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Openai.com : #1 Artificial intelligence (AI) research website

Openai.com : #1 Artificial intelligence (AI) research website



OpenAI is an artificial intelligence (AI) research website where you can search anything you want to know. This site is like your personal assistant and you can ask anything from it . According to me this website helps me to write blogs of important topis . I just need to feed topic in its search box and it write blogs for me and i just need to edit this and add more information according to me to make my blogs more easy to understand. This website comes under the OpenAI LP and its parent company, the non-profit OpenAI Inc.

The company conducts research in the field of AI with the stated goal of promoting and developing friendly AI in a way that benefits humanity as a whole.

The organization was founded in San Francisco in late 2015 by Sam Altman, Elon Musk, and others, who collectively pledged US$1 billion. Musk resigned from the board in February 2018 but remained a donor. In 2019, OpenAI LP received a US$1 billion investment from Microsoft and Matthew Brown Companies. OpenAI is headquartered at the Pioneer Building in Mission District, San Francisco.

Industry Artificial intelligence
Founded December 11, 2015; 7 years ago
  • Sam Altman
  • Ilya Sutskever
  • Greg Brockman
  • Wojciech Zaremba
  • Elon Musk
  • John Schulman
Headquarters Pioneer Building, San Francisco, California, US[1][2]
Key people
  • Greg Brockman (Chairman & President)

Sam Altman (CEO)

  • Ilya Sutskever (Chief Scientist)
  • DALL-E
  • GPT-3
  • OpenAI Five
  • ChatGPT
  • OpenAI Codex
Number of employees
>120 (as of 2020)           
Website openai.com

In December 2015, Sam Altman, Elon Musk, Greg Brockman, Reid Hoffman, Jessica Livingston, Peter Thiel, Amazon Web Services (AWS), Infosys, and YC Research announced the formation of OpenAI and pledged over US$1 billion to the venture. The organization stated it would “freely collaborate” with other institutions and researchers by making its patents and research open to the public. OpenAI is headquartered at the Pioneer Building in Mission District, San Francisco.

In April 2016, OpenAI released a public beta of “OpenAI Gym”, its platform for reinforcement learning research. In December 2016, Openai.com released “Universe”, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications.

In 2018, Musk resigned his board seat, citing “a potential future conflict (of interest)” with Tesla AI development for self driving cars, but remained a donor.

In 2019, OpenAI transitioned from non-profit to “capped” for-profit, with profit cap set to 100X on any investment.The company distributed equity to its employees and partnered with Microsoft, who announced an investment package of US$1 billion into the company. OpenAI then announced its intention to commercially license its technologies.

In 2020, openai.com announced GPT-3, a language model trained on trillions of words from the Internet. It also announced that an associated API, named simply “the API”, would form the heart of its first commercial product. GPT-3 is aimed at natural language answering of questions, but it can also translate between languages and coherently generate improvised text.

In 2021, openai.com introduced DALL-E, a deep learning model that can generate digital images from natural language descriptions.

Around December 2022, OpenAI received widespread media coverage after launching a free preview of ChatGPT, its new AI chatbot based on GPT-3.5. According to OpenAI, the preview received over a million signups within the first five days.According to anonymous sources cited by Reuters in December 2022,  openai.com was projecting a US$200 million revenue for 2023 and US$1 billion revenue for 2024.As of January 2023, it was in talks for funding that would value the company at $29 billion.


Some scientists, such as Stephen Hawking and Stuart Russell, have articulated concerns that if advanced AI someday gains the ability to re-design itself at an ever-increasing rate, an unstoppable “intelligence explosion” could lead to human extinction. Musk characterizes openai.com as humanity’s “biggest existential threat.” OpenAI’s founders structured it as a non-profit so that they could focus its research on making positive long-term contributions to humanity.

Musk and Altman have stated they are partly motivated by concerns about the existential risk from artificial general intelligence. openai.com states that “it’s hard to fathom how much human-level AI could benefit society,” and that it is equally difficult to comprehend “how much it could damage society if built or used incorrectly”.Research on safety cannot safely be postponed: “because of AI’s surprising history, it’s hard to predict when human-level AI might come within reach.” openai.com states that AI “should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as possible…”.Co-chair Sam Altman expects the decades-long project to surpass human intelligence.

Vishal Sikka, former CEO of Infosys, stated that an “openness” where the endeavor would “produce results generally in the greater interest of humanity” was a fundamental requirement for his support, and that OpenAI “aligns very nicely with our long-held values” and their “endeavor to do purposeful work”.

Cade Metz of Wired suggests that corporations such as Amazon may be motivated by a desire to use open-source software and data to level the playing field against corporations such as Google and Facebook that own enormous supplies of proprietary data. Altman states that Y Combinator companies will share their data with OpenAI.

In 2019, openai.com became a for-profit company called OpenAI LP to secure additional funding while staying controlled by a non-profit called OpenAI Inc in a structure that OpenAI calls “capped-profit”,having previously been a 501 nonprofit organization

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

ChatGPT: Optimizing Language Models for Dialogue

We are excited to introduce ChatGPT to get users’ feedback and learn about its strengths and weaknesses. During the research preview, usage of ChatGPT is free. Try it now at chat.openai.com.


In the following sample, ChatGPT asks the clarifying questions to debug code.


We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them compose their responses. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format.

To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality.

To collect this data, we took conversations that AI trainers had with the chatbot. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. Using these reward models, we can fine-tune the model using Proximal Policy Optimization. We performed several iterations of this process.

ChatGPT is fine-tuned from a model in the GPT-3.5 series, which finished training in early 2022. You can learn more about the 3.5 series here. ChatGPT and GPT 3.5 were trained on an Azure AI supercomputing infrastructure.


  • ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers. Fixing this issue is challenging, as: (1) during RL training, there’s currently no source of truth; (2) training the model to be more cautious causes it to decline questions that it can answer correctly; and (3) supervised training misleads the model because the ideal answer depends on what the model knows, rather than what the human demonstrator knows.
  • ChatGPT is sensitive to tweaks to the input phrasing or attempting the same prompt multiple times. For example, given one phrasing of a question, the model can claim to not know the answer, but given a slight rephrase, can answer correctly.
  • The model is often excessively verbose and overuses certain phrases, such as restating that it’s a language model trained by OpenAI. These issues arise from biases in the training data (trainers prefer longer answers that look more comprehensive) and well-known over-optimization issues.12
  • Ideally, the model would ask clarifying questions when the user provided an ambiguous query. Instead, our current models usually guess what the user intended.
  • While we’ve made efforts to make the model refuse inappropriate requests, it will sometimes respond to harmful instructions or exhibit biased behavior. We’re using the Moderation API to warn or block certain types of unsafe content, but we expect it to have some false negatives and positives for now. We’re eager to collect user feedback to aid our ongoing work to improve this system.

Iterative deployment

Today’s research release of ChatGPT is the latest step in OpenAI’s iterative deployment of increasingly safe and useful AI systems. Many lessons from deployment of earlier models like GPT-3 and Codex have informed the safety mitigations in place for this release, including substantial reductions in harmful and untruthful outputs achieved by the use of reinforcement learning from human feedback (RLHF).

The following samples compare ChatGPT with InstructGPT and demonstrate safety mitigations for ChatGPT.
Sample 1 of 3PreviousNext

We know that many limitations remain as discussed above and we plan to make regular model updates to improve in such areas. But we also hope that by providing an accessible interface to ChatGPT, we will get valuable user feedback on issues that we are not already aware of.

Users are encouraged to provide feedback on problematic model outputs through the UI, as well as on false positives/negatives from the external content filter which is also part of the interface. We are particularly interested in feedback regarding harmful outputs that could occur in real-world, non-adversarial conditions

,as well as feedback that helps us uncover and understand novel risks and possible mitigations.You can choose to enter the for a chance to win up to $500 in API credits.Entries can be submitted via the feedback form that is linked in the ChatGPT interface.

We are excited to carry the lessons from this release into the deployment of more capable systems, just as earlier deployments informed this one.

  1. No purchase necessary, void where prohibited. Must be at least 18 to enter.

  1. Stiennon, Nisan, et al. Advances in Neural Information Processing Systems 33 (2020): 3008-3021.
  2. Gao, Leo, John Schulman, and Jacob Hilton.  arXiv preprint  (2022). The inspiration for this contest comes in part from work by Kenway, Josh, Camille François, Sasha Costanza-Chock, Inioluwa Deborah Raji, and Joy Buolamwini. Bug Bounties For Algorithmic Harms? Lessons from Cybersecurity Vulnerability Disclosure for Algorithmic Harms Discovery, Disclosure, and Redress. Washington, DC: Algorithmic Justice League. January 2022.See also work by Brundage, Miles, Avin, Shahar, Wang, Jasmine, Belfield, Haydn, and Gretchen Krueger et al. “Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims,” April 2020.  See an earlier instance of such a competition at HackerOne. 2021b. “Twitter Algorithmic Bias. Finally, see early published work on this topic from Rubinovitz, JB, “Bias Bounty Programs as a Method of Combatting Bias in AI,”

Contributors: John Schulman, Barret Zoph, Christina Kim, Jacob Hilton, Jacob Menick, Jiayi Weng, Juan Felipe Ceron Uribe, Liam Fedus, Luke Metz, Michael Pokorny, Rapha Gontijo Lopes, Shengjia Zhao, Arun Vijayvergiya, Eric Sigler, Adam Perelman, Chelsea Voss, Mike Heaton, Joel Parish, Dave Cummings, Rajeev Nayak, Valerie Balcom, David Schnurr, Tomer Kaftan, Chris Hallacy, Nicholas Turley, Noah Deutsch, Vik Goel,

Jonathan Ward, Aris Konstantinidis, Wojciech Zaremba, Long Ouyang, Leonard Bogdonoff, Joshua Gross, David Medina, Sarah Yoo, Teddy Lee, Ryan Lowe, Dan Mossing, Joost Huizinga, Roger Jiang, Carroll Wainwright, Diogo Almeida, Steph Lin, Marvin Zhang, Kai Xiao, Katarina Slama, Steven Bills, Alex Gray, Jan Leike, Jakub Pachocki, Phil Tillet, Shantanu Jain, Greg Brockman, Nick Ryder

Also read:Samsung Electronics one of the best decisions to scale up its chip production capacity by 2023

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