President & Co-Founder @OpenAI
Joined July 2010
- Tweets 4,815
- Following 65
- Followers 498,999
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A fundamental improvement to the GPT-3 interface. You can now provide instructions for how to edit existing text, or indicate some area to insert new text. Opens the possibility space for what can be built using OpenAI:
GPT-3 can now make changes to existing content, not just predict what comes next. Released in the API today: openai.com/blog/gpt-3-edit-i…
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The OpenAI approach to making AI progress is to build working systems that can do something that was previously impossible.
Success requires top-notch engineering; every week I find myself charting the depths of a new technical problem that I hadn't previously even considered.
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Software & mathematics are both fundamentally about iteratively building better abstractions. The difference is that math is encoded as proofs, which require special skills to appreciate, while software is encoded as machine-runnable code, letting anyone enjoy the results.
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Programming is the art of coaxing reluctant computers to do what you want most of the time.
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Lessons learned from detecting & stopping hundreds of actors attempting to misuse GPT-3:
Deploying and studying the real-world use of language models helps us learn more about safety and misuse than research alone.
As we advance our safety and policy work, we're sharing some of our findings to help others do the same. openai.com/blog/language-mod…
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Humans are great at coming up with a single solution that meets any given constraints, and thereafter coming up with reasons why any other proposed solution won’t work.
So when optimizing for a goal, set aspirational constraints, and then relax them until a solution is found.
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One of the least-appreciated skills in programming is writing anti-frustrating error messages.
A good error message should make it self-evident (a) what the user did, (b) what acceptable inputs are, and (c) how to fix the problem.
Can determine love or hate for your library.
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GPT-3 for fixing OCR errors:
A rare & unknown gem of a paper — academia.edu/67825612/A_Soci… (from 1996!) analyzes the root cause of the AI winters.
The story you always hear is that the neural net people overhyped everything; the real story seems to be a dedicated smear campaign by eminent scientists.
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A rare & unknown gem of a paper — academia.edu/67825612/A_Soci… (from 1996!) analyzes the root cause of the AI winters.
The story you always hear is that the neural net people overhyped everything; the real story seems to be a dedicated smear campaign by eminent scientists.
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One of my biggest growth moments as a programmer was realizing that libraries I use are just code, and I could read them directly rather than puzzling it out from the docs.
Even today, I am surprised how much faster I move every time I start reading a layer I'm building on.
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"An academic is someone who has given up earthly rewards in exchange for the admiration of their peers" — @ilyasut
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Shortly after moving to Silicon Valley, I remember thinking that the “tech industry” was a misnomer—only route to a startup was applying already-created technologies.
No longer—from AI to fusion to crypto, the future has been (slowly!) shifting back to inventing high technology.
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Alignment is the challenge of designing technical & non-technical solutions to ensure that increasingly powerful AI systems further the hopes of their operator.
Probably unlike any prior technological experience, but with parallels to life experience.
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AI supercomputing — the art of coaxing mind-boggling numbers of machines to perform their part of a single precisely-orchestrated computation — presents both the hardest & most rewarding technical challenge I've ever worked on.
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Surprising commonality between AI & startups: newcomers tend to believe that ideas are the hard part and execution is commoditized.
In reality, many (not all!) great ideas are already in the air, and proving their value requires a rare caliber of execution.
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