What is ChatGPT and why does it matter? Here's everything you need to know

Updated: This AI chatbot's advanced conversational capabilities have created quite the buzz. Here's what you need to know.

What is ChatGPT?

ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with a chatbot. The language model can answer questions, and assist you with tasks such as composing emails, essays, and code.

Usage is currently open to public free of charge because ChatGPT is in its research and feedback-collection phase. As of Feb. 1, there is also a paid subscription version called ChatGPT Plus.

Who made ChatGPT?

ChatGPT was created by OpenAI, an AI and research company. The company launched ChatGPT on Nov. 30, 2022. 

OpenAI is also responsible for creating DALLE•2, a popular AI art generator, and Whisper, an automatic speech recognition system. 

How big a deal is ChatGPT?

It's certainly made a big splash. "ChatGPT is scary good. We are not far from dangerously strong AI," said Elon Musk, who was one of the founders of OpenAI before leaving. Sam Altman, OpenAI's chief, said on Twitter that ChatGPT had more than 1 million users in its first five days after launching. Altman told Musk the average cost of each response was in "single-digits cents" but admitted it will need to monetize it eventually because of its "eye-watering" compute costs. 

According to analysis by Swiss bank UBS, ChatGPT is the fastest growing app of all time. In January, only two months after its launch, UBS analysis estimates that ChatGPT had 100 million active users. For comparison, it took nine months for TikTok to reach 100 million. 

How can you access ChatGPT?

You can access ChatGPT simply by visiting chat.openai.com and creating an OpenAI account. 

Once you sign in, you are able to start chatting away with ChatGPT. Get your conversation started by asking a question. Because ChatGPT is still in its research stage, it is free to use and you can ask as many questions as you'd like.

I tried using ChatGPT and it says it's at capacity, what does that mean?

ChatGPT is free to use at the moment because it is still in its research phase. Because of the stir its advanced capabilities have made, a lot of people are flocking to use it. The website operates using a server, and when too many people hop onto the server, it overloads and can't process your request. This doesn't mean you won't ever be able to access it. It just means you should try visiting the site at a later time when fewer people are trying to access it. You can also keep the tab open and just refresh it periodically. 

If you want to skip the wait and have reliable access, there is an option for you. As of Feb. 1, OpenAI has a ChatGPT pro plan, ChatGPT Plus, which allows users to have general access even during peak times, experience faster response times and have priority access to new features and improvements. The premium services does come at a cost of $20/month. 

How are people using ChatGPT?

The model has many functions in addition to answering simple questions, such as composing essays, describing art in great detail, creating AI art prompts, and having philosophical conversations, and it can even code for you.

My personal favorite is asking the chatbot for help creating basic lists such as packing, grocery and to-do lists to make my everyday more productive. The possibilities are endless. 

How does ChatGPT work?

ChatGPT runs on a language model architecture created by OpenAI called the Generative Pre-trained Transformer (GPT). The specific GPT used by ChatGPT is fine-tuned from a model in the GPT-3.5 series, according to OpenAI. Generative AI models of this type are trained on vast amounts of information from the internet including websites, books, news articles and more. 

The language model was fine-tuned using supervised learning as well as reinforcement learning. The use of Reinforcement Learning from Human Feedback (RLHF) is what makes ChatGPT specially unique. Through RLHF, human AI trainers provided the model with conversations in which they played both parts, the user and AI assistants, according to OpenAI. 

What is the difference between ChatGPT and a search engine?

ChatGPT is a language model created with the purpose of holding a conversation with the end user. A search engine indexes web pages on the internet to help the user find the information they asked for. ChatGPT does not have the ability to search the internet for information. It uses the information it learned from training data to generate a response, which leaves room for error. 

Another major difference is that ChatGPT only has access to information up to 2021, whereas a regular search engine, such as Google, has access to the latest information. Therefore, if you ask ChatGPT who won the World Cup in 2022, it wouldn't be able to give a you response and Google would. 

What are ChatGPT's limitations?

Despite looking very impressive, ChatGPT still has limitations. Such limitations include the inability to answer questions that are worded a specific way, as it requires rewording to understand the input question. A bigger limitation is a lack of quality in the responses it delivers -- which can sometimes be plausible-sounding but make no practical sense or can be excessively verbose. 

Instead of asking for clarification on ambiguous questions, the model just takes a guess at what your question means, which can lead to unintended responses to questions. Already this has led developer question-and-answer site StackOverflow to at least temporarily ban ChatGPT-generated responses to questions.

"The primary problem is that while the answers that ChatGPT produces have a high rate of being incorrect, they typically look like they might be good and the answers are very easy to produce," says Stack Overflow moderators in a post. Critics argue that these tools are just very good at putting words into an order that makes sense from a statistical point of view, but they cannot understand the meaning or know whether the statements it makes are correct.

Another major limitation is that ChatGPT's data is limited to 2021. The chatbot does not have an awareness of events or news that have occurred since then. Therefore, some prompts you ask it will render no results such as "Who won the World Cup in 2022?"

Can I chat with ChatGPT?

Although some people are using ChatGPT for some really elaborate functions such as writing code or even malware, you can use ChatGPT for more mundane activities like having a friendly conversation. 

Some conversation starters could be as simple as, "I am hungry, what food should I get?" or as elaborate as, "What do you think happens in the afterlife?" Either way, ChatGPT is sure to have an answer for you. 

Why are some people worried about ChatGPT?

People are expressing concerns about AI chatbots replacing or atrophying human intelligence. For example, the chatbot can write an article on any topic efficiently (though not necessarily accurately) within seconds, potentially eliminating the need for a human writer. The chatbot can also write an entire full essay within seconds, making it easier for students to cheat or avoid learning how to write properly. This has led to some school districts blocking access to it. 

Another concern with the AI chatbot is the possible spread of misinformation. Since the bot is not connected to the internet, it could make mistakes in what information it shares. 

The bot itself says, "My responses are not intended to be taken as fact, and I always encourage people to verify any information they receive from me or any other source." OpenAI itself also notes that ChatGPT sometimes writes "plausible-sounding but incorrect or nonsensical answers."

Does a tool that recognizes ChatGPT text exist?

With concerns of students using ChatGPT to cheat, the need for a ChatGPT text detector is becoming more evident. OpenAI, the AI research company behind ChatGPT, released an imperfect, free tool to target this problem. OpenAI's "classifier" tool can only correctly identifies 26% of AI-written text with a "likely AI-written" designation. Furthermore, it provides false positives 9% of the time, incorrectly identifying human written work as AI written. 

Other AI detectors also exist on the market including GPT-2 Output Detector, Writer AI Content Detector, and Content at Scale AI Content Detection. However, ZDNET put all three of these tools to the test and the results were underwhelming. All three of the a tools were found to be unreliable sources for spotting AI, repeatedly giving false negatives. Here are ZDNET's full test results. 

Is ChatGPT a good or bad thing?

ChatGPT is an advanced chatbot that has the potential to make people's lives easier and to assist with everyday tedious tasks, such as writing an email or having to navigate the web for answers. However, there are certain technical details that have to be figured out before it's widely used, to prevent negative outcomes such as the spread of misinformation. In general, AI and ML models rely on lots of training and fine-tuning to reach a level of ideal performance. 

Does it mean that AI is taking over the world? Not yet, perhaps, but OpenAI's Altman certainly thinks that human-style intelligence in AI is now not that far off. Responding to Musk's comment about dangerously strong AI, Altman tweeted: 

"i agree on being close to dangerously strong AI in the sense of an AI that poses e.g. a huge cybersecurity risk. and i think we could get to real AGI in the next decade, so we have to take the risk of that extremely seriously too."

He also noted: "interesting watching people start to debate whether powerful AI systems should behave in the way users want or their creators intend. the question of whose values we align these systems to will be one of the most important debates society ever has."

Are there alternatives to ChatGPT worth considering?

Although ChatGPT is the chatbot getting all the buzz right now, there are plenty of other options that are just as good and might even be better suited for your needs. Despite ChatGPT's extensive abilities, there are some major downsides to the AI chatbot, including that it's free version is often at capacity. If you want to give the world of AI chatbots and writers a try, there are plenty of other options to consider, including YouChat, Jasper, and Chatsonic.  

Is ChatGPT smart enough to pass an MBA exam?

Simply put, yes. A professor at Wharton, University of Pennsylvania's business school, used ChatGPT to take an MBA exam and the results were quite impressive. 

ChatGPT not only passed the exam, but also scored a whopping B to  B-. The professor, Christian Terwiesch, was impressed at its basic operations management, process analysis questions, and explanations.  

Is ChatGPT coming to social media?

ChatGPT's first appearance in the social media space is its collaboration with Snapchat. On Feb. 7, Snapchat unveiled an integration of ChatGPT which will allow Snapchat+ subscribers to chat with the bot within its app. 

The feature, called "My AI", will include some ChatGPT limitations, including the refusal to provide responses pertaining to politics, violence, swearing, and even academic essay writing, according to The Verge.

What is Microsoft's involvement with ChatGPT?

Microsoft has been an early investor in OpenAI, the AI research company behind ChatGPT, even before ChatGPT was released to the public. Microsoft's first involvement with OpenAI was in 2019, when Microsoft invested $1 billion, and then $2 billion in the years after. In January, Microsoft extended its partnership with OpenAI through a multi-year, multi-billion dollar investment. 

Neither company disclosed the investment value, but sources revealed it will total $10 billion over multiple years, according to Bloomberg. In return, Microsoft's Azure service will be OpenAI's exclusive cloud computing provider, powering all OpenAI workloads across research, products, and API services.

Microsoft has also used its partnership with OpenAI to revamp its own Bing search engine and improve its browser. On Feb. 7, Microsoft unveiled a new Bing search engine which runs on a next-generation OpenAI large language model customized specifically for search. 

Access to Bing's AI powered chatbot is currently limited. However, ZDNET was granted early access and was impressed with the results. 

What does Microsoft's new Bing have to do with ChatGPT?

In early February, Microsoft unveiled a new version of Bing -- and its standout feature is its integration with ChatGPT. The new Bing has a chat feature powered by a next-generation version of OpenAI's large language model, making it "more powerful than ChatGPT," according to Microsoft.

Because Bing's ChatGPT is linked to the internet, the biggest difference from ChatGPT is that Bing's version has information on current events. Another major advantage of the new Bing is that it links back to the sources it pulls information from, leaving less room for misinformation. 

What is Google Bard and how does it relate to ChatGPT?

Bard is Google's AI chat service, a rival to ChatGPT. On Feb. 6, Google introduced its experimental AI chat service, which will be tested by a select number of users before rolling out to the general public in the following weeks.  

Bard will be using Google's Language Model for Dialogue Applications (LaMDA) and will draw on all the information from the web to provide responses -- a stark contrast from ChatGPT, which does not have internet access. 

Google's chat service has had a rough launch, with a demo of Bard delivering inaccurate information about the James Webb Space Telescope (JWST). 

Google told ZDNET reporter Stephanie Condon, "This highlights the importance of a rigorous testing process, something that we're kicking off this week with our Trusted Tester program," in a statement. 

Will ChatGPT become a paid service anytime soon?

As of Feb.1, ChatGPT offers a paid subscription model called ChatGPT Plus. With the chatbots skyrocketing popularity, it was only a matter of time before OpenAI rolled out a paid subscription model. 

The pro plan gives users general access even during peak times when the free version is at capacity, which lately, has been pretty often. Users will also experience faster response times and have priority access to new features and improvements. The premium services does come at a cost of $20/month. 

The launch of a paid version had been rumored for some time before the official release. In January, OpenAI announced on its Discord server that it was considering to start charging for ChatGPT with a version called ChatGPT Professional. 

OpenAI uploaded a waitlist for access to the pro service on Discord. The waitlist outlined that the new service would provide users with service that is always available (no blackout windows), fast responses from ChatGPT, and unlimited messages. While the cost for this service has not been announced, one of the survey questions on the waitlist form asks what price per month the user would consider paying for ChatGPT Pro. 

An early access user, shared a video on Twitter running a test prompt on the pro version that ran much quicker than the free version the rest of the public has access to. In the video, Khawaja showed his subscription cost, was $42/month. Now we know the actual cost OpenAI set for this plan is a little more than half less of the early access user's cost. 

How does ChatGPT work?

( "We take a deep dive into the inner workings of the wildly popular AI chatbot, ChatGPT. If you want to know how its generative AI magic happens,"

Written by David Gewirtz, Senior Contributing Editor on March 10, 2023

Reviewed by Alyson Windsor )

Google, Wolfram Alpha, and ChatGPT all interact with users via a single line text entry field and provide text results. Google returns search results, a list of web pages and articles that will (hopefully) provide information related to the search queries. Wolfram Alpha generally provides mathematically and data analysis-related answers.

ChatGPT, by contrast, provides a response based on the context and intent behind a user's question. You can't, for example, ask Google to write a story or ask Wolfram Alpha to write a code module, but ChatGPT can do these sorts of things.

Fundamentally, Google's power is the ability to do enormous database lookups and provide a series of matches. Wolfram Alpha's power is the ability to parse data-related questions and perform calculations based on those questions. ChatGPT's power is the ability to parse queries and produce fully-fleshed out answers and results based on most of the world's digitally-accessible text-based information -- at least information that existed as of its time of training prior to 2021.

In this article, we'll look at how ChatGPT can produce those fully-fleshed out answers. We'll start by looking at the main phases of ChatGPT operation, then cover some of the core AI architecture components that make it all work.

In addition to the sources cited in this article (many of which are the original research papers behind each of the technologies), I used ChatGPT itself to help me create this backgrounder. I asked it a lot of questions. Some answers are paraphrased within the overall context of this discussion.

The two main phases of ChatGPT operation

Let's use Google as an analogy again. When you ask Google to look up something, you probably know that it doesn't -- at the moment you ask -- go out and scour the entire web for answers. Instead, Google searches its database for pages that match that request. Google effectively has two main phases: the spidering and data gathering phase, and the user interaction/lookup phase.

Roughly speaking, ChatGPT works the same way. The data gathering phase is called pre-training, while the user responsiveness phase is called inference. The magic behind generative AI and the reason it's suddenly exploded is that the way pre-training works has suddenly proven to be enormously scalable.

Pre-training the AI

Generally speaking (because to get into specifics would take volumes), AIs pre-train using two principle approaches: supervised and non-supervised. For most AI projects up until the current crop of generative AI systems like ChatGPT, the supervised approach was used.

Supervised pre-training is a process where a model is trained on a labeled dataset, where each input is associated with a corresponding output.

For example, an AI could be trained on a dataset of customer service conversations, where the user's questions and complaints are labeled with the appropriate responses from the customer service representative. To train the AI, questions like "How can I reset my password?" would be provided as user input, and answers like "You can reset your password by visiting the account settings page on our website and following the prompts." would be provided as output.

In a supervised training approach, the overall model is trained to learn a mapping function that can map inputs to outputs accurately. This process is often used in supervised learning tasks, such as classification, regression, and sequence labeling.

As you might imagine, there are limits to how this can scale. Human trainers would have to go pretty far in anticipating all the inputs and outputs. Training could take a very long time and be limited in subject matter expertise.

But as we've come to know, ChatGPT has very few limits in subject matter expertise. You can ask it to write a resume for the character Chief Miles O'Brien from Star Trek, have it explain quantum physics, write a piece of code, write a short piece of fiction, and compare the governing styles of former presidents of the United States.

It would be impossible to anticipate all the questions that would ever be asked, so there really is no way that ChatGPT could have been trained with a supervised model. Instead, ChatGPT uses non-supervised pre-training -- and this is the game changer.

Non-supervised pre-training is the process by which a model is trained on data where no specific output is associated with each input. Instead, the model is trained to learn the underlying structure and patterns in the input data without any specific task in mind. This process is often used in unsupervised learning tasks, such as clustering, anomaly detection, and dimensionality reduction. In the context of language modeling, non-supervised pre-training can be used to train a model to understand the syntax and semantics of natural language, so that it can generate coherent and meaningful text in a conversational context.

It's here where ChatGPT's apparently limitless knowledge becomes possible. Because the developers don't need to know the outputs that come from the inputs, all they have to do is dump more and more information into the ChatGPT pre-training mechanism, which is called transformer-base language modeling.

Transformer architecture

The transformer architecture is a type of neural network that is used for processing natural language data. A neural network simulates the way a human brain works by processing information through layers of interconnected nodes. Think of a neural network like a hockey team: each player has a role, but they pass the puck back and forth among players with specific roles, all working together to score the goal.

The transformer architecture processes sequences of words by using "self-attention" to weigh the importance of different words in a sequence when making predictions. Self-attention is similar to the way a reader might look back at a previous sentence or paragraph for the context needed to understand a new word in a book. The transformer looks at all the words in a sequence to understand the context and the relationships between the words.

Also: I asked ChatGPT to write a WordPress plugin I needed. It did it in less than 5 minutes

The transformer is made up of several layers, each with multiple sub-layers. The two main sub-layers are the self-attention layer and the feedforward layer. The self-attention layer computes the importance of each word in the sequence, while the feedforward layer applies non-linear transformations to the input data. These layers help the transformer learn and understand the relationships between the words in a sequence.

During training, the transformer is given input data, such as a sentence, and is asked to make a prediction based on that input. The model is updated based on how well its prediction matches the actual output. Through this process, the transformer learns to understand the context and relationships between words in a sequence, making it a powerful tool for natural language processing tasks such as language translation and text generation.

Let's discuss the data that gets fed into ChatGPT first, and then take a look at the user-interaction phase of ChatGPT and natural language.

ChatGPT's training datasets

The dataset used to train ChatGPT is huge. ChatGPT is based on the GPT-3 (Generative Pre-trained Transformer 3) architecture. Now, the abbreviation GPT makes sense, doesn't it? It's generative, meaning it generates results, it's pre-trained, meaning it's based on all this data it ingests, and it uses the transformer architecture that weighs text inputs to understand context.

GPT-3 was trained on a dataset called WebText2, a library of over 45 terabytes of text data. When you can buy a 16 terabyte hard drive for under $300, a 45 terabyte corpus may not seem that large. But text takes up a lot less storage space than pictures or video.

This massive amount of data allowed ChatGPT to learn patterns and relationships between words and phrases in natural language at an unprecedented scale, which is one of the reasons why it is so effective at generating coherent and contextually relevant responses to user queries.

While ChatGPT is based on the GPT-3 architecture, it has been fine-tuned on a different dataset and optimized for conversational use cases. This allows it to provide a more personalized and engaging experience for users who interact with it through a chat interface.

For example, OpenAI (developers of ChatGPT) has released a dataset called Persona-Chat that is specifically designed for training conversational AI models like ChatGPT. This dataset consists of over 160,000 dialogues between two human participants, with each participant assigned a unique persona that describes their background, interests, and personality. This allows ChatGPT to learn how to generate responses that are personalized and relevant to the specific context of the conversation.

Also: How to save a ChatGPT conversation to revisit later

In addition to Persona-Chat, there are many other conversational datasets that were used to fine-tune ChatGPT. Here are a few examples:

Cornell Movie Dialogs Corpus: a dataset containing conversations between characters in movie scripts. It includes over 200,000 conversational exchanges between more than 10,000 movie character pairs, covering a diverse range of topics and genres.

Ubuntu Dialogue Corpus: a collection of multi-turn dialogues between users seeking technical support and the Ubuntu community support team. It contains over 1 million dialogues, making it one of the largest publicly available datasets for research on dialog systems.

DailyDialog: a collection of human-to-human dialogues in a variety of topics, ranging from daily life conversations to discussions about social issues. Each dialogue in the dataset consists of several turns, and is labeled with a set of emotion, sentiment, and topic information.

In addition to these datasets, ChatGPT was trained on a large amount of unstructured data found on the internet, including websites, books, and other text sources. This allowed ChatGPT to learn about the structure and patterns of language in a more general sense, which could then be fine-tuned for specific applications like dialogue management or sentiment analysis.

ChatGPT is a distinct model that was trained using a similar approach as the GPT series, but with some differences in architecture and training data. ChatGPT has 1.5 billion parameters, which is smaller than GPT-3's 175 billion parameters.

Overall, the training data used to fine-tune ChatGPT is typically conversational in nature and specifically curated to include dialogues between humans, which allows ChatGPT to learn how to generate natural and engaging responses in a conversational format.

Think of ChatGPT's unsupervised training this way: It was fed a lot of data and left to its own devices to find patterns and make sense of it all. This is the mechanism that allowed the new generative AI systems to scale up so quickly.

While the heavy lifting for ChatGPT's generative AI is being done by the pre-training, it also has to be able to understand questions and construct answers from all that data. That's done by the inference phase which consists of natural language processing and dialog management.

What about human involvement in pre-training?

Despite the inherent scalability of non-supervised pre-training, there is some evidence that human assistance may have been involved in the preparation of ChatGPT for public use.

The big reveal was in an article in TIME Magazine that discussed the use of human "data labelers" earning between $1.32 and $2/hour in Kenya. According to the TIME report, it was the responsibility of these workers to scan horrifying and sexually explicit Internet content to flag it for ChatGPT training.

Another article, posted in Martechpost, an AI newsletter, states that the large language model was trained using a process called Reinforcement Learning from Human Feedback (RLHF). According to the article, "The training process involved a fine-tuned initial model using supervised learning, with human trainers playing both the role of the user and an AI assistant."

There are some subtleties here, when it comes to what the word "training" means. According to ChatGPT itself, "OpenAI did not use reinforcement learning with human feedback to train me. Instead, I was pre-trained using a combination of unsupervised and supervised learning techniques, such as language modeling, auto-encoding, and sequence prediction. My training involved processing massive amounts of text data from the internet, which allowed me to learn patterns and relationships between words and phrases."

The AI continued, "However, once I was pre-trained, researchers and developers can use reinforcement learning with human feedback to fine-tune me for specific tasks or domains, such as answering questions or generating text. In these cases, humans can provide feedback in the form of rewards or penalties, which can be used to update my parameters and improve my performance on similar tasks in the future."

This seems to fit with the Marktechpost and TIME reports, in that the initial pre-training was non-supervised, allowing for a tremendous amount of data to be fed into the system. But in building the dialog responses that communicate with users (more on that below), the response engines were apparently trained both on the types of responses, and trained to filter out inappropriate material -- and that training seems to have been human assisted.

I reached out to OpenAI (the maker of ChatGPT) for clarification, but haven't yet gotten a response. If the company gets back to me (outside of ChatGPT itself), I'll update the article with its answer.

Natural language processing

Natural language processing (NLP) focuses on enabling computers to understand, interpret, and generate human language. With the exponential growth of digital data and the increasing use of natural language interfaces, NLP has become a crucial technology for many businesses.

NLP technologies can be used for a wide range of applications, including sentiment analysis, chatbots, speech recognition, and translation. By leveraging NLP, businesses can automate tasks, improve customer service, and gain valuable insights from customer feedback and social media posts.

One of the key challenges in implementing NLP is dealing with the complexity and ambiguity of human language. NLP algorithms need to be trained on large amounts of data in order to recognize patterns and learn the nuances of language. They also need to be continually refined and updated to keep up with changes in language use and context.

The technology works by breaking down language inputs, such as sentences or paragraphs, into smaller components and analyzing their meanings and relationships to generate insights or responses. NLP technologies use a combination of techniques, including statistical modeling, machine learning, and deep learning, to recognize patterns and learn from large amounts of data in order to accurately interpret and generate language.

Dialogue management

You may have noticed that ChatGPT can ask follow-up questions to clarify your intent or better understand your needs, and provide personalized responses that take into account the entire conversation history.

This is how ChatGPT can have multi-turn conversations with users in a way that feels natural and engaging. It involves using algorithms and machine learning techniques to understand the context of a conversation and maintain it over multiple exchanges with the user.

Dialogue management is an important aspect of natural language processing because it allows computer programs to interact with people in a way that feels more like a conversation than a series of one-off interactions. This can help to build trust and engagement with users, and ultimately lead to better outcomes for both the user and the organization using the program.

Marketers, of course, want to expand how trust is built up, but this is also an area that could prove scary because it's one way that an AI might be able to manipulate the people who use it.

And now you know

Even though we're pushing 2,500 words, this is still a very rudimentary overview of all that goes on inside of ChatGPT. That said, perhaps now you understand a bit more about why this technology has exploded over the past few months. The key to it all is that the data itself isn't "supervised," and the AI is able to take what it's been fed and make sense of it.

Pretty awesome, really.

To wrap up, I fed a draft of this entire article to ChatGPT and asked the AI to describe the article in one sentence. Here you go:

ChatGPT is like Google and Wolfram Alpha's brainy cousin who can do things they can't, like write stories and code modules.

ChatGPT is supposed to be a technology without an ego, but if that answer doesn't just slightly give you the creeps, you haven't been paying attention.

The best AI chatbot overall

Features:

• Uses OpenAI's GPT-3
• Can do text generation, solve math problems, and does coding
• Offers conversation capabilities
• Price: Completely free to the public right now

ChatGPT is a conversational AI chatbot that is able to produce text for you based on any prompt you input, generating emails, essays, poems, raps, grocery lists, letters and much more. 

In addition to writing for you, it can chat with you about simple or complex topics such as "What are colors?" or "What is the meaning of life?" ChatGPT is also proficient in STEM and can write and debug code, and even solve complex math equations. The best part is that the service is completely free to the public right now because it is still in its research and feedback-collection phase. 

The big downside is that the chatbot is often at capacity due to its immense popularity. However, OpenAI is in talks of releasing a professional version, which would be quicker and always accessible, at a monthly cost. 

Microsoft's Bing ChatGPT

JASPER