President & Co-Founder @OpenAI

Joined July 2010
Any sufficiently advanced magic is indistinguishable from AI.
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DALL-E 2 is stunningly better than DALL-E 1, but what's interesting (and underappreciated) is the fact that the newer model is much smaller & amount of training compute is similar. Improvements are essentially all due to algorithmic innovation.
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The way you know you are on an exponential is that you never cease to be amazed by what your team produces.
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If you can repro it cheaply, you can fix it.
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I’d always wondered what the Jabberwock would look like:
Replying to @adnothing @npew
Here's mine, using dall-e 2 as well:
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Why did the statistician go back to school? To get his degree in regression! (Courtesy of GPT-3 when prompted to make a joke involving the dual meaning of regression.)
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One trick to stop your TODO list from growing without bound: create a fresh list every day, and archive the previous list. Copy over old items only as you actively remember them. Pretty soon you get good at only tracking items that are actually worth the mental space.
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Even in machine learning, as important as knowing how to listen to the data is knowing when to ignore it.
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A surprising fraction of machine learning performance engineering is figuring out how to profile the system component that shouldn't be your bottleneck but somehow is.
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GPT for learning a new programming language:
So this is pretty amazing. @OpenAI’s playground is actually helping me learn Swift. I started using it to ask specific questions about my code or when I need a second opinion. It actually breaks it down with clear explanations and reasoning. AI collaboration = the future.
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Science optimizes for paradigm shifts; engineering optimizes for steady progress. Both like to think they are the only way—eg that engineers don't do novel work, scientists are not pragmatic. But a 10x win matches 25x 10% optimizations, and in practice, each unlocks the other.
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My team and I were intrigued by our conversation with @SamA at @OpenAI this week. Humanity is on the precipice of great change with the proliferation of #AI. This opens up immense possibilities but also ethical questions. Our aim must be to ensure equal opportunities and access.
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GPT-oetry is getting pretty good too (and just wouldn't be the same without the DALL-E-provided illustration):
“Wet Sleeves” a poem by #gpt3 illustrated by #dalle
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Hard to internalize that this image was drawn largely by doing some matrix multiplications & some other vector math (with parameters found by doing some hill climbing in a billion-dimensional vector space):
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Machine learning isn't magic, but its results are.
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Despite many valid drawbacks, it's hard to beat a monorepo for building a long-term project that involves many tightly-collaborating teams.
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3 million DALL-E images generated so far, and have been building out our safety systems in parallel. We are now ramping to 1,000 invites per week:
Update on the DALL·E 2 waitlist: We'll be onboarding up to 1,000 people every week as we continue to enhance our safety systems. openai.com/blog/dall-e-2-upd…
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zsh plugin using Codex to write your shell commands from a comment: github.com/tom-doerr/zsh_cod…
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GIF
GPT-3 summarization has gotten pretty good:
HOT OR NOT?? 🔥 I made a quick @airtable script that uses @OpenAI API to summarise any text entered in the first field and adds the "tl;dr" to another field. And it works like a charm!! There are like TONS of #nlp #ml #automation things to be done with the same logic...
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A reliable system is just an unreliable system whose failure modes have been repeatedly encountered, studied, understood, and fixed.
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