing tool , summarizing tool , and grammar checker , which are designed specifically for these purposes.
Using AI writing tools (like ChatGPT ) to write your essay is usually considered plagiarism and may result in penalization, unless it is allowed by your university . Text generated by AI tools is based on existing texts and therefore cannot provide unique insights. Furthermore, these outputs sometimes contain factual inaccuracies or grammar mistakes.
However, AI writing tools can be used effectively as a source of feedback and inspiration for your writing (e.g., to generate research questions ). Other AI tools, like grammar checkers, can help identify and eliminate grammar and punctuation mistakes to enhance your writing.
You can access ChatGPT by signing up for a free account:
- Follow this link to the ChatGPT website.
- Click on “Sign up” and fill in the necessary details (or use your Google account). It’s free to sign up and use the tool.
- Type a prompt into the chat box to get started!
A ChatGPT app is also available for iOS, and an Android app is planned for the future. The app works similarly to the website, and you log in with the same account for both.
Yes, ChatGPT is currently available for free. You have to sign up for a free account to use the tool, and you should be aware that your data may be collected to train future versions of the model.
To sign up and use the tool for free, go to this page and click “Sign up.” You can do so with your email or with a Google account.
A premium version of the tool called ChatGPT Plus is available as a monthly subscription. It currently costs $20 and gets you access to features like GPT-4 (a more advanced version of the language model). But it’s optional: you can use the tool completely free if you’re not interested in the extra features.
ChatGPT was publicly released on November 30, 2022. At the time of its release, it was described as a “research preview,” but it is still available now, and no plans have been announced so far to take it offline or charge for access.
ChatGPT continues to receive updates adding more features and fixing bugs. The most recent update at the time of writing was on May 24, 2023.
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The best AI writing generators in 2023
These 7 ai writing tools will take your content to the next level..
Of course, all AI writing software needs human supervision to deliver the best results. Left to its own devices, it tends to produce fairly generic and frequently incorrect content, even if it can pass for something a human wrote. Now that AI tools are increasingly popular, people also seem more aware of what bland AI-produced content reads like and are likely to spot it—or at least be suspicious of content that feels like it lacks something.
I've been covering this kind of generative AI technology for almost a decade. Since AI is supposedly trying to take my job, I'm somewhat professionally interested in the whole situation. Still, I think I'm pretty safe for now. These AI writing tools are getting incredibly impressive, but you have to work with them, rather than just letting them spit out whatever they want.
So, if you're looking for an AI content generator that will help you write compelling copy, publish blog posts a lot quicker, and otherwise take some of the slow-paced typing out of writing, you've come to the right place. Let's dig in.
The best AI writing software
Jasper for businesses
Copy.ai for copywriting
Anyword for assisting you with writing
Sudowrite for fiction
Writer for a non-GPT option
Writesonic for GPT-4 content
Rytr for an affordable AI writer
How do AI writing tools work?
Search Google for AI writing software, and you'll find dozens of different options, all with suspiciously similar features. There's a big reason for this: 95% of these AI writing tools use the same large language models (LLMs) as the back end.
Some of the bigger apps are also integrating their own fine-tuning or using other LLMs like Claude . But most are really just wrappers connected to OpenAI's GPT-3 and GPT-4 APIs, with a few extra features built on top—even if they try to hide it in their own marketing materials. If you wanted to, you could even create your own version of an AI writing assistant without code using Zapier's OpenAI integrations —that's how much these apps rely on GPT.
See how one writer created an AI writing coach with GPT and other ways you can use OpenAI with Zapier .
Now this isn't to say that none of these AI-powered writing apps are worth using. They all offer a much nicer workflow than ChatGPT or OpenAI's playground , both of which allow you to generate text with GPT as well. And the better apps allow you to set a "voice" or guidelines that apply to all the text you generate. But the difference between these apps isn't really in the quality of their output. With a few exceptions, you'll get very similar results from the same prompt no matter which app you use—even if they use different LLMs. Where the apps on this list stand out is in how easy they make it to integrate AI text generation into an actual workflow.
As for the underlying LLM models themselves, they work by taking a prompt from you, and then predicting what words will best follow on from your request, based on the data they were trained on. That training data includes books, articles, and other documents across all different topics, styles, and genres—and an unbelievable amount of content scraped from the open internet . Basically, LLMs were allowed to crunch through the sum total of human knowledge to form a deep learning neural network—a complex, many-layered, weighted algorithm modeled after the human brain. Yes, that's the kind of thing you have to do to create a computer program that generates bad poems .
If you want to dive more into the specifics, check out the Zapier articles on natural language processing and how ChatGPT works . But suffice it to say: GPT and other large language models are incredibly powerful already—and because of that, these AI writing tools have a lot of potential.
What makes the best AI text generator?
How we evaluate and test apps.
All of our best apps roundups are written by humans who've spent much of their careers using, testing, and writing about software. We spend dozens of hours researching and testing apps, using each app as it's intended to be used and evaluating it against the criteria we set for the category. We're never paid for placement in our articles from any app or for links to any site—we value the trust readers put in us to offer authentic evaluations of the categories and apps we review. For more details on our process, read the full rundown of how we select apps to feature on the Zapier blog .
We know that most AI text generators rely on the various versions of GPT, and even those that don't are using very similar models, so most apps aren't going to stand out because of some dramatic difference in the quality of their output. Creating effective, human-like text is now table stakes. It was required for inclusion on this list—but not sufficient on its own.
As I was testing these apps, here's what else I was looking for:
Tools powered by GPT or a similar large language model with well-documented efficacy. In practice, this means that most but not all of the AI writing tools on this list use GPT to a greater or lesser degree. Many apps are starting to hide what models they use and claim to have a lot of secret sauce built on top (because there's a marketing advantage in being different and more powerful), but the reality is that nine times out of ten, it's the GPT API that's doing the heavy lifting.
An interface that gives you a lot of control over the text output. The more options you have to influence the tone, style, language, content, and everything else, the better. I didn't want tools where you just entered a headline and let the AI do the rest; these are all tools that you collaborate with, so you can write great copy quickly. The best AI writing tools also let you set a default brand voice that's always on.
Ease of use. You shouldn't have to fight to get the AI to do what you want. With AI writing software like this, there will always be some redoing and reshaping to get the exact output you want, but working with the AI shouldn't feel like wrangling a loose horse. Similarly, great help docs and good onboarding were both a major plus.
Affordability. ChatGPT is currently free, and all these tools are built on top of an API that costs pennies . There was no hard and fast price limit, but the more expensive tools had to justify the extra expense with better features and a nicer app. After all, almost every app will produce pretty similar outputs regardless of what it costs.
Apps that weren't designed to make spam content. Previous text-generating tools could " spin " content by changing words to synonyms so that unscrupulous website owners could rip off copyrighted material and generally create lots of low-quality, low-value content. None of that on this list.
Even with these criteria, I had more than 40 different AI writing tools to test. Remember: it's relatively easy for a skilled developer to build a wrapper around the GPT API, so I had to dig deep into each one to find out if it was any good or just had a flashy marketing site.
I tested each app by getting it to write a number of different short- and long-form bits of copy, but as expected, there were very few meaningful quality differences. Instead, it was the overall user experience, depth of features, and affordability that determined whether an app made this list.
The best AI writing generators at a glance
Best ai writing generator for businesses, jasper (web).
One of the most mature and feature-filled options on the list
Integrates with Grammarly, Surfer, and its own AI art generator
Expensive given that all the apps use similar language models
Jasper (formerly Jarvis) is one of the most feature-filled and powerful AI content generators. It was among the first wave of apps built on top of GPT, and its relative longevity means that it feels like a more mature tool than most of the other apps I tested. It's continued to grow and develop in the months since I first compiled this list.
If you have a business and budget isn't your primary concern, Jasper should be one of the first apps you try. It's pivoted to mostly focus on marketing campaigns rather than just generating generic AI content. That's not a bad thing, but it means that plans now start at $49/month for individual creators and $125/month for teams.
Jasper has also moved away from just being a GPT app. It claims to combine "several large language models" including GPT-4, Claude 2, and PaLM 2, so that "you get the highest quality outputs and superior uptime." While I can't say that I noticed a massive difference between Jasper's output and any other app's, it does give you a few solid controls so that your content matches your brand.
You can create a brand Voice and Tone by uploading some appropriate sample text. Based on a few examples of my writing, Jasper created a style that "emphasizes a casual, conversational tone with humor, personal anecdotes, listicles, informal language, expertise in various subjects, and a call to action for an engaging and approachable brand voice." I don't think that's a bad summary of the content I fed in, and its output for a few test blog posts like "The Enduring Popularity of Top Gun" felt closer to my writing than when I asked it to use a generic casual tone of voice. Similarly, there's a Knowledge Base where you can add facts about your business and products so Jasper gets important details right.
While other apps also offer similar features, Jasper's seemed to work better and are fully integrated with the rest of the app. For example, you can create entire marketing campaigns using your custom brand voice. Put a bit of work into fine-tuning it and uploading the right assets to your knowledge base, and I suspect that Jasper really could create some solid first drafts of marketing materials like blog outlines, social media campaign ads, and the like.
Otherwise, Jasper rounds things out with some nice integrations. It has a built-in ChatGPT competitor and AI art generator (though, again, lots of other apps have both), plays nice with the SEO app Surfer , and there's a browser extension to bring Jasper everywhere.
You can also connect Jasper to thousands of other apps using Zapier . Learn more about how to automate Jasper , or try one of the pre-built workflows below.
Create product descriptions in Jasper from new or updated Airtable records
Create Jasper blog posts from new changes to specific column values in monday.com and save the text in Google Docs documents
Run Jasper commands and send Slack channel messages with new pushed messages in Slack
Jasper pricing: Creator plan from $49/month with one brand voice and 50 knowledge assets. Teams plan starts at $125/month for three seats, three brand voices, and 150 knowledge assets.
Best AI writing app for AI copywriting
Has an affordable unlimited plan for high-volume users
Workflow actively solicits your input, which can lead to higher quality content
Expensive if you don't produce a lot of content
Pretty much anything Jasper can do, Copy.ai can do too. It has brand voices, an infobase, a chatbot, and team features (though there isn't a browser extension). Consider it the Burger King to Jasper's McDonalds.
And like the Home of the Whopper, Copy.ai appeals to slightly different tastes. While I could argue that Copy.ai has a nicer layout, the reality is it's geared toward a slightly different workflow. While Jasper lets you and the AI loose, Copy.ai slows things down a touch and encourages you to work with its chatbot or use a template that asks some deliberate, probing questions. For creating website copy, social media captions , product descriptions, and similarly specific things, it makes more sense. But for content marketing blog posts and other long-form content, it might annoy you.
The other big difference is the pricing. While both offer plans for $49/month, Copy.ai includes five user seats and unlimited brand voices. For a small team working with multiple brands, it can be a lot cheaper. Also, if you're looking for a free AI writing generator, Copy.ai also offers a free plan that includes 2,000 words per month.
Overall, there are more similarities than differences between Jasper and Copy.ai , and both can create almost all the same kinds of text. Even when it came to analyzing my voice, they both came to pretty similar conclusions. Copy.ai decided that, to mimic me, it had to "focus on creating content that is both educational and entertaining, using a conversational tone that makes readers feel like they're having a chat with a knowledgeable friend" and "not to be afraid to inject some humor or personal anecdotes." If you're in doubt, try them both out and then decide.
Copy.ai also integrates with Zapier , so you can do things like automatically sending content to your CMS or enriching leads straight from your CRM. Here are a couple examples to get you started.
Add new blog posts created with Copy.ai to Webflow
Enrich new Salesforce leads with Copy.ai
Copy.ai pricing: Free for 2,000 words per month; from $49/month for the Pro plan with 5 users and unlimited brand voices.
Best AI writing assistant
Makes it very easy for you to include specific details, SEO keywords, and other important information
Engagement scores and other metrics are surprisingly accurate
Can be slower to use
Pretty expensive for a more limited set of features than some of the other apps on this list
While you can direct the AI to include certain details and mention specific facts for every app on this list, none make it as easy as Anyword. More than any of the others, the AI here feels like an eager and moderately competent underling that requires a bit of micromanaging (and can also try to mimic your writing style and brand voice), rather than a beast that you have to tame with arcane prompts.
Take one of its main content-generating tools: the Blog Wizard. Like with Copy.ai, the setup process requires you to describe the blog post you want the AI to create and add any SEO keywords you want to target. Anyword then generates a range of titles for you to choose from, along with a predicted engagement score.
Once you've chosen a title—or written your own—it generates a suggested outline. Approve it, and you get the option for it to create an entire ~2,000-word blog post (boo!) or a blank document where you can prompt it with additional instructions for each section of the outline, telling it things like what facts to mention, what style to take, and what details to cover. There's also a chatbot-like research sidebar that you can ask questions of and solicit input from. While certainly a slower process than most apps, it gives you a serious amount of control over the content you're creating.
Anyword is definitely aimed at marketers, and its other tools—like the Data-Driven Editor and the Website Targeted Message—all allow you to target your content toward specific audiences and give things engagement scores. While I certainly can't confirm the validity of any of these scores, they at least pass the sniff test. I generally thought the AI-generated content that Anyword scored higher was better—and even when I disagreed, I still liked one of the top options.
Anyword pricing: Starter plan from $49/month for 1 user and 1 brand voice.
Best AI writing tool for writing fiction
The only AI tool on the list explicitly aimed at writing fiction
Super fun to use if you've ever wanted to play around with fiction
It's still an AI text generator, so it can produce nonsensical metaphors, clichéd plots, incoherent action, and has a short memory for details
Very controversial in fiction writing circles
When I saw Sudowrite's marketing copy, I didn't think for a second it would make it onto this list. Then I tried it and…I kind of love it. Sudowrite is a totally different tool than all the others on this list because it's aimed at fiction writers. And with that, comes a lot of controversy. Sudowrite has been called " an insult to writers everywhere " and has been generally dismissed as a tool for hacks by a lot of Very Online writers. And while it's true that it's nowhere close to replacing a human author, it's fun, functional, and can genuinely help with writing a work of fiction.
The Story Engine feature, which allows you to generate a full work of fiction over a few days by progressively generating each story beat, has attracted the most attention ( it works but takes lots of hand-holding and your novel will be weird ). But I prefer its assistive tools.
Let's start with Describe. Select a word or phrase, click Describe , and the AI will generate a few suggestions for the sight, smell, taste, sound, and touch of the thing, as well as a couple of metaphors. If you're the kind of writer who struggles to add sensory depth to your short stories, it can help you get into the habit of describing things in more interesting ways.
Then there's Brainstorm. It allows you to use the AI to generate possible dialogue options, character names and traits, plot points, places, and other details about your world from your descriptions and cues. If you know you want a big hairy guy with a huge sword but can't think of a good name, it can suggest a few, like Thorgrim and Bohart.
And these are just scratching the surface. Sure, if you over-rely on the AI to solve all your problems, you'll probably end up with an impressively generic story. But if you use it as a writing buddy to bounce ideas off and get you out of a rut, it's got serious potential.
Best of all, Sudowrite is super easy to use. The onboarding, tool tips, and general helpful vibe of the app are something other developers could learn from.
Sudowrite pricing: Hobby & Student plan from $19/month for 30,000 AI words/month.
Best AI text generator for a non-GPT option
Not based on GPT, so free of a lot of the controversy surrounding LLMs
Surprisingly capable as an editor, making sure your team sticks to the style guide and doesn't make any wild claims
Requires a lot more setup to get the most from
GPT comes with quite a lot of baggage. OpenAI has been less than transparent about exactly what data was used to create the various versions of GPT-3 and GPT-4, and it's facing various lawsuits over the use of copyrighted material in its training dataset. No one is really denying that protected materials— potentially from pirated databases —were used to train GPT; the question is just whether or not it falls under fair use.
For most people, this is a nebulous situation filled with edge cases and gray areas. Realistically, it's going to be years before it's all sorted out, and even then, things will have moved on so far that the results of any lawsuit are likely to be redundant. But for businesses that want to use AI writing tools without controversy attached, GPT is a no-go—and will be for the foreseeable future.
Which is where Writer comes in.
Feature-wise, Writer is much the same as any of my top picks. (Though creating a specific brand voice that's automatically used is an Enterprise-only feature; otherwise, you have to use a lot of checkboxes in the settings to set the tone.) Some features, like the chatbot, are a little less useful than they are in the GPT-powered apps, but really, they're not why you'd choose Writer.
Where it stands out is the transparency around its Palmyra LLM . For example, you can request and inspect a copy of its training dataset that's composed of data that is "distributed free of any copyright restrictions." Similarly, Palmyra's code and model weights (which determines its outputs) can be audited, it can be hosted on your own servers, and your data is kept secure and not used for training by default. As an AI-powered tool, it's as above board as it comes.
In addition to generating text, Writer can work as a company-specific Grammarly-like editor, keeping on top of legal compliance, ensuring you don't make any unsupported claims, and checking that everything matches your style guide—even when humans are writing the text. As someone who routinely has to follow style guides, this seems like an incredibly useful feature. I wasn't able to test it fully since I don't have a personal style guide to input, but Writer correctly fixed things based on all the rules that I set.
In side-by-side comparisons, Writer's text generations sometimes felt a little weaker than the ones from Jasper or Copy.ai, but I suspect a lot of that was down to how things were configured. Writer is designed as a tool for companies to set up and train with their own data, not run right out of the box. I'd guess my random blog posts were a poor test of how it should be used in the real world.
Writer also integrates with Zapier , so you can use Writer to create content directly from whatever apps you use most. Learn more about how to automate Writer , or take a look at these pre-made workflows.
Create new outlines or drafts in Writer based on briefs from Asana
Generate marketing content from project briefs in Trello
Writer pricing: Team from $18/user/month for up to 5 users; after that, it's an Enterprise plan.
Best AI text generator for GPT-4 content
Allows you to select what GPT model is used to generate text
Generous free plan and affordable paid plans
A touch too focused on SEO content for my taste
While almost all the tools on this list use GPT, most are pretty vague about which particular version of it they use at any given time. This matters because the most basic version of the GPT-3.5 Turbo API costs $0.002/1K tokens (roughly 750 words), while GPT-4 starts at $0.06/1K tokens, and the most powerful version costs $0.12/1K tokens. All this suggests that most apps may not use GPT-4 in all circumstances, and instead probably rely on one of the more modest (though still great) GPT-3 models for most text generation.
If having the latest and greatest AI model matters to you, Writesonic is the app for you. Writesonic doesn't hide what AI model it uses. It even allows you to choose between using GPT-3.5 and GPT-4, at least on Business plans.
Whether the content you create will benefit from the extra power of GPT-4 or not depends. In my experience using GPT-4 through ChatGPT, the latest model is more accurate and, essentially, more sensible in how it responds. If you're churning out low-stakes copy variations for your product listings, you likely won't see much improvement. On the other hand, for long-form original blog posts, it could make a difference. Either way, the transparency in which model you're using at any given time is a huge bonus.
Feature-wise, Writesonic is much the same as any of the other apps on this list, with a Google Docs-style editor, the option to set a brand voice, a few dozen copy templates, a chatbot, a browser extension, and Surfer integration. It's cool that you can set reference articles when you're generating a blog post, but it introduces the real possibility of inadvertent plagiarism if you aren't careful with how you use it. (Its most offbeat feature is a surprisingly solid AI-powered custom chatbot builder that's due to be spun out into its own app soon.) Overall, it's pretty nice to use and skews more toward SEO-optimized content marketing—but like with all the apps, you can use it to generate whatever you want.
Writesonic also integrates with Zapier , so you can send new copy to any of the other apps you use in your writing workflow. Learn more about how to automate Writesonic , or get started with one of these examples.
Create a Google Doc with new content from Writesonic
Generate product descriptions with Writesonic from spreadsheet rows in Google Sheets
Writesonic pricing: Free for 10,000 GPT-3.5 words per month; Business from $19/month for 200,000 Premium words or 33,333 GPT-4 words.
Best free AI writing generator (with affordable upgrades)
A solid free plan and a cheap high-volume plan (though Writesonic offers better value for an unlimited plan)
It includes a basic AI art generator as part of every plan
The app is more basic than more expensive offerings
Unlimited plan isn't very competitive
Most of the apps on this list are aimed at professionals, businesses, and anyone else with a budget. The Jasper, Copy.ai, and Anyword plans I considered all started at $49/month. That isn't exactly a hobbyist-friendly sum of money, so if you want to explore AI text generators without spending as much, give Rytr a go.
There's a free plan that's good for 10,000 characters (around 2,500 words) per month, and it includes a lot of the features, like a plagiarism checker, and a few AI-generated images. The Saver plan starts at $9/month and allows you to generate 100,000 characters (around 25,000 words) per month. On that plan, you're also able to generate up to 20 images a month, which many other apps charge extra for. (There's also an unlimited plan for $29/month, but at that point, Writesonic is a better value.)
Feature-wise, there are some trade-offs. Rytr is a little less competent at generating long-form content without you guiding it through the process, and there are fewer templates for specific things. The interface also isn't as polished, and there isn't as much hand-holding to get you started. Still, as Rytr is using GPT like almost all the other apps on this list, you should be able to get it to produce substantially similar output.
Rytr Pricing: Free plan for 10,000 characters/month and lots of other features; Saver plan from $9/month for 100,000 characters; Unlimited plan from $29/month.
Other AI writing tools to consider
With so many AI text-generating tools out there, a few good ones worth considering didn't make this list, only because they didn't meet my initial criteria in some way. If none of the AI writers I chose fit the bill for you, here are a few other options worth looking into:
ChatGPT is surprisingly competent and fun to use. And best of all, it's free. ( Google Bard is a little less excellent on the content production side.)
Wordtune and Grammarly are both great tools for editing and improving your own writing . GrammarlyGO just isn't as flexible as my other picks.
Notion AI adds a powerful AI tool directly into Notion. If you already use Notion, it's worth checking out, but it's a lot to learn if you just want a text generator. (Same goes for AI within any other Notion alternative, like Coda AI .)
Surfer and Frase are both AI-powered SEO tools . They fell slightly out of scope for this list, but they can both help you optimize and improve your content—AI-generated or not.
All of the apps on this list offer at the very least a free trial, so I'd suggest trying some of them out for a few minutes until you find the one that seems to work best with your workflow.
How to use OpenAI's GPT to spark content ideas
How to create an AI writing coach with GPT and Zapier
8 ways real businesses are using AI for content creation
How to detect AI-generated content
The best AI marketing tools
This article was originally published in April 2023. The most recent update was in September 2023.
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Harry Guinness is a writer and photographer from Dublin, Ireland. His writing has appeared in the New York Times, Lifehacker, the Irish Examiner, and How-To Geek. His photos have been published on hundreds of sites—mostly without his permission.
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The 5 Best AI Tools for Postgraduate Research
What’s the most challenging part of working towards your postgraduate degree? For many students, combing through mountains of research presents an insurmountable obstacle.
For one thing, the sheer volume of research articles you have to read is overwhelming. On top of that, research material isn’t exactly an easy read, and sifting through each article to glean the relevant information you need takes a great deal of time and effort. Organizing your thoughts and then putting them on paper in a thoughtful, meaningful – and academic – way is yet another challenge in postgraduate research.
Thankfully, artificial intelligence (AI) can help make your postgraduate study life a whole lot easier. In this article, we’ll explore 5 of the best AI tools for academic researchers and postgraduate students so that you save time and energy and stay motivated!
5 Best AI Tools for Postgraduate Research
1. semantic scholar, ai-powered academic search engine.
Most people know about Google Scholar: the power of Google applied to research papers. If you’re doing any form of scientific research though, you might want to give Semantic Scholar a go. This AI-powered search and discovery tool allows you to stay up to date with more than 200 million academic papers sourced from publisher partnerships, data providers, and web crawls.
Its AI algorithms help you to discover hidden connections and links between research topics, and recommend similar papers based on the research you’ve already saved, to generate more relevant search results for you.
It can also automatically generate single-sentence summaries of each paper to help you prioritize which academic papers to read in-depth, so you spend your time on what matters the most.
AI-powered Research Organization
Being able to scour the web for online research is a gift. But with it comes two issues – the volume of information available, and the fact that all of this information comes in a range of formats, including blogs, articles, videos, infographics, and images. Identifying and sorting all the information that is relevant to different aspects of your research can be a time-consuming task on its own.
Bit.ai helps you identify and save relevant research – even interactive, media-rich research. As a cloud-based collaborative platform, it also lets you easily share this content with fellow co-researchers in real-time.
Benjamin Franklin once said, “For every minute spent organizing, an hour is earned.” When you have all your research notes and references in one easy space, it keeps you more organised, and more focused as a result.
Ai-powered research summarization.
Wouldn’t it be amazing if you could get someone to read all the academic articles you’ve saved and pull out the most important points so that you don’t have to spend hours skim-reading articles to find the most useful ones? Happily, Scholarcy does just that.
Using deep learning technology, it reads your articles, reports, and book chapters, breaking them down into bite-sized sections and highlighting key information such as important findings, limitations, and comparisons with earlier studies. Scholarcy lets you quickly screen the literature and find out, at a glance, how relevant a document is to your research. Feedback from researchers and students shows that Scholarcy can reduce the time screening articles by up to 70%. That leaves you more time to dive into the most relevant papers in detail.
Scholarcy also has a browser extension that works with both open access and your subscription services. It turns all citations into links so you can effortlessly jump straight to related research.
AI-powered Citation Evaluation
Researching and citing information isn’t always enough. It’s the context of that cited research that is key. That’s where Scite comes in. This handy tool not only lets you see how many times an article has been cited, and in what context, but also helps you uncover other related research.
In the continually evolving world of scientific research, Scite helps you keep track of whether an article has been supported or questioned by other authors, even bringing relevant editorial notices or retractions to your attention.
AI-powered Research Paper Writing
Trinka is an AI-powered writing assistant created with academic writing in mind, meaning it’ll spot errors in technical and scientific writing that other common grammar and spelling tools may miss. So whether you’re working on a paper in the field of medicine or economics, Trinka will be able to recommend improvements relevant to your particular subject.
Trinka will also identify and correct your vocabulary, grammar, spelling, syntax use, tone, and more. It can even make suggestions to make your academic paper more compliant with the APA or AMA style guides.
Research is what powers the world to move forward, and now AI technology is powering scientific discovery forward at an even faster rate. Research processes that would usually take hours digging through publication after publication can now be done in a fraction of the time. So if you’re still doing research the old-fashioned way, save yourself some time – and sanity! – and try some of these AI-powered tools.
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- Published: 24 February 2023
Artificial intelligence in academic writing: a paradigm-shifting technological advance
- Roei Golan ORCID: orcid.org/0000-0002-7214-3073 1 na1 ,
- Rohit Reddy 2 na1 ,
- Akhil Muthigi 2 &
- Ranjith Ramasamy 2
Nature Reviews Urology volume 20 , pages 327–328 ( 2023 ) Cite this article
- Preclinical research
- Translational research
Artificial intelligence (AI) has rapidly become one of the most important and transformative technologies of our time, with applications in virtually every field and industry. Among these applications, academic writing is one of the areas that has experienced perhaps the most rapid development and uptake of AI-based tools and methodologies. We argue that use of AI-based tools for scientific writing should widely be adopted.
The use of artificial intelligence (AI) in academic writing can be divided into two broad categories: those that assist authors in the writing process; and those that are used to evaluate and assess the quality and validity of written work. Tools such as natural language processing that can understand and generate human-like language can assist authors in writing and preparing manuscripts (Box 1 ). Tools such as plagiarism detection software and automated peer-review platforms can assist reviewers and editors in evaluating the quality of the manuscript. Furthermore, automated peer-review platforms can quickly and objectively evaluate large numbers of manuscripts, potentially reducing their workload 1 .
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The manuscript was edited for grammar and structure using the advanced language model ChatGPT. The authors thank S. Verma for addressing inquiries related to artificial intelligence.
These authors contributed equally: Roei Golan, Rohit Reddy.
Authors and Affiliations
Department of Clinical Sciences, Florida State University College of Medicine, Tallahassee, FL, USA
Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL, USA
Rohit Reddy, Akhil Muthigi & Ranjith Ramasamy
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Correspondence to Ranjith Ramasamy .
R.R. is funded by the National Institutes of Health Grant R01 DK130991 and the Clinician Scientist Development Grant from the American Cancer Society. The other authors declare no competing interests.
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Golan, R., Reddy, R., Muthigi, A. et al. Artificial intelligence in academic writing: a paradigm-shifting technological advance. Nat Rev Urol 20 , 327–328 (2023). https://doi.org/10.1038/s41585-023-00746-x
Published : 24 February 2023
Issue Date : June 2023
DOI : https://doi.org/10.1038/s41585-023-00746-x
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Title: supercharging academic writing with generative ai: framework, techniques, and caveats.
Abstract: Academic writing is an indispensable yet laborious part of the research enterprise. This Perspective maps out principles and methods for using generative artificial intelligence (AI), specifically large language models (LLMs), to elevate the quality and efficiency of academic writing. We introduce a human-AI collaborative framework that delineates the rationale (why), process (how), and nature (what) of AI engagement in writing. The framework pinpoints both short-term and long-term reasons for engagement and their underlying mechanisms (e.g., cognitive offloading and imaginative stimulation). It reveals the role of AI throughout the writing process, conceptualized through a two-stage model for human-AI collaborative writing, and the nature of AI assistance in writing, represented through a model of writing-assistance types and levels. Building on this framework, we describe effective prompting techniques for incorporating AI into the writing routine (outlining, drafting, and editing) as well as strategies for maintaining rigorous scholarship, adhering to varied journal policies, and avoiding overreliance on AI. Ultimately, the prudent integration of AI into academic writing can ease the communication burden, empower authors, accelerate discovery, and promote diversity in science.
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Artificial Intelligence Tools for Detection, Research and Writing
- AI Detection
Prompts and Places to Start
Research and literature review tools, writing tools, grant writing tools, meeting and video tools, other useful tools.
- Texas Tech Resources
- AgentGPT Assemble, configure, and deploy autonomous AI agents in your browser that learn on their own without requiring prompts.
- ChatGPT For those curious to try ChatGPT, this site provides a link to do so. ChatGPT is trained to interact in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
- ChatGPT Prompt Genius Discover, share, import, and use the best prompts for ChatGPT & save your chat history locally.
- Chatsonic Chatsonic is a conversational AI NLP processor that attempts to address the limitations of ChatGPT.
- Claude This AI chatbot is designed to be more ethical and safer to use than other chatbots and may be more suitable for users who are concerned about chatbots doing objectionable things.
- Forefront Forefront provides free access to ChatGPT-4, 3.5, as well as other models. It enables you to create and use personas, generate images, and share content with colleagues.
- FreedomGPT Download this free chatbot to fun locally on your computer to enjoy faster response and the privacy of not having to share your data with big tech companies.
- GPT4All This uncensored chatbot is a free-to-use, locally running, privacy-aware chatbot that can be downloaded and trained on your own data without risking your privacy to big tech companies.
- Open Assistant This free, independent, uncensored chatbot has fewer guardrails and may respond more freely to questions than more conventional bots.
- Perplexity This free AI software operates in a manner similar to ChatGPT but it is not as popular so you may not have to wait as long to access it.
- Pickaxe Pickaxe is a platform to building, managing, and deploying AI prompts. It's a hub for prompt engineers and AI pioneers.
- PromptHero This searchable index of prompts allows you to find the right words to get the best results from AI programs like ChatGPT and DALL-E.
- WebChatGPT Augment your ChatGPT prompts with more current relevant results from the web. This free extension adds relevant web results to your prompts to ChatGPT for more accurate and up-to-date conversations.
- You.com This tool allows you to search, chat, and create with AI.
- Connected Papers This visualization tool allows users to submit a paper and it will create a graphic chart of similar papers allowing you to visualize trends. It includes a Prior Works feature to trace earlier works, Derivative Works to find literature reviews, and State of the Art Works to find subsequent papers.
- Consensus Consensus is a search engine that uses AI to extract and distill findings directly from scientific research. It reads the papers for you and extracts key results.
- Counterargument Generator An AI tool designed to help generate counterarguments to a statement or assertion.
- DataSeer Analyze and extract the data you need from diagrams & datasheets: DataSeer provides automated visualization software that digitizes unstructured data trapped in 2D images and creates a digital twin database with API access.
- Dimensions AI This database provides free access to over 100 million publications and preprints to help you find exactly what you need, quickly and easily. It also shows the context - with citations, news and social media mentions, and links to funded grants and patents.
- Dovetail Accurately auto-transcribe interviews, build your taxonomy and visualize your research.
- Elicit: The AI Research Assistant Elicit uses language models to help automate research workflows, like parts of a literature review. Elicit can find relevant papers without perfect keyword match, summarize takeaways from the paper specific to your question, and extract key information from the papers. While answering questions with research is the main focus of Elicit, there are also other research tasks that help with brainstorming, summarization, and text classification.
- ExperAI The tool enables users to find experts in various fields, such as writing, engineering, psychology, and medicine, among others. Users can connect with experts to start a random chat or select a popular expert from the list.
- Explainpaper Characterizing itself as "a better way to read papers" Explainpaper allows users to upload a paper, highlight confusing text, and Explalnpaper will provide an explanation.
- Inciteful Inciteful consists of two different tools: Paper Discovery builds a network of papers from citations, uses network analysis algorithms to analyze the network, and gives you the information you need to quickly get up to speed on that topic. Literature Connector is intended for interdisciplinary scholars trying to bridge two domains, and allows you to enter two papers to give you an interactive visualization showing you how they are connected by the literature.
- Iris.ai A comprehensive platform for research processing: smart search and a wide range of smart filters, reading list analysis, auto-generated summaries, autonomous extraction and systematizing of data.
- Keenious Keenious analyzes articles that you supply and searches for similar articles to provide you with related research.
- Laser.ai Designed to reduce literature reviews and systematic reviews time by half.
- Lateral Lateral allows you to import papers relevant to your topic and then finds similar ones that match those.
- Litmaps Litmaps uses citation searching to reveal new papers on a topic and then creates a visual map of the literature to illustrate how the studies relate to one another.
- OpenRead OpenRead features a Paper Q&A tool that allows users to quickly answer any questions they have about a paper, a Paper Espresso feature that helps to generate literature reviews quickly, an AI-powered research tool that accelerates the reading process, a low code paper editor, and a powerful notes system with backlinks and outgoing links for increased efficiency.
- Paper Brain Search for complex academic white papers. This tool will then help simplify the papers by allowing you to ask questions in natural language about the papers.
- Paper Digest Paper Digest is an AI tool that distills a research paper into a three-minute read, allowing readers to quickly grasp the core ideas of a paper.
- Paper Wizard A tool for writing papers that includes source embedding and citations.
- Prophy Find, follow, share, and collect the most relevant articles for you, find referees, construct editorial boards or panels and create peer performance comparisons across disciplines and career stages.
- R Discovery R Discovery provides readers with the top 3 papers to read every day without having to search for them day-in day-out. It presents the key highlights of the papers upfront for you so that you only spend time reading full texts where it’s absolutely worth it.
- Reduct For qualitative researchers, Reduct is a collaborative transcript-based video and audio platform for reviewing, searching, highlighting, and editing content of people talking, at scale.
- Research Rabbit ResearchRabbit is an innovative “citation-based literature mapping tool" that allows you to supply a seed paper and find more papers relevant to the topic of interest.
- ScholarAI ScholarAI is a ChatGPT Plugin that helps connect users to peer-reviewed open access research articles.
- Scholarcy Scholarcy gives you more than just an abstract. It condenses papers into referenced summaries, highlights key claims and statistics and extracts tables and images. It helps you to filter out the noise, identify the most relevant papers faster, and easily jump between the sections in an article that are most important to your work.
- Scispace This AI program decodes research papers to help you read and understand scientific literature faster. Highlight confusing text, math, and tables to get a simple explanation. Ask follow-up questions and get instant answers. SciSpace Literature Review is also worth trying: https://typeset.io/search
- Scispace Copilot This AI tool summarizes scientific papers to reduce time spent reading.
- scite Discover supporting and contrasting evidence for papers. Upload a PDF to check if the references have been supported, contrasted, retracted, received an expression of concern, etc.
- Semantic Scholar An academic search engine that utilizes artificial intelligence methods to provide highly relevant results and novel tools to filter them with ease.
- Snowball Snowball allows users to quickly find and filter through papers using the snowballing method. Start with a core collection of papers, and find more by going through their citations and references.
- Zeta Alpha Use state-of-the-art Neural Search and Generative AI to improve how you and your team discover, organize and share internal knowledge.
- AI-Writer This subscription-based tool boasts the ability to generate article drafts just from a headline, cite verifiable sources, and draw upon up to date content.
- Humata Summarize lengthy papers and turn complex technical papers into simply explained summaries. Automatically create new writing based on your file.
- Jenni AI This research and writing tool automatically completes sentences, creates tone and style, cites in multiple styles, paraphrases and rewrites.
- PaperPal This tool offers real-time, subject specific language suggestions to help you write better and faster.
- QuillBot This tool allows you to paraphrase text, check grammar, and get explanations to learn from mistakes and improve your writing.
- Sudowrite According to their website, Sudowrite is the non-judgmental,always-there-to-read-one-more-draft, never-runs-out-of-ideas-even-at-3am, AI writing partner you always wanted.
- Wordtune Spices Unlike ChatGPT, Wordtune Spices can cite its sources and utilize the most recent data from the internet.
- Writefull Automated paraphraser, abstract generator, title generator and copyeditor that has been trained on millions of scholarly journal articles.
- Writesonic An article writing tool that features paraphrasing, text expansion, and single-click summarization.
- Grantable Upload existing grant proposals and writing to train the AI. Input a question or prompt from an RFP. Let Grantable AI draft answers for you to approve using your previous answers.
- Granted ai Granted's specialized AI is trained on successful proposals that have generated billions of dollars. Instantly generate high-quality drafts for a wide range of grant proposals, appeals, letters of support, articles, and more, just by entering simple information about your project.
- Fathom Fathom records, transcribes, highlights, and summarizes your meetings so you can focus on the conversation
- Fireflies.ai Fireflies.ai helps your research team transcribe, summarize, search, and analyze voice conversations
- Machine Translation This tool is designed to analyze, compare, and recommend the best machine translation for any given text and language pair. It relies on the formidable abilities of GPT-4 to determine the strengths and weaknesses of each MT engine translation output, which in turn provides a tailored translation experience for each user.
- Otter.ai This AI meeting assistant records audio, writes notes, automatically captures slides, and generates summaries.
- Summarize.tech Get a summary of any long YouTube video, like a lecture, live event or a government meeting. Powered by ChatGPT.
- Tome Type a prompt into the command bar and let Tome generate a presentation, outline, or story for you—complete with text and images.
- Whisper AI Whisper is a free speech-to-text tool that allows you to transcribe audio into text.
- AlphaCode AlphaCode uses transformer-based language models to generate code at an unprecedented scale, and then smartly filters to a small set of promising programs.
- ChatGPT Code Interpreter Code interpreter allows ChatGPT subscribers to analyze data, create charts, solve math problems and edit files, among other uses. It also supports uploading and downloading files, which was not possible in ChatGPT before. Access limited to ChatGPT subscribers.
- Futurepedia AI Tools Directory This site contains a collection of AI tools and claims to be "the largest AI tool directory, updated daily."
- Future Tools This site collects and organizes AI tools into categories that you can select to see what tools are available.
- GitHub Copilot GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor.
- Mainstay This AI system features automated two-way chatbot messaging that sends students timely reminders of course requirements, customized feedback on progress, weekly digests with due dates, encouragement and support during exam time and quizzes offering practice exams.
- Quivr Quivr is a cloud-based open source platform that allows you to upload and store papers and pose queries like "what are the gaps in this paper?"
- Sentient Syllabus A newsletter addressing issues related to the growth of artificial intelligence in the academy.
- Teach Anything This tool allows you to write your question, select language, select difficulty and receive an answer based on your selections.
- There's An AI For That This is an AI aggregator site that features thousands of AI tools and a searchable index.
- TutorAI Type a topic in the search box and it will create a course for you.
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Artificial Intelligence and Large Language Models
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This is Susan Myers. I am not an expert on AI but am naturally interested and curious about technology in all forms. I have used Bard for shopping, helping me generate emails (when I was struggling for tone), giving me ideas for a structure for a LibGuide, and more. This topic guide is intended to be a starting place for thought and discussion. I am open for collaborators both on this guide and in using AI in academia. Feel free to email me or stop by my office.
- What is AI (Artificial Intelligence)
Artificial intelligence (AI) refers to both the areas of computer science and engineering that aim to build intelligent computers as well as the human-like intelligence, judgment, learning, and awareness displayed by machines.
- Algorithm: In computing, an algorithm is a precise list of operations that a Turing machine could do. For the purpose of computing, algorithms are written in pseudocode, flow charts, or programming languages. https://simple.m.wikipedia.org/wiki/Algorithm
- Artificial Intelligence: T he theory and development of computer systems able to perform tasks that usually require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. https://www.lexico.com/en/definition/artificial%20intelligence
- Deep Learning: In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technology is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). However, its capabilities are different. While basic machine learning models do become progressively better at whatever their function is, they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine on its own if a prediction is accurate or not through its own neural network. https://www.zendesk.com/blog/machine-learning-and-deep-learning/
- Imitation Learning : Generally, imitation learning is useful when it is easier for an expert to demonstrate the desired behavior rather than to specify a reward function that would generate the same behavior or to directly learn the policy. The main component of IL is the environment, which is essentially a Markov Decision Process (MDP). https://smartlabai.medium.com/a-brief-overview-of-imitation-learning-8a8a75c44a9c
- Machine Learning: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it... learn for themselves. https://expertsystem.com/machine-learning-definition/
- Neural Network in AI: A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy. https://aws.amazon.com/what-is/neural-network/
- Reinforcement Learning: In this kind of machine learning, AI agents are attempting to find the optimal way to accomplish a particular goal, or improve performance on a specific task. As the agent takes action that goes toward the goal, it receives a reward. The overall aim: predict the best next step to take to earn the biggest final reward. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/
- Supervised and Unsupervised Learning: In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/
ChatGPT is a language model developed by OpenAI that uses the GPT-3.5 architecture. It is designed to generate human-like responses in natural language conversations. The model is trained on a vast amount of text data from the internet, allowing it to learn patterns, grammar, and contextual information. When given a prompt or a message, ChatGPT analyzes the input, generates possible completions based on its training, and ranks them to select the most appropriate response. It relies on the context provided by previous messages in the conversation to maintain coherence and relevance. The responses are generated by predicting the next word or phrase, taking into account the probability distribution of different options. While ChatGPT is capable of generating impressive and coherent responses, it may sometimes produce incorrect or nonsensical answers, so caution should be exercised when relying on its outputs.
DALL-E 2 is an advanced version of the original DALL-E model developed by OpenAI. It is a cutting-edge artificial intelligence model trained to generate unique and creative images from textual descriptions. DALL-E 2 combines the power of deep learning techniques and generative adversarial networks (GANs) to understand and interpret natural language prompts, enabling it to create highly detailed and realistic images based on those descriptions. This model expands on the capabilities of its predecessor, allowing for more complex and intricate image generation. It has been trained on a vast dataset of images and text, enabling it to generate novel and imaginative visual concepts. DALL-E 2 has the potential to revolutionize the field of computer-generated imagery, providing a powerful tool for artists, designers, and creators.
Bard is a large language model (LLM) chatbot developed by Google AI. It is trained on a massive dataset of text and code and can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Bard is still under development, but it has learned to perform many kinds of tasks
Bing and Microsoft have teamed up to update the original Bing search engine to include OpenAI's language model and answer questions and requests with a conversational style. Microsoft Bing claims to be more powerful than ChatGPT and is designed specifically for Internet searching.
Humata.ai innovative approach to software development, is an invaluable resource for those seeking to simplify their research process and expedite the journey to discovery. This AI-driven Q&A platform represents a bridge between complex data and actionable insights, facilitating the transition from learning to implementing.
I HIGHLY recommend the blog "One Useful Thing" by Ethan Mollick. I subscribed to it and receive his new posts in my email. He focuses on AI and its impact on work and education.
Published weekly, the Chronicle of Higher Education is a top source for news, information, and jobs for college and university faculty members and administrators.
Provides cross-searchable access to over three million entries from 273 titles and 57 publishers. Features full-text content from hundreds of reference books covering every major subject.
Provides researchers with the latest information and current trends in high technology. This database offers full text for nearly 300 publications and indexing and abstracts for nearly 450 publications.
includes more than 860 full-text newspapers, providing more than 35 million full-text articles. In addition, the database features more than 857,000 television and radio news transcripts.
A comprehensive research database that provides easy access to a multitude of full text science-oriented content. This database contains full text for nearly 640 science encyclopedias, reference books, periodicals, etc. Topics covered include: biology, chemistry, earth & space science, environmental science, health & medicine, history of science, life science, physics, science & society, science as inquiry, scientists, technology and wildlife.
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177 Great Artificial Intelligence Research Paper Topics to Use
In this top-notch post, we will look at the definition of artificial intelligence, its applications, and writing tips on how to come up with AI topics. Finally, we shall lock at top artificial intelligence research topics for your inspiration.
What Is Artificial Intelligence?
It refers to intelligence as demonstrated by machines, unlike that which animals and humans display. The latter involves emotionality and consciousness. The field of AI has gained proliferation in recent days, with many scientists investing their time and effort in research.
How To Develop Topics in Artificial Intelligence
Developing AI topics is a critical thinking process that also incorporates a lot of creativity. Due to the ever-dynamic nature of the discipline, most students find it hard to develop impressive topics in artificial intelligence. However, here are some general rules to get you started:
Read widely on the subject of artificial intelligence Have an interest in news and other current updates about AI Consult your supervisor
Once you are ready with these steps, nothing is holding you from developing top-rated topics in artificial intelligence. Now let’s look at what the pros have in store for you.
Artificial Intelligence Research Paper Topics
- The role of artificial intelligence in evolving the workforce
- Are there tasks that require unique human abilities apart from machines?
- The transformative economic impact of artificial intelligence
- Managing a global autonomous arms race in the face of AI
- The legal and ethical boundaries of artificial intelligence
- Is the destructive role of AI more than its constructive role in society?
- How to build AI algorithms to achieve the far-reaching goals of humans
- How privacy gets compromised with the everyday collection of data
- How businesses and governments can suffer at the hands of AI
- Is it possible for AI to devolve into social oppression?
- Augmentation of the work humans do through artificial intelligence
- The role of AI in monitoring and diagnosing capabilities
Artificial Intelligence Topics For Presentation
- How AI helps to uncover criminal activity and solve serial crimes
- The place of facial recognition technologies in security systems
- How to use AI without crossing an individual’s privacy
- What are the disadvantages of using a computer-controlled robot in performing tasks?
- How to develop systems endowed with intellectual processes
- The challenge of programming computers to perform complex tasks
- Discuss some of the mathematical theorems for artificial intelligence systems
- The role of computer processing speed and memory capacity in AI
- Can computer machines achieve the performance levels of human experts?
- Discuss the application of artificial intelligence in handwriting recognition
- A case study of the key people involved in developing AI systems
- Computational aesthetics when developing artificial intelligence systems
Topics in AI For Tip-Top Grades
- Describe the necessities for artificial programming language
- The impact of American companies possessing about 2/3 of investments in AI
- The relationship between human neural networks and A.I
- The role of psychologists in developing human intelligence
- How to apply past experiences to analogous new situations
- How machine learning helps in achieving artificial intelligence
- The role of discernment and human intelligence in developing AI systems
- Discuss the various methods and goals in artificial intelligence
- What is the relationship between applied AI, strong AI, and cognitive simulation
- Discuss the implications of the first AI programs
- Logical reasoning and problem-solving in artificial intelligence
- Challenges involved in controlled learning environments
AI Research Topics For High School Students
- How quantum computing is affecting artificial intelligence
- The role of the Internet of Things in advancing artificial intelligence
- Using Artificial intelligence to enable machines to perform programming tasks
- Why do machines learn automatically without human hand holding
- Implementing decisions based on data processing in the human mind
- Describe the web-like structure of artificial neural networks
- Machine learning algorithms for optimal functions through trial and error
- A case study of Google’s AlphaGo computer program
- How robots solve problems in an intelligent manner
- Evaluate the significant role of M.I.T.’s artificial intelligence lab
- A case study of Robonaut developed by NASA to work with astronauts in space
- Discuss natural language processing where machines analyze language and speech
Argument Debate Topics on AI
- How chatbots use ML and N.L.P. to interact with the users
- How do computers use and understand images?
- The impact of genetic engineering on the life of man
- Why are micro-chips not recommended in human body systems?
- Can humans work alongside robots in a workplace system?
- Have computers contributed to the intrusion of privacy for many?
- Why artificial intelligence systems should not be made accessible to children
- How artificial intelligence systems are contributing to healthcare problems
- Does artificial intelligence alleviate human problems or add to them?
- Why governments should put more stringent measures for AI inventions
- How artificial intelligence is affecting the character traits of children born
- Is virtual reality taking people out of the real-world situation?
Quality AI Topics For Research Paper
- The use of recommender systems in choosing movies and series
- Collaborative filtering in designing systems
- How do developers arrive at a content-based recommendation
- Creation of systems that can emulate human tasks
- How IoT devices generate a lot of data
- Artificial intelligence algorithms convert data to useful, actionable results.
- How AI is progressing rapidly with the 5G technology
- How to develop robots with human-like characteristics
- Developing Google search algorithms
- The role of artificial intelligence in developing autonomous weapons
- Discuss the long-term goal of artificial intelligence
- Will artificial intelligence outperform humans at every cognitive task?
Computer Science AI Topics
- Computational intelligence magazine in computer science
- Swarm and evolutionary computation procedures for college students
- Discuss computational transactions on intelligent transportation systems
- The structure and function of knowledge-based systems
- A review of the artificial intelligence systems in developing systems
- Conduct a review of the expert systems with applications
- Critique the various foundations and trends in information retrieval
- The role of specialized systems in transactions on knowledge and data engineering
- An analysis of a journal on ambient intelligence and humanized computing
- Discuss the various computer transactions on cognitive communications and networking
- What is the role of artificial intelligence in medicine?
- Computer engineering applications of artificial intelligence
AI Ethics Topics
- How the automation of jobs is going to make many jobless
- Discuss inequality challenges in distributing wealth created by machines
- The impact of machines on human behavior and interactions
- How artificial intelligence is going to affect how we act accordingly
- The process of eliminating bias in Artificial intelligence: A case of racist robots
- Measures that can keep artificial intelligence safe from adversaries
- Protecting artificial intelligence discoveries from unintended consequences
- How a man can stay in control despite the complex, intelligent systems
- Robot rights: A case of how man is mistreating and misusing robots
- The balance between mitigating suffering and interfering with set ethics
- The role of artificial intelligence in negative outcomes: Is it worth it?
- How to ethically use artificial intelligence for bettering lives
Advanced AI Topics
- Discuss how long it will take until machines greatly supersede human intelligence
- Is it possible to achieve superhuman artificial intelligence in this century?
- The impact of techno-skeptic prediction on the performance of A.I
- The role of quarks and electrons in the human brain
- The impact of artificial intelligence safety research institutes
- Will robots be disastrous for humanity shortly?
- Robots: A concern about consciousness and evil
- Discuss whether a self-driving car has a subjective experience or not
- Should humans worry about machines turning evil in the end?
- Discuss how machines exhibit goal-oriented behavior in their functions
- Should man continue to develop lethal autonomous weapons?
- What is the implication of machine-produced wealth?
AI Essay Topics Technology
- Discuss the implication of the fourth technological revelation in cloud computing
- Big database technologies used in sensors
- The combination of technologies typical of the technological revolution
- Key determinants of the civilization process of industry 4.0
- Discuss some of the concepts of technological management
- Evaluate the creation of internet-based companies in the U.S.
- The most dominant scientific research in the field of artificial intelligence
- Discuss the application of artificial intelligence in the literature
- How enterprises use artificial intelligence in blockchain business operations
- Discuss the various immersive experiences as a result of digital AI
- Elaborate on various enterprise architects and technology innovations
- Mega-trends that are future impacts on business operations
Interesting Topics in AI
- The role of the industrial revolution of the 18 th century in A.I
- The electricity era of the late 19 th century and its contribution to the development of robots
- How the widespread use of the internet contributes to the AI revolution
- The short-term economic crisis as a result of artificial intelligence business technologies
- Designing and creating artificial intelligence production processes
- Analyzing large collections of information for technological solutions
- How biotechnology is transforming the field of agriculture
- Innovative business projects that work using artificial intelligence systems
- Process and marketing innovations in the 21 st century
- Medical intelligence in the era of smart cities
- Advanced data processing technologies in developed nations
- Discuss the development of stelliform technologies
Good Research Topics For AI
- Development of new technological solutions in I.T
- Innovative organizational solutions that develop machine learning
- How to develop branches of a knowledge-based economy
- Discuss the implications of advanced computerized neural network systems
- How to solve complex problems with the help of algorithms
- Why artificial intelligence systems are predominating over their creator
- How to determine artificial emotional intelligence
- Discuss the negative and positive aspects of technological advancement
- How internet technology companies like Facebook are managing large social media portals
- The application of analytical business intelligence systems
- How artificial intelligence improves business management systems
- Strategic and ongoing management of artificial intelligence systems
Graduate AI NLP Research Topics
- Morphological segmentation in artificial intelligence
- Sentiment analysis and breaking machine language
- Discuss input utterance for language interpretation
- Festival speech synthesis system for natural language processing
- Discuss the role of the Google language translator
- Evaluate the various analysis methodologies in N.L.P.
- Native language identification procedure for deep analytics
- Modular audio recognition framework
- Deep linguistic processing techniques
- Fact recognition and extraction techniques
- Dialogue and text-based applications
- Speaker verification and identification systems
Controversial Topics in AI
- Ethical implication of AI in movies: A case study of The Terminator
- Will machines take over the world and enslave humanity?
- Does human intelligence paint a dark future for humanity?
- Ethical and practical issues of artificial intelligence
- The impact of mimicking human cognitive functions
- Why the integration of AI technologies into society should be limited
- Should robots get paid hourly?
- What if AI is a mistake?
- Why did Microsoft shut down chatbots immediately?
- Should there be AI systems for killing?
- Should machines be created to do what they want?
- Is the computerized gun ethical?
Hot AI Topics
- Why predator drones should not exist
- Do the U.S. laws restrict meaningful innovations in AI
- Why did the campaign to stop killer robots fail in the end?
- Fully autonomous weapons and human safety
- How to deal with rogues artificial intelligence systems in the United States
- Is it okay to have a monopoly and control over artificial intelligence innovations?
- Should robots have human rights or citizenship?
- Biases when detecting people’s gender using Artificial intelligence
- Considerations for the adoption of a particular artificial intelligence technology
Are you a university student seeking research paper writing services or dissertation proposal help ? We offer custom help for college students in any field of artificial intelligence.
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Best AI writers of 2023
Use the best AI writers to create written content quickly
- Best AI writer overall
Best choice for marketers
Best for wordpress users, best for long-form writing.
- Best free version
- Best for short-form
Best for sourcing
- Industry rankings
1. Best AI writer overall 2. Best for marketers 3. Best for WordPress users 4. Best for long-form writing 5. Best free option 6. Best for short-form writing 7. Best for sourcing 8. TechRadar's AI writer rankings
The word "AI" has been named the word of the year. ChatGPT has made waves since its introduction in late 2022. With every new iteration of this powerful tool, users have found innovative ways to use it to simplify and speed up their work.
Now, there are many AI writing assistants on the market, competing with ChatGPT to become the king of AI-powered writing tools. These new tools aim to simplify the writing process by generating long-form content, researching keywords, creating images from text, and more. Many bloggers are using these tools to improve their content and save time.
However, there are some drawbacks to using AI writers. The content generated may require additional editing to ensure it's polished and accurate. AI-generated content may also lack the unique voice and style a human writer can provide. Despite this, the benefits of using AI writers, such as cost and time savings, often outweigh these minor drawbacks.
In conclusion, AI writers are an excellent solution for creating high-quality content without spending countless hours or breaking the bank. With numerous writing tools available today, content creation can be easily sped up and simplified. If you're interested in trying one of these tools, we've got you covered with our list of the best AI writers of the year. Check it out!
The best AI writers in 2023
Why you can trust TechRadar We spend hours testing every product or service we review, so you can be sure you’re buying the best. Find out more about how we test.
See how our top picks compare in the following analysis and reviews as we discuss reasons to subscribe, reasons to avoid, our test results, and what we liked most about each cloud storage platform.
The best AI writer overall
Our expert review:
Reasons to buy, reasons to avoid.
✔️ You also need a grammar editor: Getting help from an AI writer is even better with one that also helps you with grammar, like Grammarly.
✔️ Need to use it across multiple apps: With Grammarly installed on your computer, you instantly gain access to it across your favorite apps like Microsoft Word and other word processing packages.
✔️ Want something that's easy to use — once you learn how to do it: Once you understand how GrammarlyGO works, it's going to make your life easier and assist you in making your text better.
❌ You're on a strict budget: No doubt, Grammarly itself is expensive, especially if you only want to pay for it on a month-to-month basis.
❌ You don't need a grammar tool: Maybe this is overkill, depending on your situation.
❌ You need social networking-specific tools: GrammarlyGO doesn't offer these types of tools, at least for now.
Even in beta, GrammarlyGO is an excellent addition to an already powerful Al-based writing tool. You can use it to become a much better writer in just a few steps.
Check out our in-depth GrammarlyGo review for a closer look at the service, and see why it's our top AI writer pick for 2023.
Grammarly has been a reliable companion for writers, helping them polish their craft by providing suggestions that make their writing clearer, more engaging, or more effective. And now, they've taken it up a notch by introducing GrammarlyGO, a feature-packed add-on that comes bundled with the rest of the software.
To get the most out of GrammarlyGO, you must understand how Grammarly works, as the two are closely intertwined. When using GrammarlyGO, it's essential to remember the number of prompts available to you, which varies depending on the plan you choose. Whenever you ask Grammarly to improve your text, one prompt is used, regardless of whether you use the suggestion. Grammarly Free users get access to 100 prompts per month, while Grammarly Premium subscribers receive 1,000 prompts per month. For Grammarly Business users, the number goes up to 1,000 prompts per user every month.
GoogleGO AI features are classified into five categories: ideate, compose, reply, rewrite, and personalize. The ideate feature helps you generate article ideas that are exciting and thought-provoking. With Grammarly's assistance, you can develop topics like "Five ways to motivate employees" or "Name five great topics about fall" that can capture the reader's attention and spark their interest.
The compose feature is perfect when you want Grammarly to help you write something from scratch. For instance, you could ask Grammarly to help you write an announcement about your engagement or a cover letter for a new job. The more information you provide, the better the results, and GrammarlyGO can help you create a masterpiece with its advanced suggestions and insights. Adding details like the name of your fiancé and the engagement date can make your announcement even more unique. In contrast, information about the job you're applying for can make your cover letter more effective.
If you're unsure how to respond to a message, Grammarly's reply feature can save the day. You can ask Grammarly to answer questions like "What should I say to Brent about the new job?" or "How can I congratulate Tom and Becky on their upcoming nuptials?" With Grammarly's superior writing skills, you can craft an engaging, effective, and impressive response.
With these incredible features, GrammarlyGO can help you take your writing to new heights and unleash your creativity.
Please read our full GrammarlyGo review .
✔️ You are a marketer: Anyword is the best AI writer for marketers on the planet. It helps you create content based on your company's "voice," and learns as it goes.
✔️ You enjoy trial and error: There's no "right" answer when it comes to AI text generation. Anyword makes it easier to tweak text once or unlimited times to help you get the text perfect for your needs.
✔️ You need to generate unlimited words: There are limits elsewhere, but Anyword doesn't limit how many words it will generate on a monthly basis.
❌ You want app integration: You'll need to copy and paste text from your favorite word processor to Anyword, which can get annoying for some.
❌ You want a free plan: Once you exhaust your trial, you'll need to pick a free plan to continue.
❌ You aren't a marketer: The heavy marketing focus can't be avoided.
Anyword offers a slick and easy-to-use interface. In mere moments, you'll be able to create excellent content that caters to your intended audience
Check out our in-depth Anyword review for a closer look .
Are you tired of struggling to generate creative marketing copy or unsure how to optimize your existing content? Anyword is an innovative cloud-based writing tool that can help you overcome these challenges and achieve exceptional results. With Anyword's intuitive interface and user-friendly tools, you can generate, test, and optimize your copy in ways you never thought possible.
With unlimited words in each plan, Anyword gives you plenty of space to craft compelling content that truly resonates with your audience. Its advanced AI-powered algorithms can analyze your existing marketing materials and produce multiple variations of your copy, enabling you to compare and contrast different solutions and select the best option for your brand.
One of the key features of Anyword is its Brand Voice function, which enables businesses to establish a consistent identity across all their marketing materials. Whether you're refining your tone of voice, targeting your audience, or building out your messaging bank, Anyword can help you create copy that accurately reflects your brand's personality, tone, and style.
Additionally, Anyword seamlessly integrates with various platforms, from Hubspot to LinkedIn Ads, enabling you to use copy intelligence to enhance the quality of your future content. By analyzing the performance of your existing materials, Anyword empowers you to make informed decisions about optimizing your messaging for even better results.
But that's not all - Anyword's advanced algorithms can also analyze the performance of your competitors' marketing materials, giving you insights into their copy and enabling you to create content that sets you apart from the competition.
In summary, Anyword can help you unlock your creativity and produce exceptional marketing copy that resonates with your audience. With its range of user-friendly tools, advanced AI-powered algorithms, and seamless integration with various platforms, Anyword is the perfect writing tool for businesses looking to enhance their marketing efforts.
Please read our full Anyword review .
✔️ You use WordPress heavily: Articleforge works great with WordPress; get started in just a few steps.
✔️ You want package customization: The more you're willing to pay, the more words you can generate each month. It's flexible.
✔️ You need marketing-based tools: Offers SEO optimization, content in bulk, and more.
❌ You don't want to self-edit : Articleforge may require more post-generation editing than other options, which could slow you down.
❌ You don't want to deal with duplicates: Yes, sometimes Articleforge repeats suggestions.
❌ If you don't want to spend more for a monthly subscription: It's cheaper to buy this on a yearly basis, and that might not be a commitment you're willing to make.
Articleforge utilizes deep learning and AI to improve content over time, though heavy editing and fact-checking are often necessary.
Check out our in-depth Articleforge review for more information.
Articleforge is a tool that can greatly help speed up the writing process for users. It is a valuable resource for those who need to generate content quickly but do not have the time or resources to do so themselves. However, it is important to note that it is not a replacement for an experienced writer or editor. While it can provide recommendations for titles and automate SEO and WordPress publishing, it is not designed to produce ready-to-publish content.
One of the benefits of Articleforge is that it is available in seven languages, making it ideal for international blogs or multi-language sites. Users simply need to enter a few keywords and the topic they want to cover, and the platform will generate content in under 60 seconds. This generated content will use the provided keywords and cover the desired topic.
In addition to its quick and easy content generation, Articleforge also offers various integrations, including MS Word and WordPress integrations. The platform also provides integrations with other software systems like SEO AutoPilot, CyberSEO, RankerX, SEnuke TNG, and more. These integrations are easy to use, thanks to the API key offered by the platform.
While the platform is a convenient tool for creating content, its output quality falls short of expectations. As with any automated system, fact-checking is necessary for the majority of the content offered by the platform. The platform does offer better output quality with customized input. However, extensive testing is required, which can quickly exhaust the "word meter."
Overall, Articleforge can significantly expedite the writing process and help overcome writer's block. It is advisable to test it out and find a balance between the platform and tailored input to yield superior results. While it should not be relied upon as a sole source of content, using it in conjunction with other resources can help users quickly generate high-quality content.
Read our full Articleforge AI writer review .
✔️ You want a great plagiarism checker: You want to create original content, right? This checker makes sure that's true.
✔️ You need to create content in multiple languages: Some folks are writing content in multiple languages and Jasper lets you do this.
✔️ You want access to a lot of features: The team behind this solution tends to add new features on a regular basis without raising the price. That's sweet.
❌ You don't have time to learn: Any AI writer takes time to learn. Jasper takes a little bit more time.
❌ If you don't want to spend a lot: Some folks might not feel the Creator package is enough and the next one is expensive.
❌ If you aren't willing to edit a lot: Some of the content Jasper creates is better than others. Some extra editing is key.
Jasper is a platform that is highly customizable and comes with a user-friendly interface. All the necessary tools that you need are just a click away. The platform’s content generation capabilities are hidden behind easy-to-reach templates, making it an easy-to-use tool.
Check out our review of Jasper to learn more about the AI writer.
Jasper is a powerful platform that leverages natural language processing (NLP) algorithms to analyze data from various sources across the web. With this ability, it can generate content ideas based on the relevant data you input, such as keywords, topics, and more. Jasper can help create a wide range of content formats, including blog posts, social media content, emails, and much more.
One of the most significant advantages of Jasper is that it has a tone feature that allows you to assign a corresponding tone to the copy you create if you’re targeting a specific persona. This feature is incredibly useful because it helps ensure that the content you create resonates with your target audience. Additionally, Jasper can help you improve your content through optimization recommendations, which can be accessed through numerous templates available on the platform.
If you’re short on time and need to create content quickly, Jasper is the perfect solution. It can offer content in mere seconds with just a few clicks and some input. With Jasper, you can save time and increase productivity, allowing you to focus on other essential tasks.
Another unique feature that Jasper offers is the “Boss Mode” feature, which allows you to write 5x faster. With this mode, you can give commands to Jasper, and it will do all the work for you. You can even write a complete book using this mode in just minutes. This feature is incredibly useful for writers who need to create a large volume of content in a short amount of time.
In the Boss Mode, you can also use pre-built recipes offered by the Jasper team such as “Write about (keyword),” but you also have the option of creating your own, which can be incredibly helpful if you have specific requirements for your content. By automating your writing process, you can save time and focus on other critical tasks.
Read our full Jasper review .
The best free version
✔️ You want a free plan for minor work: If you're okay with only generating 2,000 words per month, there's a free plan for that.
✔️ You need unlimited word generation: You can create unlimited words each month with all the paid plans.
✔️ You want multiple tools: New features are added often, making the product even better.
❌ You are a marketer: There are better options if you primarily need to write marketing copy.
❌ You aren't willing to learn: Here's another option that is a little bit harder to learn, at least initially.
❌ You need app integration: Expect to stay on the CopyAI website to get your work done, which requires copy and paste.
CopyAI helps writers create high-quality copy with the power of AI, saving time and producing better content that resonates with your audience.
Check out our in-depth CopyAI review to see whether it's the tool for you.
CopyAI has been making waves in the world of AI writing tools, becoming a favorite among users who want to create high-quality content that can help them stand out in today's crowded digital space. The tool offers a wide range of options that allow users to get started and take their writing to the next level, from exploring various writing templates, settings, and features on the user dashboard to creating a compelling copy in minutes.
The user dashboard serves as the creative command center for CopyAI users, providing a user-friendly and intuitive interface that makes it easy to navigate through different features. From here, you can quickly access various options that can help you create content that resonates with your audience, whether you need to write a blog post about travel or an email to a potential client.
The chat feature is the default option that acts as a blank canvas to help generate inspiration. The brainstorm feature allows you to create copy such as "ten catchy Twitter headlines on holiday shopping," "the best Facebook headlines for marketing professionals," and more. If you're struggling to come up with a topic, don’t worry. CopyAI’s chat function provides prompt templates to give you a head start. These templates cover various topics, including content creation, SEO, email marketing, social media, PR and communication, sales, and strategy.
Moreover, you can create custom templates that cater to your specific needs. Each template in the collection provides various options, so whether you need a headline generator, a step-by-step guide, or a product description, CopyAI has you covered. You can even use the "rewrite content" option to enhance your written content, making it more engaging and effective.
To fully personalize your experience with CopyAI, you should create one or more brand voices. This process involves providing text that accurately describes you or your company's unique voice. This text should be between 50 and 500 words and can come from various sources such as blog articles, social media posts, website copy, marketing emails, and more. This allows CopyAI to tailor its AI-powered tools to better suit your brand's needs, making it easier for you to create content that resonates with your target audience and helps you achieve your goals.
In summary, CopyAI is a powerful AI writing tool that offers a wide range of features and options to help you create high-quality content. From the user dashboard to the chat and brainstorming features, CopyAI provides a user-friendly and intuitive interface that makes it easy to create compelling and engaging content. With custom templates and brand voices, you can personalize your experience with CopyAI and create content that resonates with your target audience, helping you stand out in today's crowded digital space.
Read our full CopyAI review .
The best for short-form
✔️ You aren't tech savvy: It's one of the easiest-to-use solutions on the market.
✔️ You need lots of languages: Writesonic supports 25 languages and counting.
✔️ You're a freelancer: There's a package just for you.
❌ You want to pay a lower price: Some have criticized Writesonic for being too expensive. That's true with the Smart Team options
❌ You need more third-party integrations: It doesn't really place nice with other software tools, which could add some time to your work.
❌ You don't like tackling a learning curve : Like others on this list, there's a slighter higher learning curve with this one
Writesonic is an expansive AI writing platform with an intuitive interface and versatile templates for all content creation scenarios.
Check out our in-depth Writesonic review to see if this is the AI writing tool for you.
Writesonic is an innovative content creation platform that provides users with various features and tools to generate high-quality, engaging content. One of the most impressive features of Writesonic is its versatility - it supports over 25 languages, including English, French, Italian, German, Japanese, Chinese, and more. This is a significant advantage for businesses that operate in multiple regions and need to produce content in different languages.
Another notable feature of Writesonic is its tone of voice customization tool. Users can choose from various tones, including "Excited," "Creative," and more. This feature adds a unique twist to the content, tailoring it to the user's needs. However, it's essential to note that the tone of voice feature may require additional editing to ensure the content is clear and coherent.
Regarding website copywriting, Writesonic provides users with an impressive range of tools. It can create landing pages, SEO meta descriptions, and feature-rich headers and subheaders. Additionally, it allows users to create social media ads on Facebook, LinkedIn, and Google . Content creators can also benefit from Writesonic's blog writing, point expansion, and text analysis tools, which can rephrase the content and improve its readability.
The platform also offers e-commerce copy creation and popular copywriting formulas, such as the "AIDA" and "Pain-Agitate-Solution" formulas. These formulas are tried and tested approaches to writing compelling, persuasive content that resonates with readers.
However, it's essential to note that the content generated by Writesonic may require significant editing from the user. While the platform does an excellent job of generating content, it's essential to ensure it's clear, coherent, and tailored to the user's needs. If you're looking for a completely hands-off approach to content creation, Writesonic may not be the best option. However, for businesses that need to generate high-quality, engaging content quickly, Writesonic is an excellent choice.
Please read our full Writesonic review .
✔️ You want a cheap package: You can get an AI writer for as little as $19/month.
✔️ You need clear sourcing: No better solution is available for providing sources for all text provided.
✔️ You want an easy solution: A very low learning curve exists.
❌ You want app integration: There's seamless integration with WordPress, but that's about it.
❌ You need marketing-specific tools: Sorry, this one doesn't have it.
❌ You want unlimited word generation: If you need to generate more than 120 articles per month, this is a very expensive solution.
AI-Writer is a unique word-generating tool that simplifies text creation and editing through an intuitive user interface. However, it may not be suitable for everyone.
Check out our in-depth AI-Writer review to see if it's the solution for you.
AI-Writer is a word generator that is easily accessible and is popular among freelancers and bloggers. It may not be as extensive as Anyword or CopyAI, which are primarily aimed at marketing and sales, but it serves its intended audience well.
One of the most significant advantages of AI-Writer is its unparalleled sourcing capabilities. It is the only AI content generator that cites sources for "everything it writes." Additionally, it updates its sources frequently, ensuring that any article generated by it sources the latest information on the subject. This is a significant advantage, particularly for those who are writing about current events or trending topics. With AI-Writer, writers can create content that is well-researched and accurate, without having to spend hours scouring the internet for sources.
Another significant advantage of AI-Writer is that it recognizes that not all types of content require sourcing. For instance, op-eds or personal essays don't usually require sources. AI-Writer recognizes this and hides sources and links from the main results page, making it easier for writers to focus on their content and not worry about sources.
One of the drawbacks of using AI-Writer is that its extensive sourcing process can cause a minor delay in generating results compared to other AI writing tools. Although this may not be a significant issue, the noticeable delay should be mentioned. For instance, alternative tools like GrammarlyGO offer results without hesitation. However, it is worth noting that the issue with AI-Writer is primarily due to its thorough sourcing process.
In conclusion, AI-Writer is an excellent tool for freelancers and bloggers who want to create high-quality content. It excels in sourcing capabilities, making it a go-to tool for writers who need well-researched and accurate content. Its ability to recognize when sourcing is not required is also an added advantage. While it may not be the fastest tool on the market, its thorough sourcing process is worth the wait.
Read our full AI-Writer review .
TechRadar's AI writer rankings
Numerous AI writing solutions are already available in the market, and we can expect more to arrive in the future. Have a look at our rankings of popular services below, and also check out the honorable mentions that currently can’t compete with the top services available.
What is an AI writer?
An AI writer is a revolutionary tool, capable of creating text and content without human help; it utilizes algorithms and machine learning to generate various AI content. From data-driven, high-value pieces to conversion-focused content perfect for marketing campaigns, AI writers can easily create just about any content.
As AI writing assistants gain exposure to various forms of real-world information, they gain proficiency in generating natural-sounding output. With their data coming from human sources, the output created also has a human-like quality. Much like how humans rely on existing content to craft something new, AI content tools scour the web for relevant data to fulfill the user's instructions, thus creating original content.
This, in a nutshell, explains what AI writing is and how it functions.
How to choose the best AI writer for you?
Let’s get this out of the way. Whichever platform you choose, you will have to do some editing, if you want to create useful texts.
Here are some of the factors you should consider when choosing the right tool for you:
1. Ease of use
Tools that don’t require technical knowledge or prior experience should be on the top of your list. Investment in good UI means that other aspects of the tool are also likely to be of a higher quality.
While this may not be popular with everyone, the price should play a major role in deciding which tool to go for. Some are simply overpriced while not offering much more than their lower-priced competition. Pay attention to the amount of content each price plan offers.
3. High-quality output
Despite the fact that, in the end, you will be editing the texts and images the tool creates, having a tool that creates high-quality content will mean less time spent on fixing mistakes and editing.
If you’re running a blog or business and have been doing so for some time, you probably have your set of tools that you use for writing content. Making sure that the new AI platform syncs well with your existing toolbox can be essential for how long and how well you utilize the AI tool.
The list above is not exhaustive, but does offer a great starting point in your quest to find the best AI writing tool for your needs.
The best AI writers: How we test
During our assessment, we’ve evaluated various aspects such as the number of writing templates, categories, recipes, number of languages supported, grammar checkers, etc. Our goal was to create an extensive list of AI writing assistants that offer much more than simple rewording features.
We tested the overall capabilities of the AI software, the tool's interface and ease of use, monthly article limits, SEO optimization features, and pricing, among other aspects.
In addition, we gave each platform a test article to write for us (a simple topic) and checked its sentence structure and content relevance.
Read more on how we test, rate, and review products on TechRadar .
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Bryan M. Wolfe is a staff writer at TechRadar, iMore, and wherever Future can use him. Though his passion is Apple-based products, he doesn't have a problem using Windows and Android. Bryan's a single father of a 15-year-old daughter and a puppy, Isabelle. Thanks for reading!
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Artificial Intelligence Research Paper
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II. History of Artificial Intelligence
III. Knowledge Representation
a. Symbolic Artificial Intelligence (Top-Down Approach)
i. Predicate Logic or First-Order Logic
ii. Rule-Based Systems (Production Systems)
iii. Fuzzy Logic
iv. Semantic Networks, Frames, and Scripts
v. Bayesian Networks
b. Subsymbolic Artificial Intelligence (Bottom-Up Approach)
i. Neural Networks
ii. Evolutionary Computation
iii. Particle Swarm Optimization
iv. Behavior-Based Artificial Intelligence
IV. Artificial Intelligence in Psychology
a. SOAR (State Operator and Result)
b. PDP (Parallel Distributed Processing)
c. ACT-R (Adaptive Control of Thought-Rational)
V. Criticisms of Artificial Intelligence
a. Weak AI Versus Strong AI
i. Criticisms of the Strong AI View
ii. Machines Cannot Have Understanding—The Chinese Room Argument
Artificial intelligence (AI) comprises a vast interdisciplinary field, which has benefited from its beginning from disciplines such as computer science, psychology, philosophy, neuroscience, mathematics, engineering, linguistics, economics, education, biology, control theory, and cybernetics. Although the goals of AI are as wide as the field is interdisciplinary, AI’s main goal is the design and construction of automated systems (computer programs and machines) that perform tasks considered to require intelligent behavior (i.e., tasks that require adaptation to complex and changing situations).
The role of psychology in artificial intelligence is twofold. On the one hand, psychologists can help in the development and construction of AI systems because knowledge of cognitive and reasoning processes such as perception, language acquisition, and social interaction is crucial to AI. AI has much to learn from humans because we are the best model of intelligent behavior we know, and because many AI machines will have to interact with us. On the other hand, psychologists could benefit from the AI techniques and tools to develop further their own discipline using AI tools such as modeling and simulation of theories, expert systems in diagnosis and organization, and interactive techniques in education, just to mention a few.
History of Artificial Intelligence
It seems that the desire to build machines that behave intelligently has always been a part of human history. For example, around 2500 BCE in Egypt, citizens and peregrines turned to oracles (statues with priests hidden inside) for advice. Homer’s Iliad, a remarkable literature work from ancient Greece, narrates how the Greek god Hephaestos creates Talos, a man of bronze whose duty is to patrol and protect the beaches of Crete. The idea of building humans and machines with intelligence transferred from mythology into modern literature. For example, Karel Kapek’s play R.U.R. (Rossum’s Universal Robots), which opened in London in 1923, coined the word “robot.” Shortly after, the very popular science fiction movie Metropolis, by Fritz Lang (1927), had a robot character (Maria) that played a decisive role in the plot of the movie. And, in the 1940s, Isaac Asimov started publishing his famous collection of books about robotics.
However, people not only wrote about the possibility of creating intelligent machines; they actually built them. For example, the ancient Greeks were fascinated with automata of all kinds, which they used mostly in theater productions and religious ceremonies for amusement. In the 4th century BCE, the Greek mathematician Archytas of Tarentum built a mechanical bird (a wooden pigeon) that, when propelled by a jet of steam or compressed air, could flap its wings. Supposedly, in one test, it flew a distance of 200 meters (however, once it fell to the ground, it could not take off again). Toward the end of the Middle Ages, clockmakers helped build devices that tried to mimic human and animal behavior. For example, Leonardo da Vinci built a humanoid automaton (an armored knight) around the end of the 15th century for the amusement of royalty. This armored knight was apparently able to make several humanlike motions, such as sitting up and moving its arms, legs, and neck. Reportedly, da Vinci also built a mechanical lion that could walk a programmable distance. In the early 16th century, Hans Bullmann created androids that could play musical instruments for the delight of paying customers. In the 18th century, Jacques de Vaucanson created a mechanical life-size figure (The Flute Player) capable of playing a flute with a repertoire of 12 different tunes. He also created an automatic duck (The Digesting Duck) that could drink, eat, paddle in water, and digest and excrete eaten grain.
In modern scientific artificial intelligence, the first recognized work was Warren McCulloch and Walter Pitts’s 1943 article A Logical Calculus of the Ideas Immanent in Nervous Activity, which laid the foundations for the development of neural networks. McCulloch and Pitt proposed a model of artificial neurons, suggesting that any computable function could be achieved by a network of connected neurons and that all logical connectives (and, or, not, etc.) could be implemented by simple network structures. In 1948, Wiener’s popular book Cybernetics popularized the term cybernetic and defined the principle of the feedback theory. Wiener suggested that all intelligent behavior was the result of feedback mechanisms, or conditioned responses, and that it was possible to simulate these responses using a computer. One year later, Donald Hebb (1949) proposed a simple rule for modifying and updating the strength of the connections between neurons, which is now known as Hebbian learning. In 1950, Alan M. Turing published Computing Machinery and Intelligence, which was based on the idea that both machines and humans compute symbols and that this commonality should be the basis for building intelligent machines. Turing also introduced an operational strategy to test for intelligent behavior in machines based upon an imitation game known as the Turing test. (A brief description of the test and its impact on AI is discussed.) Because of the impact of his ideas on the field of AI, Turing is considered by many to be the father of AI.
The term artificial intelligence was coined at the Dartmouth Summer Research Project on Artificial Intelligence in 1956 at Dartmouth College. This two-month workshop was organized by John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester and included as participants Trenchard More from Princeton, Arthur Samuel from IBM, Ray Solomonoff and Oliver Selfridge from MIT, and Allen Newell and Herbert Simon from Carnegie Tech, all of whom played fundamental roles in the development of AI. The Dartmouth workshop is considered the official birthplace of AI as a field, and it provided significant advances from previous work. For example, Allen Newell and Herbert Simon demonstrated a reasoning program, the Logic Theorist, which was capable of working with symbols and not just numbers.
The early years of artificial intelligence were promising and full of successes. Both a symbolic approach (i.e., an approach that uses symbols and rules) and a subsymbolic approach (i.e., an approach that does not use rules but learns by itself) to AI coexisted with many successes. In the symbolic approach, some of the early successes include the presentation of the General Problem Solver by Newell, Shaw, and Simon (1963), a program designed to imitate human problem- solving protocols, and John McCarthy’s LISP (1958), which became one of the predominant languages in AI. Some of the early successes in subsymbolic AI include the development of the Adelines by Widrow and Hoff (1960), which enhanced Hebbs’s learning methods, and the perceptron, by Frank Rosenblatt (1962), which was the precursor of the artificial neural networks we know today.
However, by the end of the 1960s, difficulties arose as the artificial intelligence promises from the decade before fell short and started to be considered “hype.” Research in subsymbolic AI was mostly relegated after Minsky and Papert formally proved in 1969 that perceptrons (i.e., simple neural networks) were flawed in their representation mechanism because they could not represent the XOR (exclusive-OR) logical problem: a perceptron could not be trained to recognize situations in which either one or another set of inputs had to be present, but not both at the same time. The discovery that AI systems were not capable of solving simple logical problems that humans can easily solve resulted in significant reductions in research funding for artificial neural networks, and most researchers of this era decided to abandon the field.
The 1970s focused almost exclusively on different techniques in symbolic artificial intelligence (such as production systems, semantic networks, and frames) and the application of these techniques to the development of expert systems (also known as knowledge-based systems), problem solving, and the understanding of natural language. By the mid-1980s, interest in symbolic AI began to decline because, once again, many promises remained unfulfilled. However, artificial neural networks became interesting again through what became known as the connectionism movement, largely due to two books discussing parallel distributed processing published by Rumelhart and McClelland (1986). These books demonstrated that complex networks could resolve the logical problems (e.g., X‑OR) that early perceptrons could not resolve, and allowed networks to resolve many new problems. This new impulse of AI research resulted in the development of new approaches to AI during the late 1980s and 1990s, such as the subsymbolic approaches of evolutionary computing with evolutionary programming and genetic algorithms, behavior-based robotics, artificial life, and the development of the symbolic Bayesian networks. Today, AI is becoming successful in many different areas, especially in the areas of game playing, diagnosis, logistics planning, robotics, language understanding, problem solving, autonomous planning, scheduling, and control.
Knowledge representation addresses the problem of how knowledge about the world can be represented and what kinds of reasoning can be done with that knowledge. Knowledge representation is arguably the most relevant topic in artificial intelligence because what artificial systems can do depends on their ability to represent and manipulate knowledge. Traditionally, the study of knowledge representation has had two different approaches: symbolic AI (also known as the top-down approach) and subsymbolic AI (also known as the bottom-up approach).
Symbolic Artificial Intelligence (Top-Down Approach)
Symbolic artificial intelligence has been referred to also as conventional AI, classical AI, logical AI, neat AI, and Good Old Fashioned AI (GOFAI). The basic assumption behind symbolic AI is that (human) knowledge can be represented explicitly in a declarative form by using facts and rules: Knowledge, either declarative (or explicit) or procedural (or implicit), can be described by using symbols and rules for their manipulation.
Symbolic AI is traditionally associated with a top down approach because it starts with all the relevant knowledge already present for the program to use and the set of rules to decompose the problem through some inferential mechanism until reaching its goal. A top-down approach can be used only when we know how to formalize and operationalize the knowledge we need to solve the problem. Because of its higher level of representation, it is well suited to perform relatively high-level tasks such as problem solving and language processing. However, this approach is inherently poor at solving problems that involve ill-defined knowledge, and when the interactions are highly complex and weakly interrelated, such as in commonsense knowledge, when we do not know how to represent the knowledge hierarchy, or when we do not know how to represent the mechanism needed to reach a solution. Many different methods have been used in symbolic AI.
Predicate Logic or First-Order Logic
Logic is used to describe representations of our knowledge of the world. It is a well-understood formal language, with a well-defined, precise, mathematical syntax, semantics, and rules of inference. Predicate logic allows us to represent fairly complex facts about the world, and to derive new facts in a way that guarantees that, if the initial facts are true, so then are the conclusions. There are well-defined procedures to prove the truth of the relationships and to make inferences (substitution, modus ponens, modus tollens, unification, among others).
Rule-Based Systems (Production Systems)
A rule-based system consists of a set of IF-THEN rules, a set of facts, and some interpreter controlling the application of the rules, given the facts. A rule-based system represents knowledge in terms of a set of rules that guides the system inferences given certain facts (e.g., IF the temperature is below 65 F degrees AND time of day is between 5:00 p.m. and 11:00 p.m. THEN turn on the heater). Rulebased systems are often used to develop expert systems. An expert system contains knowledge derived from an expert or experts in some domain, and it exhibits, within that specific domain, a degree of expertise in problem solving that is comparable to that of the human experts. Simply put, an expert system contains a set of IF-THEN rules derived from the knowledge of human experts. Expert systems are supposed to support inspection of their reasoning processes, both in presenting intermediate steps and in answering questions about the solution processes: At any time we can inquire why expert systems are asking certain questions, and these systems can explain their reasoning or suggested decisions.
Fuzzy logic is a superset of classical Boolean logic that has been extended to handle the concept of partial truth. One of the limitations of predicate (first-order) logic is that it relies in Boolean logic, in which statements are entirely true or false. However, in the real world, there are many situations in which events are not clearly stated and the truth of a statement is a matter of degree (e.g., if someone states a person is tall, the person can be taller than some people but shorter than other people; thus, the statement is true only sometimes). Fuzzy logic is a continuous form of logic that uses modifiers to describe different levels of truth. It was specifically designed to represent uncertainty and vagueness mathematically and provide formalized tools for dealing with the imprecision intrinsic to many problems. Because fuzzy logic can handle approximate information systematically, it is ideal for controlling and modeling complex systems in which an inexact model exists or systems where ambiguity or vagueness is common. Today, fuzzy logic is found in a variety of control applications such as expert systems, washing machines, video cameras (e.g., focus aperture), automobiles (e.g., operation of the antilock braking systems), refrigerators, robots, failure diagnosis, pattern classifying, traffic lights, smart weapons, and trains.
Semantic Networks, Frames, and Scripts
Semantic networks are graphical representations of information consisting of nodes, which represent an object or a class, and links connecting those nodes, representing the attributes and relations between the nodes. Semantic networks are often called conceptual graphs. Researchers originally used them to represent the meaning of words in programs that dealt with natural language processing (e.g., understanding news), but they have also been applied to other areas, such as modeling memory.
One interesting feature of semantic networks is how convenient they are to establish relations between different areas of knowledge and to perform inheritance reasoning. For example, if the system knows that entity A is human, then it knows that all human attributes can be part of the description of A (inheritance reasoning).
A problem with semantic networks is that as the knowledge to be represented becomes more complex, the representation grows in size, it needs to be more structured, and it becomes hard to define by graphical representations. To allow more complex and structured knowledge representation, frames were developed. A frame is a collection of attributes or slots with associated values that describe some real-world entity. Frame systems are a powerful way of encoding information to support reasoning. Each frame represents a class or an instance (an element of a class, such as height) and its slots represent an attribute with a value (e.g., seven feet).
Scripts are used to develop ideas or processes that represent recurring actions and events. They are often built on semantic networks or frames, although production systems are also common. Scripts are used to make inferences on a whole set of actions that fall into a stereotypical pattern. A script is essentially a prepackaged inference chain relating to a specific routine situation. They capture knowledge about a sequence of events, and this knowledge has been used as a way of analyzing and describing stories. Typical examples of scripts are the sequence of actions and the knowledge needed to, for example, take a flight or buy a train ticket.
Bayesian networks are also known as belief networks, probabilistic networks, causal networks, knowledge maps, or graphical probability models. They are a probabilistic graphical model with nodes, which represent discrete or continuous variables, and links between those nodes, which represent the conditional dependencies between variables. This graphical representation with nodes and links connecting the nodes provides an intuitive graphical visualization of the knowledge, including the interactions among the various sources of uncertainty. Because a Bayesian network is a complete model for the variables and their relationships, it can be used to answer probabilistic queries about them, and it can allow us to model and reason about uncertainty in complex situations. For example, a Bayesian network can be used to calculate the probability of a patient having a specific disease, given the absence or presence of certain symptoms, if the probabilistic dependencies between symptoms and disease are assumed to be known.
Bayesian networks have been used for diverse applications, such as diagnosis, expert systems, planning, learning, decision making, modeling knowledge in bioinformatics (gene regulatory networks, protein structure), medicine, engineering, document classification, image processing, data fusion, decision support systems, and e-mail spam filtering.
Subsymbolic Artificial Intelligence (Bottom-Up Approach)
After researchers became disillusioned in the mid- 1980s with the symbolic attempts at modeling intelligence, they looked into other possibilities. Some prominent techniques arose as alternatives to symbolic AI, such as connectionism (neural networking and parallel distributed processing), evolutionary computing, particle swarm optimization (PSO), and behavior-based AI.
In contrast with symbolic AI, subsymbolic AI is characterized by a bottom-up approach to AI. In this approach, the problem is addressed by starting with a relatively simple abstract program that is built to learn by itself, and it builds knowledge until reaching an optimal solution. Thus, it starts with simpler elements and then, by interacting with the problem, moves upwards in complexity by finding ways to interconnect and organize the information to produce a more organized and meaningful representation of the problem.
The bottom-up approach has the advantage of being able to model lower-level human and animal functions, such as vision, motor control, and learning. It is more useful when we do not know how to formalize knowledge and we do not know how to reach the answer beforehand.
Neural networks, or more correctly, artificial neural networks, to differentiate them from biological neural networks, are computing paradigms loosely modeled after the neurons in the brain and designed to model or mimic some properties of biological neural networks. They consist of interconnected processing elements called nodes or neurons that work together to produce an output function. The output of a neural network depends on the cooperation of the individual neurons within the network. Because it relies on its collection of neurons to perform or reach a solution, it can still perform its overall function even if some of the neurons are not functioning, which makes them able to tolerate error or failure. They learn by adapting their structure based on external or internal information that flows through the network. Thus, they are mostly used to model complex relationships between inputs and outputs or to find patterns in data.
As you may recall, one of the simplest instantiations of a neural network, the perceptrons, were very popular in the early 1960s (Rosenblatt, 1962), but interest in them dwindled at the end of the 1960s because they were not able to represent some simple logical problems (Minsky & Papert, 1969). They became hugely popular again in the mid-1980s (McClelland et al., 1986; Rumelhart et al., 1986) because the problem associated with the perceptron was addressed and because of the decreased interest in symbolic approaches to AI. The utility of artificial neural network models lies in the fact that they can be used to infer a function from observations. Such inferences are particularly useful in applications in which the complexity of the data or task makes the design of such a function by hand impractical.
There are three major learning paradigms in the neural network field, each corresponding to a particular abstract learning task: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the network is given a set of examples (inputs) with the correct responses (outputs), and it finds a function that matches these examples with the responses. The network infers the mapping implied by the data by using the mismatch between the mapping and the data and correcting the weight of the connection between the nodes until the network is able to match the inputs with the outputs. With this function, new sets of data or stimuli, previously unknown to the system, can be correctly classified. In unsupervised learning, the network has to learn patterns or regularities in the inputs when no specific output values are supplied or taught to the network. Finally, in reinforcement learning, the set of examples with their answers are not given to the network, but are found by interacting with the environment and integrating the instances that lead to reinforcement. The system performs an action and, based on the consequences of that action (some cost according to some dynamics), relates the stimuli (inputs) and the responses (outputs). Artificial neural networks have been applied successfully to speech recognition, image analysis, adaptive control and navigation, video games, autonomous robots, decision making, detection of credit card fraud, data mining, and pattern recognition, among other areas.
Evolutionary computation applies biologically inspired concepts from natural selection (such as populations, mutation, reproduction, and survival of the fittest) to generate increasingly better solutions to the problem. Some of the most popular methods are evolutionary programming, evolution strategies, and genetic algorithms. Evolutionary computation has been successfully applied to a wide range of problems including aircraft design, routing in communications networks, game playing, robotics, air traffic control, machine learning, pattern recognition, market forecasting, and data mining. Although evolutionary programming, evolution strategies, and genetic algorithms are similar at the highest level, each of these varieties implements evolutionary algorithms in a different manner.
Evolutionary programming, originally conceived by Lawrence J. Fogel in 1960, emphasizes the relationship between parent solutions (the solutions being analyzed) and their offspring (new solutions resulting from some modification of the parent solutions). Fogel, Owens, and Walsh’s 1966 book Artificial Intelligence Through Simulated Evolution is the landmark publication in this area of AI. In general, in evolutionary programming, the problem to be solved is represented or encoded in a string of variables that defines all the potential solutions to the problem. Each full set of variables with its specific values is known as an individual or candidate solution. To solve the problem, a population of “individuals” is created, with each individual representing a random possible solution to the problem. Each of the individuals (i.e., each candidate solution) is evaluated and assigned a fitness value based on how effective the candidate solution is to solving the problem. Based on this fitness value, some individuals (usually the most successful) are selected to be parents, and offspring are generated from these parents.
In the generation process, a mutation operator selects elements of the parents’ representation of the solution and manipulates these elements when they are transferred to the offspring. A mutation operator is a rule that selects random variables and randomly alters the value of these variables in some degree, generating new individuals or candidate solutions from the selected parents. Thus, some characteristics of the parent solutions are changed slightly and then transferred to the offspring solution. In general, the degree of mutation is greater in the first generations, and it is gradually decreased as generations evolve and get closer to an optimal solution. The offspring candidate solutions are then evaluated based on their fitness, just like their parents were, and the process of generating offspring from the parents is repeated until an individual with sufficient quality (an optimal solution to the problem) is found or a previously determined computational limit is reached (e.g., after evolving for a given number of generations).
Evolution strategies (Bäck, Hoffmeister, & Schwefel, 1991; Rechenberg, 1973) and evolutionary programming share many similarities. The main difference is that, in evolution strategies, offspring are generated from the selected parents not only by using a mutation operator but also by recombination of the code from selected parents through a crossover operator. A crossover operator applies some rule to recombine the elements of the selected parents to generate offspring. The recombination operation simulates some reproduction mechanism to transfer elements from the parents to their offspring.
The crossover operator can take many variants (e.g., interchange the first half of the elements from one of the parents and the second half from the other one for one offspring; the reverse for another offspring). The crossover operator is inspired by the role of sexual reproduction in the evolution of living things. The mutation operator is inspired by the role of mutation in natural evolution. Generally, both mutation and reproduction are used simultaneously. Recombination and mutation create the necessary diversity and thereby facilitate novelty, while selection acts as a force increasing quality.
Genetic algorithms, popularized by John Holland (1975), are similar to evolution strategies in the general steps that the algorithm follows. However, there are substantial differences in how the problem is represented. One of the main differences is that the problem to be solved is encoded in each individual of the population by having arrays of bits (bit-strings), which represent chromosomes. Each bit in the bit-string is analogous to a gene (i.e., an element that represents a variable or part of a variable of the problem). In a genetic algorithm, each individual or candidate solution is encoded at a genotype level, whereas in evolutionary programming and evolution strategy, the problem is encoded at a phenotype level, in which there is a one-to-one relationship between each value encoded in the phenotype and the real value that it represents in the problem. A genetic algorithm can differentiate between genotype (the genes) and phenotype (the expression of a collection of genes). The manipulation at the level of genotype allows for more elaborated implementation of the crossover operator and the mutation operator. Additionally, the focus on genetic algorithms when creating offspring in successive generations is on reproduction (crossover) and no mutation, which is often considered as a background operator or secondary process.
Particle Swarm Optimization
PSO applies the concept of social interaction to problem solving. It was developed in 1995 by James Kennedy and Russ Eberhart, and it is inspired by the social behavior of bird flocking and fish schooling.
PSO shares many similarities with evolutionary computation techniques. The system is initialized with a population of random potential solutions (known in this framework as particles), which searches for an optimal solution to the problem by updating generations. However, unlike evolutionary computing, PSO has no evolution operators such as crossover and mutation. In PSO, the particles fly through the problem space by following the current best solution (the particle with the best fitness). Each particle (individual) records its current position (location) in the search space, records the location of the best solution found so far by the particle, and a gradient (direction) in which the particle will travel if undisturbed. In order to decide whether to change direction and in which direction to travel (searching for the optimal solution), the particles work with two fitness values: one for that specific particle, and another for the particle closer to the solution (best candidate solution). Thus, particles can be seen as simple agents that fly through the search space and record (and possibly communicate) the best solution that they have discovered so far. Particles travel in the search space by simply adding their vector (the direction in which they were traveling) and the vector (direction) of the best solution candidate. Then, each particle computes its new fitness, and the process continues until the particles converge on an optimal solution.
Behavior-Based Artificial Intelligence
Behavior-based AI is a methodology for developing AI based on a modular decomposition of intelligence. It was made famous by Rodney Brooks at MIT (see Brooks, 1999, for a compendium of his most relevant papers in the topic), and it is a popular approach to building simple robots, which surprisingly appear to exhibit complex behavior. The complexity of their behavior lies in the perception of the observer, not in the processing mechanism of the system. This approach questions the need for modeling intelligence using complex levels of knowledge representation and the need for a higher cognitive control. Brooks presents a series of simple robots that mimic intelligent behavior by using a set of independent semiautonomous modules, which interact independently with the environment and are not communicating information at any higher level. For example, a spiderlike robot will navigate through a path with obstacles just by each of its legs addressing its own situation, without a mechanism relating what each other leg knows about the environment. This approach has been successful when dealing with dynamic, unpredictable environments. Although behavior-based AI has been popular in robotics, it also can be applied to more traditional AI areas.
Artificial Intelligence in Psychology
The greatest impact of AI in psychology has been through the development of what has come to be known as the information processing paradigm or the computer analogy. Once computers started to be perceived as information-processing systems able to process symbols and not just numbers, an analogy between computers and the human mind was established. Both systems receive inputs (either through sensors or as the output from other processes or devices), process those inputs through a central processing unit, and generate an output (through motor responses or as the input for other processes or devices). The idea that the mind works on the brain just as a program works on a computer is the focus of cognitive psychology, which is concerned with information-processing mechanisms focusing especially on processes such as attention, perception, learning, and memory. It is also concerned with the structures and representations involved in cognition in general.
One of the dominant paradigms in psychology, before the surge of AI at the end of the 1950s, was behaviorism, in which the focus was on the study of the responses of the organism (behavior) given particular inputs (stimuli). Its main assumption was that, because researchers can only scientifically study what they can observe and measure, behavior should be the only subject matter of study of scientific psychology. With cognitive psychology and the computer analogy, the focus started to shift toward the study of mental processes, or cognition. Cognitive psychology is interested in identifying in detail what happens between stimuli and responses. To achieve this goal, psychological experiments need to be interpretable within a theoretical framework that describes and explains mental representations and procedures. One of the best ways of developing these theoretical frameworks is by forming and testing computational models intended to be analogous to mental operations. Thus, cognitive psychology views the brain as an information-processing device that can be studied through experimentation and whose theories can be rigorously tested and discussed as computer programs.
A stronger focus on computer modeling and simulations and on study of cognition as a system resulted in the development of cognitive science. Cognitive science is an interdisciplinary discipline concerned with learning how humans, animals, and machines acquire knowledge, represent that knowledge, and how those representations are manipulated. It embraces psychology, artificial intelligence, neuroscience, philosophy, linguistics, anthropology, biology, evolution, and education, among other disciplines.
More recently, AI philosophy and techniques have impacted a new discipline, cognitive neuroscience, which attempts to develop mathematical and computational theories and models of the structures and processes of the brain in humans and other animals. This discipline is concerned directly with the nature of the brain and tries to be more biologically accurate by modeling the behavior of neurons, simulating, among other things, the interactions among different areas of the brain and the functioning of chemical pathways. Cognitive neuroscience attempts to derive cognitive-level theories from different types of information, such as computational properties of neural circuits, patterns of behavioral damage as a result of brain injury, and measures of brain activity during the performance of cognitive tasks.
A new and interesting approach resulting from developments in computer modeling is the attempt to search and test for a so-called unified architecture (a unified theory of cognition). The three most dominant unified theories of cognition in psychology are SOAR (based on symbolic AI), PDP (based on subsymbolic AI), and ACT-R (originally based on symbolic AI, currently a hybrid, using both symbolic and subsymbolic approaches).
SOAR (State Operator and Result)
SOAR (Laird, Newell, & Rosenbloom, 1987; Newell, 1990) describes a general cognitive architecture for developing systems that exhibit intelligent behavior. It represents and uses appropriate forms of knowledge such as procedural, declarative, and episodic knowledge. It employs a full range of problem-solving methods, and it is able to interact with the outside world.
PDP (Parallel Distributed Processing)
In the 1980s and 1990s, James L. McClelland, David E. Rumelhart, and the PDP Research Group (McClelland et al., 1986; Rumelhart et al., 1986) popularized artificial neural networks and the connectionist movement, which had lain dormant since the late 1960s. In the connectionist approach, cognitive functions and behavior are perceived as emergent processes from parallel, distributed processing activity of interconnected neural populations, with learning occurring through the adaptation of connections among the participating neurons. PDP attempts to be a general architecture and explain the mechanisms of perception, memory, language, and thought.
ACT-R (Adaptive Control of Thought-Rational)
In its last instantiation, ACT-R (Anderson et al., 2004; Anderson & Lebiere, 1998) is presented as a hybrid cognitive architecture. Its symbolic structure is a production system. The subsymbolic structure is represented by a set of massive parallel processes that can be summarized by a number of mathematical equations. Both representations work together to explain how people organize knowledge and produce intelligent behavior. ACT-R theory tries to evolve toward a system that can perform the full range of human cognitive tasks, capturing in great detail how we perceive, think about, and act on the world. Because of its general architecture, the theory is applicable to a wide variety of research disciplines, including perception and attention, learning and memory, problem solving and decision making, and language processing.
The benefits of applying computer modeling to psychological hypotheses and theories are multiple. For example, computer programs provide unambiguous formulations of a theory as well as means for testing the sufficiency and consistency of its interconnected elements. Because computer modeling involves using a well-formulated language, it eliminates vagueness and highlights hidden or ambiguous intermediated processes that were not previously known or made explicit with the verbal description of the theory. Explicit formulations also allow researchers to falsify (i.e., test) the theory’s assumptions and conclusions. Additionally, given the same data set, alternative programs/theories can be run through the data to analyze which hypotheses are more consistent with the data and why.
Computer modeling has focused mostly in the areas of perception, learning, memory, and decision making, but it is also being applied to the modeling of mental disorders (e.g., neurosis, eating disorders, and autistic behavior), cognitive and social neuroscience, scientific discovery, creativity, linguistic processes (e.g., dyslexia, speech movements), attention, and risk assessment, among other fields. Importantly, the impact of AI on psychology has not been relegated to only theoretical analyses. AI systems can be found in psychological applications such as the development of expert systems for clinical diagnosis and education, human-computer interaction and user interfaces, and many other areas, although the impact in application areas is not as extended as might be expected—there seems to be much work to do in this area.
Criticisms of Artificial Intelligence
Is true AI possible? Can an AI system display true intelligence and consciousness? Are these functions reserved only to living organisms? Can we truly model knowledge?
Weak AI Versus Strong AI
The weak AI view (also known as soft-cautious AI) supports the idea that machines can be programmed to act as if they were intelligent: Machines are only capable of simulating intelligent behavior and consciousness (or understanding), but they are not really capable of true understanding. In this view, the traditional focus has been on developing machines able to perform in a specific task with no intention of building a complete system able to perform intelligently in all or most situations.
Strong AI (also known as hard AI) supports the view that machines are really intelligent, and that, someday, they could have understanding and conscious minds. This view assumes that all mental activities of humans can be eventually reducible to algorithms and processes that can be implemented in a machine. Thus, for example, there should be no fundamental differences between a machine that emulates all the processes in the brain and the actions of a human being, including understanding and consciousness. One of the problems with the strong AI view centers on the following two questions: (a) How do we know that an artificial system is truly intelligent? (b) What makes a system (natural or not) intelligent? Even today, there is no clear consensus on what intelligence really is. Turing (1950) was aware of this problem and, recognizing the difficulties in agreeing on a common definition on intelligence, proposed an operational test to circumvent this question. He named this test the imitation game and it was later known as the Turing test.
The Turing test is conducted with two people and a machine. One person, who sits in a separate room from the machine and the other person, plays the role of an interrogator or judge. The interrogator knows the person and the machine only as A and B, respectively, and has no way of knowing beforehand which is the person and which is the machine. The interrogator can ask A and B any question she wishes. The interrogator’s aim is to determine which one is the person and which one is the machine. If the machine fools the interrogator into thinking that it is a person, then we can conclude that the machine can think and it is truly intelligent, or at least as intelligent as the human counterpart. The Turing test has become relevant in the history of AI because some of the criticisms and philosophical debates about the possibilities of AI have focused on this test.
Criticisms of the Strong AI View
For many decades it has been claimed that Gödel’s (1931) incompleteness theorem precludes the possibility of the development of a true artificial intelligence. The idea behind the incompleteness theorem is that within any given branch of mathematics, there will always be some propositions that cannot be proved or disproved within the system using the rules and axioms of that mathematical branch itself. Thus, there will be principles that cannot be proved in a system, and the system cannot deduce them. Because machines do little more than follow a set of rules, they cannot truly emulate human behavior. Human behavior is too complex to be described by any set of rules.
Machines Cannot Have Understanding—The Chinese Room Argument
John Searle stated in his Chinese room argument that machines work with encoded data that describe other things, and that data are meaningless without a cross-reference to the things described. This point led Searle to assert that there is no meaning or understanding in a computational machine itself. As a result, Searle claimed that even a machine that passes the Turing test would not necessarily have understanding or be conscious. Consciousness seems necessary in order to show understanding.
In his thought experiment, Searle asked people to imagine a scenario in which he is locked in a room and receives some Chinese writing (e.g., a question). He does not know Chinese, but he is given a rule book with English instructions that allows him to correlate the set of Chinese symbols he receives with another set of Chinese symbols (e.g., by their shape), which he would give back as an answer. Imagine the set of rules to correlate both sets of symbols is so advanced that it allows him to give a meaningful answer to the question in Chinese. Imagine the same thing happening with an English question and its answer. For a Chinese and an English observer, respectively, the answers from Searle are equally satisfying and meaningful (i.e., intelligent). In both situations Searle would pass the Turing’s test. However, can we say Searle understands Chinese in the same way that he understands English? Actually, he does not know any Chinese. A computer program would behave similarly by taking a set of formal symbols (Chinese characters) as input and, following a set of rules in its programming, correlating them with another set of formal symbols (Chinese characters), which it presents as output. In this thought experiment, the computer would be able to pass the Turing test. It converses with Chinese speakers, but they do not realize they are talking with a machine. From a human observer’s view, it seems that the computer truly understands Chinese. However, in the same way that Searle does not understand Chinese, the machine does not understand Chinese either. What the computer does is mindless manipulation of the symbols, just as Searle was doing. Although it would pass the Turing test, there is no genuine understanding involved.
Several other authors have argued against the possibility of a strong AI (see, e.g., Penrose, 1989). The arguments denying the possibility of a strong AI and their counterarguments have populated the AI literature, especially the philosophical discussion of AI.
Machines able to sense and acquire experiences by interacting with the world should be able to solve most of these criticisms. In fact, most AI researchers are concerned with specific issues and goals to resolve, such as how to improve actual systems or how to create new approaches to the problem of AI, and not passing the Turing test and proving that AI systems can be truly intelligent. Additionally, the Turing test has been seriously criticized as a valid test for machine intelligence. For example, Ford and Hayes (1998) highlighted some of the problems with the Turing test. The central defect of the Turing test is its species-centeredness. It assumes that human thought is the highest achievement of thinking against which all other forms of thinking must be judged. The Turing test depends on the subjectivity of the judge, and it is culture-bound (a conversation that passes the test in the eyes of a British judge might fail it according to a Japanese or Mexican judge). More important, it does not admit as measures of intelligence weaker, different, or even stronger forms of intelligence than those deemed human. Ford and Hayes made this point clearly when they compared AI with artificial flight. In the early thinking about flight, success was defined as the imitation of a natural model: for flight, a bird; for intelligence, a human. Only when pioneers got some distance from the model of a bird did flying become successful. In the same analogy, you do not deny an airplane can fly just because it does not fly like a bird does. However, truly artificial intelligence is denied if it does not parallel that of a human being.
Many supporters of strong AI consider that it is not necessary to try to isolate and recreate consciousness and understanding specifically. They accept that consciousness could be a by-product of any sufficiently complex intelligent system, and it will emerge automatically from complexity. They focus on the analogy of the human brain. In humans, a single neuron has nothing resembling intelligence. Only when billions of neurons combine to form a mind does intelligence emerge. It would appear, then, that the brain is more than the sum of its parts. Intelligence emerges with sufficient joint complexity of neurons.
In the final analysis, the debate of the possibility of a strong AI seems to be based on the classical mind/body problem. On the one side, we have the physicalism view (the belief that nothing exists but the physical world that can be studied scientifically; mental states are assumed to mirror brain states). In this view, strong AI could be eventually possible. On the other side, we have the view based on dualism (mind and matter are not the same thing). In this view, strong AI is not possible.
The most important influence of AI in psychology has been the computer metaphor, in which both living organisms and computers can be understood as information processors. The information-processing approach has brought psychology new paradigms such as cognitive psychology and cognitive science. The goal of these disciplines is to learn what happens within the organism, at the level of the brain. Brain or mental processes cannot be observed directly, but computer formalization of theories of how mental processes work can be tested scientifically.
Although AI and cognitive science share techniques and goals, there is at least one fundamental difference between them. Psychology has the restriction that its computer programs and simulations have to achieve the same results as the simulated system (human and animal cognition), and they have to do it following the same processes. Only when there is a match with real processes can psychologists assume that the relationships proposed in the program were correct. AI does not have a restriction of similarity of processes: Psychological theory must be able to predict error empirically. AI focuses on efficiency; psychology focuses on plausibility.
Additionally, computer modeling and the focus on brain structure and their function have given rise to the field of neuroscience. Neuroscience is one of the most rapidly emerging disciplines in psychology, largely due to computer modeling. Computer modeling allows for the formalization of models and the testing of hypotheses about how neurons work together. The new computational techniques applied to brain activity measurement (e.g., fMRI) allow researchers to observe how brain structures actually work. Thus, formal models that represent theories and hypotheses can be tested against real data.
The impact of cognitive modeling in psychology should increase as psychological models move forward toward a unified theory of cognition. Only through computer formalizations and implementations does it seem plausible to work on a unified view of psychology in which all the theories might be integrated in a single framework. One of the underlying assumptions of computer modeling is that the role of formal modeling of theories and hypotheses in psychology plays a role similar to the one of mathematics in the physical sciences.
Although computer modeling has strongly influenced AI in psychology, the impact of AI in psychology goes far beyond computer modeling. AI techniques can be applied to many areas of psychology, especially those that focus on diagnosis, classification, and decision making.
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How to Write a Research Paper on Artificial Intelligence
In the modern world, the subject of artificial intelligence and the field of development of intelligent technologies have ceased to be the prerogative of a purely scientific community. It is impossible to overestimate the importance of creating a system of artificial intelligence functioning at the necessary and sufficient level, for which the presence of reason will be recognized.
There is a significant success of IT developers, neuroscientists, psychologists, physicists, and other specialists, who for a long time have been limited to individual scientific disciplines and now united in the context of interdisciplinarity. However, reading about it is exciting and promising, but writing about it is exceptionally hard. To help you out, we’ve interviewed professional academic writers from Smart Writing Service and they shared up-to-date tips on how to write a research paper on artificial intelligence.
Talk About the Most Trending Approaches
- The scientific community sees different versions of developments in the field of artificial intelligence. D. Hawkins, for example, proposes an integrative approach that combines engineering, neurobiological, cognitive, and even ethical approaches. Within the framework of the integrative approach, there is no reason to expect from a reasonable machine that it should look, act, feel, or think like a person. “The thoughts and behavior of a rational machine may differ significantly from those of a human being, and it will have intelligence, which is determined by the predictive ability of hierarchical memory, and not by human-like behavior.”
- The physicist and mathematician Roger Penrose, who works in the field of the general theory of relativity and quantum theory, proves the impossibility of unfolding human intelligence into algorithms. Behind all these arguments is the “obviousness” of the assumption that “a mind endowed with consciousness simply cannot work like a computer, despite the algorithmic nature of many components of our mental activity.”
- Sphere of application. About the spheres of application of artificial intelligence, Ignacy Belda argues: “Artificial intelligence gradually entered our lives. Sooner or later, the day will come when there will be systems with the same level of creativity, sensation, and emotional intelligence as a person. On the day when this happens, we will understand that we are not alone.” The classic textbook for artificial intelligence in the United States was the work of well-known computer scientists Stewart Russell and Peter Norvig “Artificial Intelligence: A Modern Approach,” in which artificial intelligence is defined as “the science of agents who receive results from their environment-related actions
State What Make AI Project Successful
Traditionally, an indicator of overall success in the development of artificial intelligence systems is considered to be the ability to externally model typical human functions, qualities, and properties, thereby surpassing a person in typical human activities. Manifestations and “self-realization” of the developed samples are perceived through the prism of the human factor and the so-called “effect of AI” (meaninglessness and “demythologization” of activity), which is a latent, but nonetheless global problem in this sphere.
The problem is particularly relevant due to the lack of criteria for interpreting and “understanding” what we have as the results of activities in the field of developing artificial intelligence. Namely, a purely algorithmic mechanism lacking the capacity for understanding, or a psycho-machine with the potential for the emergence of proto-metal qualities, that is, the makings of the psyche and, perhaps, intelligence. Despite the terminological features of the very concept of “artificial intelligence,” in the world scientific community, it is considered that the presence of consciousness, and not of intelligence, will become a necessary and sufficient basis for recognizing a machine reasonable.
Offer Not Trivial Solutions
In contrast to the development of logo-machines, offer a more creative idea: the formation of a psycho-machine. The purpose of the psycho-machine is not to replace a person in complex, unpleasant, or non-odd activities or to compete with a person in intellectual or logical tasks. For this machine, it is not at all necessary to demonstrate intellectual or mental indicators, but instead having an extensive structured and well-written base of the corresponding algorithms, which will allow it to quite successfully cope with activities that are beyond the power of a person due to the human factor. The idea of the psycho-machine is much more ambitious, and even spiritual and specifically existential in its own way.
In essence, we are talking about creating something much larger than the man himself, something super-anthropic or even meta-anthropic. And this is precisely the idea of creating a psycho-machine, which is the apotheosis and quintessence of human capabilities, as well as the resolution of the so-called “Complex of God.” The established technology should immeasurably surpass the human skills and abilities in the sphere of mental, intellectual, spiritual, and existential.
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Learn How to Use ChatGPT to Write a Scientific Research Paper
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Are you a student struggling to write your scientific research paper? If yes, then don’t worry. Especially, to make your academic writing process easier, there are several free AI writing tools available on the market. One such free popular writing tool is ChatGPT with which you can effectively come up with an excellent scientific research paper. In case, you have no idea how to use ChatGPT to write a scientific research paper, continue reading this blog post. Here, we have shared the simple and effective steps to write a scientific research paper using ChatGPT. Simply by following the ways suggested here, you can craft an outstanding scientific research paper deserving of an A+ grade.
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What is a Scientific Research Paper?
A Scientific research paper is a detailed academic document that is prepared to summarize the findings of extensive research conducted on any science topic. Especially, through scientific research papers, one can communicate important scientific findings and discoveries to the large academic and research community.
This academic paper is being written to distinguish its dependence on objective, empirical analysis, and evidence-based data. Moreover, its ultimate aim is to analyze certain research questions and prove the relevant thesis statement with valid supporting evidence and proper citations.
When it comes to writing a scientific research paper, it is necessary to organize all the ideas clearly. So, a well-structured scientific research paper can be crafted by including essential sections such as an abstract, introduction, literature review, methodology, results, discussion, and conclusion. Remember, the composed scientific research paper should neatly present the analysis, results, and evidence to the readers.
What is ChatGPT?
ChatGPT is an artificial intelligence language model developed by OpenAI. It will help to generate text, videos, and images based on the input or prompts provided by the user. This is one of the effective writing tools in which the users to have a human-like conversation with a Chatbot. Especially, professionals from different sectors can earn a lot of benefits by using ChatGPT for various tasks. Moreover, with the help of ChatGPT, students can also create excellent scientific research papers, theses, dissertations, essays, reports, and other academic documents.
Steps for Writing a Scientific Research Paper
Before we look at how to use ChatGPT to write a scientific research paper, it is important to understand the fundamental steps for writing a scientific research paper.
In case, you are asked to submit a scientific research paper, then this is what you should do.
- Select a topic: Firstly, analyze and find a good scientific research paper topic matching your area of interest. Also, remember the topic you have chosen should be original, specific, significant, feasible, and adhere to ethical standards.
- Conduct Research: Secondly, conduct in-depth research on the selected topic and gather the important details, facts, and evidence from relevant and credible materials to prove your thesis statement.
- Create an Outline: Thirdly, organize all the gathered ideas and sketch a well-structured scientific research paper outline as per the standard format by including the major components.
- Compose the scientific research paper: Next, elaborate on the outline and draft a detailed scientific research paper in accordance with your university guidelines.
- Proofread the paper: Lastly, after you complete writing your scientific research paper, double-check the content. Then, rectify if there are any grammar, spelling, and punctuation errors in the paper. Note that, the final draft of the academic paper that is ready for submission should be non-plagiarized, and error-free.
Also Read : ChatGPT: Opportunities and Challenges For Education
Standard Format of a Scientific Research Paper Outline
Composing a well-structured scientific research paper always begins with creating a neat outline. So, first, as per the standard format, you need to sketch an excellent scientific research paper outline by including the key sections listed below.
- Title: Begin with a clear and informative title related to the subject of your study.
- Abstract: In a single paragraph, summarize your paper’s aims, methodology, findings, and conclusions.
- Introduction: State the relevance of the study topic and explain your research question or hypothesis.
- Literature Review: Discuss important earlier research and its relevance to your project.
- Methodology: Explain your research methodology, data collection methods, and data analysis approaches.
- Results: Use tables, graphs, or written explanations to present your findings.
- Discussion: Interpret your findings, explain their consequences, and address any shortcomings.
- Conclusion: Summarize your significant results and their larger implications.
- References: Use a specific citation style (e.g., APA, MLA) to cite all sources used in your article.
- Appendices: Add any additional materials that are required.
Till now, we have seen the basic steps for writing a scientific research paper and the standard format of a scientific research paper outline. In the next section, let us see how to use ChatGPT to write a scientific research paper effectively.
Learn How to Use ChatGPT to write a Scientific Research Paper
Are you unsure how to write a scientific research paper using ChatGPT? If yes, then just follow the simple and effective ways that are recommended below.
ChatGPT will help you begin your scientific research paper by creating interesting ideas and suggesting relevant topics. If you provide a brief description of your research topic to ChatGPT, it will recommend research questions. Additionally, it will also suggest hypotheses or research directions.
Create Literature Review
It is important to conduct a literature review before you start crafting a scientific research paper. You can ask ChatGPT to summarize existing research and highlight important references or primary sources.
Formulate Research Question
The core of a research paper is formulating a clear and succinct research question or a topic. You can use ChatGPT to refine your research question so that it is specific, relevant, and connected to your goals.
Identify the Methodology
You can get guidance from ChatGPT for selecting the best experimental design and research methodologies for your study. Usually, ChatGPT will explain several approaches and will then help you decide which methodology is appropriate for your study.
Ask ChatGPT for assistance with statistical analysis and data interpretation. Remember, for data analysis, all you need to give ChatGPT is the relevant data that you have collected for your research. Based on the data you have provided, ChatGPT will perform data analysis, interpret results, and create charts or graphs.
Compose the Introduction
The introduction is the key component of a scientific research paper. With the help of ChatGPT, you can create an interesting opening that gives background information, establishes context, and defines the aims of your research.
Also Read : What Is AGI Artificial Intelligence?
Craft the Methodology Section
Work together with ChatGPT to compose the methodology section of your scientific research paper. The tool is capable of providing extensive explanations of data collection methods, experimental procedures, and statistical approaches.
Present Results and Discussion
ChatGPT can help you organize your findings and discussion areas. Also, it can assist you with explaining the relevance of your results, connecting them to your research topic, and suggesting potential interpretations.
For a scientific research paper, proper citation and referencing is a must. By using ChatGPT, you can format your citations and generate a list of references as per different citation styles such as APA, MLA, Chicago, etc.
Proofread and Edit
ChatGPT can help you proofread and modify your research paper once you’ve finished it. Also, it can detect and rectify grammatical errors, enhance sentence structure, and improve the overall readability of your paper.
Sample ChatGPT prompts for Writing a Scientific Research Paper
Basically, ChatGPT is simple and easy to use. If you enter a prompt in ChatGPT, it will give you relevant results. In case, you are confused about what prompt to give in ChatGPT for writing your scientific paper, take a look below. Here, we have suggested some sample ChatGPT prompts for scientific research paper writing.
- I have to write a research paper on ‘Globalization of Fashion’. It must be ten pages long with five different original sources cited. Can you help me think of a certain topic?
- Could you give me an outline for a ten-page long research paper that must include five original sources on the topic “The role of fashion in shaping cultural identities”?
- Can you give me sources for a ten-page long paper on this topic, “The role of fashion in shaping cultural identities”?
Similarly, by using valid prompts you can compose an outstanding scientific research paper using the ChatGPT.
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We hope you are now aware of how to use ChatGPT to write a scientific research paper. But use the tool only as an assistant for your writing process rather than relying entirely on it for writing a scientific research paper. In case, you still have doubts about using ChatGPT for preparing your scientific research paper, without any hesitation contact us quickly. We have experienced and well-qualified assignment helper and research paper writers from different educational backgrounds to offer high-quality scientific research paper help online. Particularly, as per your needs, our scholarly writers will compose and deliver a plagiarism-free scientific research paper on time and will help you achieve top grades.
Just book your order on our website and enjoy the extraordinary benefits offered by our scientific research paper writing help service.
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A Guide to Using AI Tools to Summarize Literature Reviews
Table of Contents
Needless to say, millions of scientific articles are getting published every year making it difficult for a researcher to read and comprehend all the relevant publications.
Back then, researchers used to manually conduct literature reviews by sifting through hundreds of research papers to get the significant information required for the research.
Fast forward to 2023 — things have turned out quite distinct and favorable. With the inception of AI tools, the literature review process is streamlined and researchers can summarize hundreds of research articles in mere moments. They can save time and effort by using AI tools to summarize literature reviews.
This article articulates the role of the top AI tools used to summarize literature reviews. You can also learn how AI is used as a powerful tool for summarizing scientific articles and understanding the impact of AI on academic research.
Understanding the Role of AI Tools in Literature Reviews
Before we talk about the benefits of AI tools to summarize literature reviews, let’s understand the concept of AI and how it streamlines the literature review process.
Artificial intelligence tools are trained on large language models and they are programmed to mimic human tasks like problem-solving, making decisions, understanding patterns, and more. When Artificial Intelligence and machine learning algorithms are implemented in literature reviews, they help in processing vast amounts of information, identifying highly relevant studies, and generating quick and concise summaries — TL;DR summaries.
AI has revolutionized the process of literature review by assisting researchers with powerful AI-based tools to read, analyze, compare, contrast, and extract relevant information from research articles.
By using natural language processing algorithms, AI tools can effectively identify key concepts, main arguments, and relevant findings from multiple research articles at once. This assists researchers in quickly understanding the overview of the existing literature on a respective topic, saving their valuable time and effort.
Key Benefits of Using AI Tools to Summarize Literature Review
1. best alternative to traditional literature review.
Traditional literature reviews or manual literature reviews can be incredibly time-consuming and often require weeks or even months to complete. Researchers have to sift through myriad articles manually, read them in detail, and highlight or extract relevant information. This process can be overwhelming, especially when dealing with a large number of studies.
However, with the help of AI tools, researchers can greatly save time and effort required to discover, analyze, and summarize relevant studies. AI tools with their NLP and machine learning algorithms can quickly analyze multiple research articles and generate succinct summaries. This not only improves efficiency but also allows researchers to focus on the core analysis and interpretation of the compiled insights.
2. AI tools aid in swift research discovery!
AI tools also help researchers save time in the discovery phase of literature reviews. These AI-powered tools use semantic search analysis to identify relevant studies that might go unnoticed in traditional literature review methods. Also, AI tools can analyze keywords, citations , and other metadata to prompt or suggest pertinent articles that align and correlate well with the researcher’s search query.
3. AI Tools ensure to stay up to date with the most research ideas!
Another advantage of using AI-powered tools in literature reviews is their ability to handle the ever-increasing volume of published scientific research. With the exponential growth of scientific literature, it has become increasingly challenging for researchers to keep up with the latest scientific research and biomedical innovations.
However, AI tools can automatically scan and discover new publications, ensuring that researchers stay up-to-date with the most recent developments in their field of study.
4. Improves efficiency and accuracy of Literature Reviews
The use of AI tools in literature review reduces the occurrences of human errors that may occur during traditional literature review or manual document summarization. So, literature review AI tools improve the overall efficiency and accuracy of literature reviews, ensuring that researchers can access relevant information promptly by minimizing human errors.
List of AI Tools to Streamline Literature Reviews
We have several AI-powered tools to summarize literature reviews. They utilize advanced algorithms and natural language processing techniques to analyze and summarize lengthy scientific articles.
Let's take a look at some of the most popular AI tools to summarize literature reviews.
SciSpace Literature Review
Semantic scholar, paper digest.
SciSpace Literature Review is the best AI tool for summarizing literature review. It is the go-to tool that summarizes articles in seconds. It uses natural language processing models GPT 3.5 and GPT 4.0 to generate concise summaries. It is an effective and efficient AI-powered tool to streamline the literature review process and summarize multiple research articles at once. Once you enter a keyword, research topic, or question, it initiates your literature review process by providing instant insights from the top 5 highly relevant papers at the top.
These insights are backed by citations that allow you to refer to the source. All the resultant relevant papers appear in an easy-to-digest tabular format explaining each of the sections used in the paper in different columns. You can also customize the table by adding or removing the columns according to your research needs. This is the unique feature of this literature review AI tool.
SciSpace Literature review stands out as the best AI tool to summarize literature review by providing concise TL;DR text and summaries for all the sections used in the research paper. This way, it makes the review process easier for any researcher, and could comprehend more research papers in less time.
Try SciSpace Literature Review now!
Semantic Scholar is an AI-powered search engine that helps researchers find relevant research papers based on the keyword or research topic. It works similar to Google Scholar.It helps you discover and understand scientific research by providing suitable research papers. The database has over 200 million research articles, you can filter out the results based on the field of study, author, date of publication, and journals or conferences.
They have recently released the Semantic Reader — an AI-powered tool for scientific readers that enhances the reading process. This is available in the beta version.
Try Semantic Scholar here
Paper Digest — another valuable text summarizer tool (AI-powered tool) that summarizes the literature review and helps you get to the core insights of the research paper in a few minutes! This powerful tool works pretty straightforwardly and generates summaries of research papers. You just need to input the article URL or DOI and click on “Digest” to get the summaries. It comes for free and is currently in the beta version.
You can access Paper Digest here !
SciSummary is another AI tool that summarizes scientific articles and literature review. It uses natural language processing algorithm to generate concise summaries. You need to upload the document on the dashboard or send the article link via email and your summaries will be generated and delivered to your inbox. This is the best AI-powered tool that helps you read and understand lengthy and complicated research papers. It has different pricing plans (both free and premium) which start at $4.99/month, you can choose the plans according to your needs.
You can access SciSummary here
Step-by-Step Guide to Using AI Tools to Summarize Literature Reviews
Here’s a short step-by-step guide that clearly articulates how to use AI tools for summary generation!
- Select the AI-powered tool that best suits your research needs.
- Once you've chosen a tool, you must provide input, such as an article link, DOI, or PDF, to the tool.
- The AI tool will then process the input using its algorithms and techniques, generating a summary of the literature.
- The generated summary will contain the most important information, including key points, methodologies, and conclusions in a succinct format.
- Review and assess the generated summaries to ensure accuracy and relevance.
Challenges of using AI tools for summarization
AI tools are designed to generate precise summaries, however, they may sometimes miss out on important facts or misinterpret specific information.
Here are the potential challenges and risks researchers should be wary of when using AI tools to summarize literature reviews!
1. Lack of contextual intelligence
AI-powered tools cannot ensure that they completely understand the context of the research papers. This leads to inappropriate or misleading summaries of similar academic papers.
To combat this, researchers should feed additional context to the AI prompt or use AI tools with more advanced training models that can better understand the complexities of the research papers.
2. AI tools cannot ensure foolproof summaries
While AI tools can immensely speed up the summarization process, but, they may not be able to capture the complete essence of a research paper or accurately decrypt complex concepts.
Therefore, AI tools are just to be considered as technology aids rather than replacements for human analysis or understanding of key information.
3. Potential bias in the generated summaries
AI-powered tools are largely trained on the existing data, and if the training data is biased, it can eventually lead to biased summaries.
Researchers should be cautious and ensure that the training data is diverse and representative of various sources, different perspectives, and research domains.
4. Quality of the input article affects the summary output
The quality of the research article that we upload or input data also has a direct effect on the accuracy of the generated summaries.
If the input article is poorly written or contains errors, the AI tool might not be able to generate coherent and accurate summaries. Researchers should select high-quality academic papers and articles to obtain reliable and informative summaries.
AI summarization tools have a substantial impact on academic research. By leveraging AI tools, researchers can streamline the literature review process, enabling them to stay up-to-date with the latest advancements in their field of study and make informed decisions based on a comprehensive understanding of current knowledge.
By understanding the role of AI tool to summarize literature review, exploring different AI tools for summarization, following a systematic review process, and assessing the impact of these tools on their academic research, researchers can harness AI tools in enhancing their literature review processes.
If you are also keen to explore the best AI-powered tool for summarizing the literature review process, head over to SciSpace Literature Review and start analyzing the research papers right away — SciSpace Literature Review
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The Current State of Artificial Intelligence in Disaster Recovery: Challenges, Opportunities, and Future Directions
Boe, T. The Current State of Artificial Intelligence in Disaster Recovery: Challenges, Opportunities, and Future Directions. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-23/120, 2023.
The study explored the potential of artificial intelligence (AI) in disaster response and environmental remediation by providing a historical overview, conducting a meta-analysis of related literature, identifying Natural Language Processing (NLP) and image analysis as key research areas, and presenting conceptual examples of AI's potential applications. The study emphasized the need for further research and development while addressing barriers and challenges to ensure ethical deployment and data privacy. The findings highlighted the significant potential of AI to revolutionize various sectors, including disaster response and environmental remediation.
This study explored the potential of artificial intelligence (AI) in disaster response and environmental remediation by conducting a meta-analysis of AI-related literature and providing a historical overview of AI. The study identified Natural Language Processing (NLP) and image analysis as two primary research areas that could greatly benefit from AI integration. Conceptual examples were presented, showcasing the potential of NLP in enhancing communications, code writing, interpreting scientific material, and strategy optimization, while image analysis applications included visualizing training scenarios, debris characterization, and design prototyping. The study also discussed emerging developments and potential impediments that may influence the adoption of AI in the public sector. It emphasized the importance of establishing policy frameworks and guidelines to ensure ethical applications and data privacy. The paper highlighted the significant potential of AI to revolutionize various sectors, particularly disaster response and environmental remediation, while also addressing existing barriers and challenges associated with the deployment and utilization of this technology. The research demonstrated the transformative potential of AI in disaster response and environmental remediation, but also underscored the need for further exploration and development of AI applications. By addressing the challenges and barriers surrounding AI deployment, AI has the potential to bring about revolutionary changes across various sectors, including disaster response and environmental remediation.
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