President & Co-Founder @OpenAI
Joined July 2010
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A software skill people not often appreciated outside security: probing the emergent semantics of a complex system. First understood this at Stripe while integrating various financial backends. Turns out to be extremely useful for building systems for training neural networks.
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A small taste of the kinds of content that you will be able to produce personally in the not-too-distant future:
Back in 2018 at OpenAI, a few of us wrote a story with gpt as an AI "co-author". We didn't have an AI illustrator back then, but now we sort of do, so I tried plugging the text into #dalle.
Here is the result! “The Bees”, a short story by humans & AIs: web.mit.edu/phillipi/www/the…
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Any technology comes with benefits and commensurate risks. AI is unique only in the unprecedented magnitude we expect from both.
Navigating these risks & distributing these benefits is daunting a priori, but each day of progress & experience makes the puzzle a bit more clear.
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Tending to an ML training run is like gardening.
Need to sprinkle your budding ML model with fresh data, shine energy on it in the form of compute, and nip off any unpromising growths in the form of divergences.
Main difference is that plants at maturity don't speak English.
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GPS maps made getting lost largely obsolete. Search engines made recall of specific facts far less important. Very curious what new capabilities AI systems will give us that we will soon take for granted.
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Being an ML engineer sometimes feels more like being a manager than an individual contributor. When writing software, you think through every edge case & behavior yourself. In ML engineering, you shape the behavior of systems through indirect means. Trickier, but more leverage.
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A universal design trick in software: make as much of your application stateless as possible, and concentrate your persistent state in a central, simple, perhaps unscalable solution (database, custom process). State is a fundamentally-hard part of software, so work to minimize it
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Realized today one major difference between in-person and virtual meetings: laughter.
IRL, I’ll often make jokes, but over two years of video calls I can barely recall a single funny moment.
Surprised how easy it was to forget an entire dimension of interaction, and not notice.
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How to succeed at a startup: get one big idea right, and then execute diligently for years on innumerable small features (e.g. being able to directly copy-paste working API code from your website) to delight your users.
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The difference between a framework and a library is that a framework controls how other code is written, while a library is designed to be composed with any other kind of code. Explains why developers love writing frameworks, even though users basically always want libraries.
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A surprisingly catchy (and comprehensive) song about AI progress. Will definitely be a hit at all the SF parties:
From Yann LeCun (@ylecun) to Andrew Ng (@AndrewYNg), conversational AI is full of legendary supermodels.
So we created a music video honoring the history & brilliant makers behind this incredible tech, while also poking some fun at our own #AIsupermodels.
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It is less popular but more satisfying to focus on your own success than someone else's failure.
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The hardest problem is often figuring out the easiest version of the problem that will you absolutely have to solve and cannot defer.
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Some things never change:
An ancient Egyptian painting depicting an argument over whose turn it is to take out the trash #dalle
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