Open AI and ChatGPT

- Overview

ChatGPT is an artificial intelligence chatbot that uses natural language processing to generate human-like text. It is developed by OpenAI and launched in November 2022. ChatGPT can answer questions, compose written content, and help with tasks like writing emails, papers, and code. It can also generate responses in real time based on user input.

ChatGPT uses GPT-4, a large language model that leverages deep learning to generate human-like text. It can generate text based on context and past conversations. It can also guide the conversation to the desired length, format, style, level of detail and language used.

ChatGPT is available on mobile devices via a browser such as Google Chrome or the official ChatGPT app for iOS or Android. It's free to use and download. 

- OpenAI and ChatGPT

OpenAI is not the same as ChatGPT. OpenAI is a company that develops AI models, and ChatGPT is a specific model created by OpenAI.

OpenAI is a private research company that develops artificial intelligence (AI). The company was founded in 2015 by Elon Musk and Sam Altman, and is headquartered in San Francisco.

OpenAI's mission is to ensure that AI benefits all of humanity. The company's goal is to develop "safe and beneficial" artificial general intelligence, which it defines as "highly autonomous systems that outperform humans at most economically valuable work".

OpenAI's research focuses on generative models and how to align them with human values. The company also has an in-house initiative called the superalignment team, which is dedicated to preventing a superintelligence from going rogue.

OpenAI is a privately held company and is not publicly traded on NYSE or NASDAQ in the U.S.

ChatGPT is an AI chatbot that uses natural language processing to create human-like conversational dialogue. It can respond to questions and compose various written content, including articles, social media posts, essays, code, and emails.

ChatGPT is primarily designed to generate human-like responses to text input. It can have conversations on topics from history to philosophy, generate lyrics in the style of Taylor Swift or Billy Joel, and suggest edits to computer programming code.

ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.

- ChatGPT Technology

ChatGPT, an AI-powered large language model developed by OpenAI. Users are using ChatGPT for everything from improving productivity to helping find diagnoses for medical challenges. As Google has become the way people describe searching the Internet, ChatGPT has quickly turned into the shorthand for how people use AI.

ChatGPT is a chatbot that takes human language, puts it into an AI system, and transforms large amounts of information into clear, organized, and nearly human-sounding responses.

ChatGPT, which stands for Chat Generative Pre-trained Transformer, is a large language model–based chatbot developed by OpenAI and launched on November 30, 2022, which enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language used.

ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response. ChatGPT is built on top of OpenAI's GPT-3 family of large language models and has been fine-tuned (an approach to transfer learning) using both supervised and reinforcement learning techniques.

ChatGPT is an extrapolation of a class of machine learning natural language processing models known as large language models (LLMs). LLMs ingest large amounts of text data and infer relationships between words in the text. These models have evolved over the past few years as we have seen advances in computing power. As the size of the input dataset and parameter space increases, so does the power of the LLM.

- Training

ChatGPT — a generative pretrained transformer (GPT) — is fine-tuned on top of GPT-3.5 using supervised learning and reinforcement learning (a transfer learning method). Both methods use human trainers to improve the performance of the model. 

In the case of supervised learning, the model provides a dialogue in which the trainer plays the roles of two parties: the user and the AI assistant. In the reinforcement learning step, the human trainer first ranks the responses the model has created in previous conversations. 

These rankings are used to create a "reward model" that is further fine-tuned using multiple iterations of proximal policy optimization (PPO). Proximal policy optimization algorithms bring cost benefits to trust domain policy optimization algorithms; they negate many computationally expensive operations with faster performance. The models are trained in partnership with Microsoft on its Azure supercomputing infrastructure. 

Additionally, OpenAI continues to collect data from ChatGPT users, which can be used to further train and fine-tune ChatGPT. Allows users to upvote or downvote responses they receive from ChatGPT; when voting upvotes or downvotes, they can also fill in a text field with additional feedback.

The most basic training of a language model involves predicting words in sequences of words. Most commonly, this is observed for next-token prediction and masked language modeling.

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- next-token prediction

Next-token prediction is a self-supervised objective that allows language modeling (LLM) to train models to predict the next token in a text sequence. For example, if the input is "The sky is", the next token predictor might predict "blue" as the most likely word or symbol to follow.

To perform next-token prediction, the model takes the output vector of each token and uses it to predict the next token in the sequence. The model then outputs a probability distribution over the vocabulary predicted for each next token.

Next-token prediction is a common pre-training goal for causal language models. It is heavily used for pre-training and fine-tuning.

According to Redwood Research, language models are much better than humans at predicting the next-token.

- masked language modeling

Masked language model (MLM) is a type of language model used in natural language processing (NLP) tasks. MLMs are trained to predict masked words or tokens in a given input sequence based on the context provided by surrounding words.

Masked Language Modelling (MLM) is a deep learning technique that predicts missing words in sentences. 

The model is trained by masking some words in the input text and then predicting the masked words based on the context of the surrounding words. MLM is used for natural language processing (NLP) tasks, especially for training Transformer models such as BERT, GPT-2, and RoBERTa.

In MLM, part of the input text is "masked" or randomly replaced with special marks. The model has full access to tokens on the left and right sides, meaning it can handle tokens in both directions.

MLM is well suited for tasks that require a good contextual understanding of the entire sequence. For example, masking 40% outperforms 15% for BERT large-scale models on GLUE and SQuAD.

GPT is an example of a pretrained causal language model, while BERT is an example of a masked language model.