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Introducing OpenAI (Founding Announcement, December 2015)
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: OpenAI
This is the original founding statement of OpenAI (December 2015), historically significant as it articulates early institutional intentions around safety, openness, and beneficial AI development — useful context for understanding how OpenAI's mission and structure have evolved over time.
Metadata
Importance: 62/100blog postprimary source
Summary
The founding announcement of OpenAI, originally established as a non-profit AI research company in December 2015, articulating its mission to advance AI for broad human benefit rather than shareholder return. The post outlines OpenAI's core philosophy: that AI should be openly researched, widely distributed, and developed with safety and positive human impact as primary goals. It introduces the founding team and signals concern about the societal risks of misaligned or misused advanced AI.
Key Points
- •OpenAI was founded as a non-profit with the explicit goal of benefiting humanity broadly, free from financial return obligations.
- •The founding philosophy emphasizes open publication of research (papers, code, blog posts) and free collaboration across institutions.
- •The announcement acknowledges both the transformative potential and serious risks of human-level AI if 'built or used incorrectly'.
- •Deep learning's surprising progress motivated the need for a safety-focused institution to guide AI development toward good outcomes.
- •Founding team included Ilya Sutskever (Research Director), Greg Brockman (CTO), and other world-class AI researchers and engineers.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| OpenAI | Organization | 62.0 |
7 FactBase facts citing this source
| Entity | Property | Value | As Of |
|---|---|---|---|
| OpenAI | Founded Date | Dec 2015 | — |
| OpenAI | Legal Structure | Nonprofit 501(c)(3) | Dec 2015 |
| OpenAI | Founded By | sid_JKsVHQ5stQ, sid_eYrBgMLJDw, sid_Yq4s2cI7ng, sid_69vftmr1jg, sid_4eNHxKcml1, sid_ZocUS6ej6S | — |
| Elon Musk | Role / Title | Co-chairman | Dec 2015 |
| Ilya Sutskever | Employed By | 1LcLlMGLbw | Dec 2015 |
| Greg Brockman | Role / Title | Co-founder & CTO | Dec 2015 |
| Greg Brockman | Employed By | 1LcLlMGLbw | Dec 2015 |
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The Wayback Machine - http://web.archive.org/web/20240425131034/https://openai.com/blog/introducing-openai
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Introducing OpenAI
OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return. Since our research is free from financial obligations, we can better focus on a positive human impact.
Illustration: Justin Jay Wang
December 11, 2015
Authors
Greg Brockman
Ilya Sutskever
OpenAI
Announcements
OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return. Since our research is free from financial obligations, we can better focus on a positive human impact.
We believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as possible. The outcome of this venture is uncertain and the work is difficult, but we believe the goal and the structure are right. We hope this is what matters most to the best in the field.
Background
Artificial intelligence has always been a surprising field. In the early days, people thought that solving certain tasks (such as chess) would lead us to discover human-level intelligence algorithms. However, the solution to each task turned out to be much less general than people were hoping (such as doing a search over a huge number of moves).
The past few years have held another flavor of surprise. An AI technique explored for decades, deep learning, started achieving state-of-the-art results in a wide variety of problem domains. In deep learning, rather than hand-code a new algorithm for each problem, you design architectures that can twist themselves into a wide range of algorithms based on the data you feed them.
This approach has yielded outstanding resu
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