President & Co-Founder @OpenAI
Joined July 2010
- Tweets 4,815
- Following 65
- Followers 498,999
- Likes 1,386
We do build on Azure, but also operate at such an unprecedented scale that we have to solve problems at essentially all layers of the stack.
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An illustration that is all-too-applicable to this tweet — "network switch that looks like a monster rampaging a datacenter" as generated by @sidorszymon
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Was thinking about my single most important tool for programming productivity, and had a surprising realization — it's not emacs or mypy, it's Slack. The job is about working in close coordination with a team to achieve something you couldn't on your own.
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As an illustration – "The systems are down" by DALL-E 2. Happy that it's just the dev cluster!
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That feeling when your phone starts blowing up with alerts, indicating everything is down... and then you realize the alerts are all for your dev cluster 😅.
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Essay co-authored with GPT-3 was selected for the 2022 Best American Essays anthology:
Best American 2022 selection: Vauhini Vara (@vauhinivara)'s "Ghosts," edited by @chameauleon, in @believermag! believermag.com/ghosts/
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A book of 1000 paintings of robots. Generated by a robot, curated by a human:
I spent most of this past weekend experimenting with DALL·E 2, OpenAI’s new AI system that can create realistic images from a written description.
I curated a book of 1000 robot paintings. You can view the entire thing online at archive.org/details/11111010… #dalle
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Unpopular opinion: don't rely on implicit truthy constructs in your language, and instead always convert to bool yourself.
For example, in Python rather than "if mylist:", do "if len(mylist) > 0:".
An example of trading more keystrokes for less cognitive burden for readers.
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DALL-E 2 applied to generating assets for game development:
Prototype of DALL·E 2 x @Replit integration:
- Describe the asset in the search
- If it doesn't exist it generates it on the fly
- Instantly use it in the game
🤯 🤯 🤯
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The mathematics of measuring how well our true objective (e.g. helpful or accurate responses) is being optimized when training an AI systems on a proxy metric (e.g. the output of a reward model). Pretty cool & not obvious that you can measure this at all!
openai.com/blog/measuring-go…
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Software engineering: 50% understanding requirements, 40% complexity management, 9% debugging, 1% solving "interesting" algorithmic problems.
You'll enjoy software engineering a whole lot more if you instead think of the first 99% as the interesting part.
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Big challenge in ML engineering: finding aggregate views that let you quickly understand the micro details of everything happening in your system. Often the biggest problems are simple problems obvious from looking at one specific computation. ML rewards attention to detail.
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It’s time to become an ML engineer: blog.gregbrockman.com/its-ti…
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As a bonus, here's a DALL-E generation for "a program running on a universe-sized quantum computer":
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Reality is a program running on a universe-sized quantum computer. So the best way to solve a hard problem is to maximize contact with reality — lets you tap into an unfathomable amount of compute.
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