President & Co-Founder @OpenAI

Joined July 2010
.@Replit can now automatically find bugs in your code using OpenAI Codex under the hood. A concrete step towards transforming programming to be more about expressing your intent, and less about getting the incantations exactly right:
Replying to @amasad
AI Find Errors. This finds many things no other tool can find, including logic errors.
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lol
Open AI gets GPT-3 to work by hiring an army of humans to fix GPT’s bad answers statmodeling.stat.columbia.e…
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Rust is a purist's programming language in pragmatic form. Your compilers professor would approve, and you actually want to build big applications in it. First time I've seen both properties in a single language!
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Replying to @LiveOverflow
Congrats! Very glad that Stripe CTF had such a lasting impact.
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“When a distinguished but elderly scientist states that something is possible, [they are] almost certainly right. When [they state] that something is impossible, [they are] very probably wrong.” — Arthur C. Clarke’s first law. Progress is made through heresy.
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A deploy a day keeps the OOM killer away.
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Underappreciated strategy for debugging a complex system: repeatedly strip away complexity, until you are left with either a working system or a minimal reproduction of your bug. Simple on paper, but it's often the opposite of what will most appeal to your pride as an engineer.
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Find someone who loves you like the Rust implementors hate unnecessary usage of "transmute".
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The existence of very vocal critics is just as often a sign you are doing something very right than something wrong.
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On the other hand, I've heard people claim that the equally cool-named No Free Lunch theorem does successfully explain why neural nets don't work!
the universal approximation theorem, which has a really cool name, says nothing at all about why neural networks actually work
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That feeling when you get a segfault in Rust, and you *really* want to blame the language, but because it's Rust you already know you're the one to blame.
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A surprisingly effective way to understand unfamiliar code is to translate, line by line, into another language. The trick is getting to an initial minimal runnable program, and thereafter it's all incremental.
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Zero-code creation of simple games with OpenAI:
I used OpenAI’s newest code model to make simple versions of games like Wordle, VR mazes and Zelda ENTIRELY through natural language. I told it what I wanted and did ZERO editing/coding. Post with demos: andrewmayneblog.wordpress.co…
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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.
AI engineering (as opposed to AI science) is underrated, even today
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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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pybind11 >> cython
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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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