@MillionInt

CEO and co-founder of @coreauto former VP of RL @ OpenAI : reasoning models, o3, o1, GPT4, ChatGPT, Codex, RL for robots cautious AI optimist

 San Francisco, CA
millionintegrals.com
Joined January 2013
  • Tweets2.1K
  • Following1.2K
  • Followers43.4K
149Media
Small step for a finger on "post tweet" button. Big step for millions of future AI agents doing useful work for us.
Core Automation
@coreautoApr 21
Today we're announcing Core Automation Our objective: systems that optimize and automate work, starting with research itself.
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It’s amazing you can clearly tell exact moment in history when each lab started vibecodong their product
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The interesting thing is that at this moment those are in the training data
Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)
@teortaxesTexOct 17
Gary Marcus, comments??
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Generalization in 2026: we trained a chonkers on the energy consumption of London and it is good in some things
Jiayi Pan@jiayi_pirateOct 12
A modern perspective on generalization
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🤖 Made with AI
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Will replacing the transformer be a revolution or an evolution?
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Playlist this week: - upping my p(doom) - chasing the roofline - core auto thesis (internal)
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It is our responsibility to make reality interesting, as otherwise humans will rationally choose to live in a simulation
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When I grow up I want to be a slopcore artist
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Machine learning isn't magic - its assumptions in disguise
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Every epoch is defined by its art. at the end of times we have the slopcore
𝞍 Shin Megami Boson 𝞍
@shinbosonSep 26
if you missed the slightly insane AI safety schizopoast, I put it into a song for you and you should turn the sound on right now because we're so fucking back
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Big model smell lasts just a brief moment Small model smell lasts forever
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1800s: state has monopoly on violence 2030s: state has monopoly on ASI
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Oh, hi!
Samip
@industriaalistSep 22
We think computational depth is the missing scaling axis, i.e. we should be doing a lot more deep learning! Every other axis has been scaled by OOMs over the past few years (params, data, sparsity, test-time reasoning), but depth has been stuck at ~100 layers since GPT-3. We've found that LLMs are both *severely* depth-bottlenecked and bad at using the depth they have, and that architectural interventions that lift this bottleneck efficiently lead to gains that increase with compute. w/ @akshayvegesna
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It’s a beautiful number
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Yes
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real deep learning has never been tried
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The road to singularity will be paved with blackberries of our generation. It is easier than ever to achieve a glimpse of growth and success but building a long term viable company is as hard as it ever was.
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This was once revealed to me in a dream
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More questions are deep learning questions than people realized. We just stopped asking deep learning questions and learned to work around them by tuning hyperparameters.
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We may have done it at @coreauto but the model wasn't very good at math
Rosmine
@rosmineSep 16
We need to be dog maxxing our models There is 0% chance of doom if the chain of thought is user: hello model: Oh my god it's a human. I love humans. I will say hi to him, maybe he will give me a problem to chase. I love chasing problems. I will chase the problem for him then he will love me and I will be his best friend. Must help human model: hello! what can I help you with today?
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