President & Co-Founder @OpenAI
Joined July 2010
- Tweets 4,815
- Following 65
- Followers 498,999
- Likes 1,386
A good overview of what it's like working with DALL-E:
A week with Dall-E 2, OpenAI's text-to-image AI tool that is in private research beta and feels like a breakthrough in the history of consumer tech (@caseynewton / The Verge)
theverge.com/23162454/openai…
techmeme.com/220610/p21#a220…
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Greg Brockman retweeted
Check out this post with @gdb, if you are curious about how to train large deep learning models and you may find it is easier than you expected :) Also we are hiring!
Techniques for training large neural networks, by @lilianweng and @gdb: openai.com/blog/techniques-f…
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How large neural networks are trained across increasingly massive clusters — cleverly slicing the computation on a wide variety of axes, rematerializing intermediate results, and much more:
Techniques for training large neural networks, by @lilianweng and @gdb: openai.com/blog/techniques-f…
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A surprising fraction of accomplishing any unprecedented feat is having the conviction that it can be done.
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Twitter Spaces discussion on Best Practices for LLM deployment with @AI21Labs @OpenAI @CohereAI at noon PT, moderated by @percyliang:
Join @Miles_Brundage @aidangomezzz @percyliang and @Udi73613335 for a Twitter Spaces discussion in an hour (noon PT) to hear about best practices for deploying large language models👇
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Shows that there’s room for improvement with the next version of DALL-E.
I asked #dalle to generate paintings that AIs like but humans don’t. Nice try, AI, but I actually like SEVERAL of these paintings!
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Very cool to see how useful Copilot already is. Easy to forget that it’s still just the beginning of the road for this technology, and in a few years it’ll be astronomically more useful than it is today.
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Each year, we've built something that makes our previous work look antiquated—a trend across both OpenAI & the field. The tech is getting powerful, and the only way to continually ship 10x better models to the world is for industry to work together to grow into worthy custodians.
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What makes our approach different is that we're starting with a practical, useful foundation that companies have an incentive to opt into — these best practices are grounded in hard-won experience with how these models are used & abused in practice. (7/8)
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We ultimately hope to grow cooperation like this into an approach to mitigate ex-risk from race conditions & a multi-polar world around AGI (something covered in our charter, and which everyone agrees is good in theory, but to date no approach has found traction). (6/8)
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The best practices we developed, with cooperation & support from others in the industry, are the most concrete step so far towards building industry regulations for AI. We'll welcome thoughtful government regulation, but can't delay for it given how fast the tech is moving. (5/8)
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Credit card processing ended up being primarily industry-regulated for historical reasons — the networks established rules with a strong eye towards consumer protection early on, and so there was not a strong public need for government to upend the industry. (4/8)
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It didn't have to be this way. Many other financial sectors are primarily government-regulated, which makes innovation much harder & rules often end up not matching the needs of the present day. This results in more consumer protection, at the cost of less useful products. (3/8)
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The fact that card processing is industry-regulated is very beneficial for consumers, since the rules can adapt when new paradigms appear (e.g. to allow crowdfunding—great but risky innovation, since card networks might have pay back consumers if the maker doesn't ship). (2/8)
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One note from my experience at @stripe — surprisingly, the credit card processing industry is primarily industry-regulated. The card networks like Visa make rules, which get pushed to their member banks; those banks make rules for the processors who run on top of them. (1/8)
We're working with others in the nascent industry of creating & deploying language models in order to produce safety best practices.
Ultimate goal is to have a practice for cooperating on increasingly high-stakes safety problems as AI progresses.
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We're working with others in the nascent industry of creating & deploying language models in order to produce safety best practices.
Ultimate goal is to have a practice for cooperating on increasingly high-stakes safety problems as AI progresses.
Preliminary best practices for language model providers, authored together with @CohereAI and @AI21Labs. openai.com/blog/best-practic…
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