What is ChatGPT?

by Stephen M. Walker II, Co-Founder / CEO

ChatGPT is a large language model-based chatbot developed by OpenAI, launched on November 30, 2022. It uses natural language processing to create human-like conversational dialogue and can respond to questions and compose various written content, including articles, social media posts, essays, code, and emails.

ChatGPT is a form of generative AI, which means it can generate human-like images, text, or videos based on user prompts. It's trained with reinforcement learning through human feedback and reward models that rank the best responses, helping to improve future responses.

The versatility of ChatGPT is one of its key features. It can write and debug computer programs, compose music, draft emails, summarize articles, and even solve math problems. It can also be used to create intelligent chatbots for customer service, sales, or support, producing human-like responses.

Despite its impressive capabilities, it's important to note that ChatGPT doesn't "think" the way humans do. It uses large amounts of data and computing techniques to make predictions about stringing words together in a meaningful way.

ChatGPT has been praised for its unprecedented capabilities, with Kevin Roose of The New York Times calling it "the best artificial intelligence chatbot ever released to the general public". However, it has also been criticized for engaging in biased or discriminatory behaviors.

ChatGPT was initially free to the public, but OpenAI had plans to monetize the service later. By December 4, 2022, ChatGPT had over one million users. GPT-4, which was released on March 14, 2023, was made available via API and for premium ChatGPT users.

To access ChatGPT, you need to create an OpenAI account. After signing up, you can type a prompt or question in the message box on the ChatGPT homepage.

What is ChatGPT?

ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI that uses natural language processing to create humanlike conversational dialogue. It's a sibling model to InstructGPT and is trained to follow an instruction in a prompt and provide a detailed response. The model is trained using Reinforcement Learning from Human Feedback (RLHF), with an initial model trained using supervised fine-tuning. AI trainers provided conversations in which they played both sides—the user and an AI assistant.

Examples, capabilities, and limitations of ChatGPT

ChatGPT, while a fun tool for generating creative content like Shakespearean sonnets or brainstorming marketing email subject lines, also plays a crucial role in data collection for OpenAI. This data collection aspect led to a temporary block of ChatGPT in Italy in early 2023, but the issues have since been addressed and resolved.

Currently, ChatGPT provides access to two versions of the GPT model. The standard GPT-3.5 model is available for free to all users, while the more powerful GPT-4 model is exclusive to ChatGPT Plus subscribers, who are allotted a limited number of interactions—currently 25 messages every three hours, although this is subject to change.

A key feature of ChatGPT is its conversational memory, which allows it to retain context from previous exchanges. This contextual awareness enables it to provide more informed responses and maintain a coherent dialogue, allowing for requests for revisions or further elaboration based on earlier parts of the conversation.

To experience ChatGPT's capabilities firsthand, you can interact with it for free. This will give you a better understanding of its functionality before diving into the technical details of how it operates.

How does ChatGPT work?

ChatGPT is a generative AI language model developed by OpenAI that uses deep learning to generate human-like text. It is based on the transformer architecture, a type of neural network that has been successful in various natural language processing (NLP) tasks. ChatGPT is fine-tuned from GPT-3.5 and optimized for dialogue using Reinforcement Learning with Human Feedback (RLHF), a method that uses human demonstrations and preference comparisons to guide the model toward desired behavior.

The training process of ChatGPT involves two main phases: pre-training and fine-tuning. In the pre-training phase, the model is trained on a massive corpus of text data to learn patterns and generate predictions. In the fine-tuning phase, the model is further optimized using RLHF, which involves collecting human-written demonstrations and human-labeled comparisons between model outputs. A reward model is then trained on this dataset to predict which output the labelers would prefer. The model is fine-tuned using the Proximal Policy Optimization (PPO) algorithm to maximize the reward.

Despite its impressive capabilities, ChatGPT has limitations, such as occasionally producing incorrect or nonsensical answers. It relies on the data it was trained on, which means it might not always have information on very recent topics or niche subjects.

Transformer architecture

ChatGPT's training on a deep learning neural network, which mimics the human brain's structure, enables it to recognize patterns in text and generate human-like responses. The key to this capability is the transformer architecture, introduced in 2017, which revolutionized AI model design by allowing computations to occur simultaneously, thus speeding up training and reducing costs.

Unlike older models that processed text sequentially, transformers analyze all words in a sentence concurrently, using a mechanism called "self-attention" to weigh the importance of each word relative to others. This approach enables the model to focus on the most relevant parts of the text, regardless of their position in the sentence, and to do so efficiently on modern hardware.

In practice, transformers don't process words directly but instead work with "tokens," which are pieces of text represented as vectors—quantities that have magnitude and direction in space. The spatial proximity of these token-vectors indicates their relatedness. Attention mechanisms, also vector-encoded, help the model retain critical information throughout a paragraph.

While the intricacies of transformer math are complex, they underpin the model's ability to understand and generate language with a nuanced grasp of context and relevance.

Tokens

Understanding how AI models comprehend text is crucial. Tokens are the building blocks for this process. GPT-3, for instance, was trained on approximately 500 billion tokens, which are on average about four characters long. These tokens help the model predict the most likely follow-on text by mapping them in a multidimensional vector space. While many words correspond to a single token, complex words can be segmented into multiple tokens.

The tokens originate from a vast array of human-generated content, including books, articles, and a significant amount of data from the internet. This diverse corpus enables the model to simulate human-like text generation.

GPT-3 operates with 175 billion parameters, which influence how it processes an input prompt and determines the most appropriate response, factoring in variable weightings and a degree of randomness. While OpenAI has not disclosed the number of parameters for GPT-4, it is speculated to be more than GPT-3 but fewer than 100 trillion. The enhanced capabilities of GPT-4 are likely due not only to an increased parameter count but also to refinements in its training methodology and the Mixture of Experts architecture.

Supervised fine-tuning

GPT, which stands for "Generative Pre-trained Transformer," leverages a pre-training phase that sets it apart from earlier AI models that relied on supervised learning. Supervised learning models required extensive, manually-labeled datasets, which are costly and limited in scope. In contrast, GPT's pre-training involves processing massive amounts of unlabeled text data from the internet, allowing it to infer patterns and relationships within language without explicit guidance.

To ensure GPT's outputs are predictable and appropriate, it undergoes a fine-tuning process after pre-training. This process incorporates elements of supervised learning, using smaller, curated datasets to refine its responses and align them with desired outcomes.

Reinforcement learning from human feedback (RLHF)

To ensure ChatGPT's responses were safe, sensible, and coherent for public interaction, OpenAI employed a technique known as reinforcement learning with human feedback (RLHF). This process involved training the model with demonstration data to illustrate appropriate responses and using a reward model based on comparisons of multiple responses ranked by human evaluators. This method, while not purely supervised learning, effectively fine-tunes the AI to generate the most suitable replies in various contexts.

Natural language processing (NLP)

Natural language processing (NLP) is a broad field within artificial intelligence that includes speech recognition, machine translation, and chatbots. It involves teaching AI to understand language rules and syntax, developing algorithms to represent these rules, and using them to perform specific tasks like responding to user prompts.

ChatGPT, powered by GPT's transformer-based neural network, goes beyond simple predictive text. It generates coherent responses to prompts by breaking down the input into tokens, determining the most relevant parts, and then producing a sequence of tokens that form a suitable reply. This process is based on patterns learned during its extensive training and fine-tuning phases.

For instance, when prompted with "Klu is…", ChatGPT might respond with "Klu is an all-in-one platform designed to help AI engineers and teams design, deploy, and optimize applications powered by large language models (LLMs)." Variations in the response, such as "Klu provides a suite of tools that enable rapid iteration based on model, prompt, and user insights, facilitating the creation of personalized and efficient AI-powered software products" occur due to the model's design to introduce randomness and handle subtle differences in queries. This randomness, adjustable via a "temperature" setting in some GPT applications, ensures diverse yet accurate responses.

ChatGPT's understanding of natural language enables it to discern the nuances between prompts like "What is Klu?" and "What does Klu.ai do?", providing slightly different but fundamentally similar answers. The model avoids nonsensical replies, instead varying its output based on the likelihood of different follow-on texts. Even when upgraded to GPT-4, the essence of ChatGPT's responses remains consistent, demonstrating its robust grasp of NLP.

What is the difference between ChatGPT and other Generative AI models?

Generative AI and ChatGPT are both related to the generation of content, but they have distinct focuses and applications.

Generative AI is a broad field of artificial intelligence that encompasses techniques and models capable of generating new content, such as images, music, text, and more. The underlying principle of generative AI is to learn patterns from existing data and use that knowledge to generate original content that aligns with the learned patterns. It enables AI systems to unleash their creativity and produce novel outputs based on the data they were trained on. Generative AI has found applications in diverse areas such as art, design, and content creation.

ChatGPT, on the other hand, is a specific implementation of generative AI designed explicitly for conversational purposes. It is a language model trained on extensive amounts of text data, enabling it to generate human-like responses to user prompts. ChatGPT has been fine-tuned to engage in dialogue and simulate conversation effectively. Its primary objective is to create interactive and realistic conversations with users, making it a powerful tool for chatbots, virtual assistants, and customer support applications.

The main distinction between generative AI and ChatGPT lies in their respective applications and focuses. Generative AI aims to generate new content by learning patterns from existing data, enabling AI systems to exhibit creativity and produce original outputs. It encompasses a wide range of techniques and models used across various domains. On the other hand, ChatGPT is a specific implementation of generative AI that excels in conversational interactions. It has been extensively trained on text data and optimized for generating realistic responses in dialogue settings.

ChatGPT is based on a GPT model, which is a type of generative model. It's considered a generative model because it can generate new text. However, it's different from a traditional generative model like naive bayes, where class distributions are inferred.

ChatGPT stands for “Chat Generative Pre-Trained Transformer”, and it's a generative AI language model that acts in a conversational way. You can ask it questions and get human-like answers. It is developed by OpenAI. You can use it to do all sorts of things, such as explaining complex concepts in a simple, easy-to-understand way, writing a paragraph explaining the history of a certain topic, optimizing sections of your code, or generating entirely new code, and even writing a haiku.

ChatGPT has been trained on a massive dataset which includes the whole of Wikipedia, scholarly articles, research papers, news articles, books, and documentation. While there is a lot of hype about ChatGPT being a “Google Killer,” since ChatGPT does not do an internet search to provide information, it cannot currently replace Google.

While there is a general consensus among experts that ChatGPT is not an example of human-level intelligence (also known as “artificial general intelligence” or “strong AI” — it doesn't quite fit into the definition of traditional AI products (“narrow artificial intelligence” or “weak AI.” Instead, it falls into a murky category between them.

Words, not Knowledge

While AI like ChatGPT are often described as "understanding" language, it's more accurate to say they have a detailed map of how concepts are related. They don't truly comprehend English in the way humans do. OpenAI acknowledges that ChatGPT can generate incorrect or harmful information and is actively working to improve its accuracy.

For instance, when prompted to describe "Andrew Huberman the scientist," GPT-3.5 and GPT-4 provided different responses. GPT-3.5's answer included some correct information mixed with inaccuracies, likely derived from common associations in its training data rather than specific knowledge about me. It mentioned publications I've never written for, confusing them with others I have, such as mistaking Popular Science for Popular Mechanics.

GPT-4, on the other hand, correctly identified my work and the publications I've contributed to, showcasing OpenAI's progress in enhancing GPT-4's accuracy. However, its response still seemed to construct a plausible bio without genuine insight into my reputation, indicating that while it's more sophisticated than GPT-3, it operates on a similar principle of predicting text sequences.

The improvements from GPT-3.5 to GPT-4 are notable, and as GPT-4 is currently only available through a premium subscription, most publicly seen ChatGPT content is generated by GPT-3.5. This is likely to evolve, and the future advancements with GPT-5 and beyond are highly anticipated.

Capabilities

ChatGPT, developed by OpenAI, is a GPT-4-based natural language processing tool that allows users to have human-like conversations with an AI chatbot. It's capable of a wide range of tasks, including:

  1. Answering Questions — Users can ask all kinds of questions to this AI tool to get straightforward and uncluttered responses. It can be used as an encyclopedia, for instance, to define Newton's laws of motion or to write a poem.

  2. Content Creation — ChatGPT can process code, write code, and help developers debug codes. For instance, it can be used to generate SQL queries.

  3. Data Management and Manipulation — ChatGPT can convert unstructured data into a structured format by manipulating data. For instance, the tool can be used to add data to a table, make indexes, and understand JSON queries.

  4. Tutoring — ChatGPT can explain words, code, and even physics. As the AI tutor capabilities of ChatGPT develop and become more refined, it can dramatically alter the way students interact with the outside world.

  5. Language Translation and Text Summarization — ChatGPT is particularly well-suited for tasks such as language translation, text summarisation, and conversation generation.

  6. Voice and Image Capabilities — OpenAI has expanded the capabilities of ChatGPT by introducing voice and image features. These new additions provide users with a more intuitive and interactive experience by allowing voice conversations and image inputs.

However, ChatGPT also has some limitations. It may provide wrong answers as it is a large language model that is continuously being trained to increase the accuracy of responses. It also has limitations in training data and bias issues. ChatGPT's training data cuts off in 2021, which means it is completely unaware of current events, trends, or anything that happened after its training. It cannot verify facts, provide references, or perform calculations or translations. It can only generate responses based on its own internal knowledge and logic. Furthermore, it can make computational and logic errors as it is a language model, not a calculator.

Despite these limitations, ChatGPT is a powerful AI tool that can be used for a variety of purposes and marks an exciting time in the technological landscape.

Use Cases

ChatGPT has various applications across different industries, including:

  1. Customer service: Create intelligent chatbots for customer support, sales, or as personal virtual assistants.
  2. Code writing and debugging: Generate code for simple tasks and assist in code completion.
  3. Content creation: Generate content for blogs, marketing materials, and social media.
  4. Translation: Translate text between languages and assist in language learning.
  5. Education: Help teachers develop lesson plans, activities, and projects, and assist students with research and academic writing.
  6. Marketing: Generate personalized marketing messages, ad copy, and email campaigns.
  7. SEO: Generate topic ideas and optimize content with relevant keywords.
  8. Human Resources: Create onboarding materials, job descriptions, and answer employee questions.
  9. Healthcare: Assist in diagnosing and treating patients, and provide health advice.
  10. Finance: Automate financial analysis, investment advice, and customer support.
  11. Legal services: Automate contract drafting, review, and legal research.
  12. Mental health: Provide support in interpersonal communication and social skills training.

These are just a few examples of how ChatGPT can be utilized across various sectors. It's important to note that while ChatGPT can generate impressive responses, it's still a machine and may not always provide accurate or reliable information.

What is ChatGPT Enterprise?

ChatGPT Enterprise is a business-focused version of OpenAI's ChatGPT, offering enterprise-grade security, privacy, unlimited higher-speed GPT-4 access, longer context windows for processing longer inputs, advanced data analysis capabilities, customization options, and more. It is designed to help businesses improve efficiency, productivity, and customer satisfaction while reducing costs. Some key features of ChatGPT Enterprise include:

  1. Enterprise-grade security and privacy: Customer prompts and company data are not used for training OpenAI models, and data encryption is provided at rest (AES 256) and in transit (TLS 1.2+).
  2. Management features: ChatGPT Enterprise has an admin console for bulk user management, single sign-on (SSO), domain verification, and an analytics dashboard for usage insights.
  3. Improved performance: ChatGPT Enterprise offers unlimited access to GPT-4 with no usage caps and performs up to 2x faster than the standard GPT-4.
  4. Advanced data analysis: ChatGPT Enterprise provides unlimited access to advanced data analysis, formerly known as Code Interpreter.
  5. Longer context windows: ChatGPT Enterprise supports 32k token context windows for 4x longer inputs, files, or follow-ups.
  6. Shareable chat templates: ChatGPT Enterprise allows users to create shareable chat templates for internal collaboration and building common workflows.

OpenAI's ChatGPT Enterprise pricing is not publicly listed. Instead, OpenAI has adopted a fluid pricing plan that is personalized based on the specific needs and requirements of each business. However, a Reddit user reported being quoted $60 per user per month, with a minimum of 150 seats for a 12-month contract.

ChatGPT Enterprise offers several features that are not available in the lower-tier plans. These include enterprise-grade security and privacy, unlimited high-speed access to GPT-4, longer input context windows (32k tokens), advanced data analysis capabilities, and customization options. It also includes API credits to build your own solutions, and the user data is not used for model training.

Limitations and Challenges

Despite its capabilities, ChatGPT has some limitations. It sometimes writes plausible-sounding but incorrect or nonsensical answers, and it is sensitive to tweaks to the input phrasing or attempting the same prompt multiple times. In 2023, researchers found that the performance levels of ChatGPT have degraded over several months, with significant performance changes for the worse in tasks such as solving math problems, answering sensitive questions, code generation, and visual reasoning. Furthermore, its responses can sometimes sound like a machine and unnatural, and it summarizes but does not cite sources.

Future Developments

OpenAI is committed to improving ChatGPT and plans to make regular model updates to address its limitations. They also introduced an enterprise version of ChatGPT in August 2023, offering the higher-speed GPT-4 model with longer context windows, customization options, and data analysis. OpenAI is also excited to carry the lessons from this release into the deployment of more capable systems.

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