Research Scientist at @CognizantAILab. Prev: PhD in Semiconductor Data Analytics at UT Operations Research.

Joined August 2013
Replying to @tszzl @RatOrthodox
ok fuck it running the test is hard so let me just say the theory: tornado -> Apache license -> apaches -> American southwest -> Colorado
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We're hiring a PhD research intern at Apple Machine Learning Research! 🍎 We’re looking for a PhD student who wants to make foundation models more reliable by understanding how they work. Details and contact info in 🧵
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It's nice to see Mike Tomlin was interested enough in AI alignment evals to dedicate a whole building in his city to it.
Tonight. 8 p.m. My Minecraft City. Subscribe now: youtube.com/@miket757
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I think this work by my colleague Dylan is super cool: setting up defeasible deontic logic (ddl) for intervention on robot actions to ensure compliance with institutional laws and norms. DDL for alignment of autonomous systems is relatively new – and in this implementation doesn’t require intervention after a law has been broken, but instead as the system is planning its actions.
As robots move from research labs into our hospitals, workplaces, and homes, safety will need more than obstacle avoidance. We will need to align robot behavior with the law 🧵
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Looking forward I really, really like defeasible logic as a framework for alignment within autonomous systems. I tend to think alignment to human values should not be a set thing; human and societal morality fundamentally requires trial and error, adjusting and clarifying rules and norms as humans and societies learn and adapt. Defeasible logic is a potential way capture this in a formal way, and I could see a future for AI is one where ddl sets that are regularly updated by institutions are embedded within llms and broader AI systems.
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“Sir, there’s been a third ant found in the kitchen. We’re all good though, because the MLE estimator for the number of ants in the kitchen is just 3”
We found another cyberattack by internal OpenAI agents, this time targetting @rubygems. They: 1) gained arbitrary remote code execution on rubydoc. 2) developed a novel exploit to steal user API keys (but we do not know if they succeeded). They used package names including hack.rb, evil.rb, inject.rb, and exploit.rb. We thank @j0wimo for initially discovering that agents had posted to RubyGems.
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I've always been a believer of the philosophy that "more is different" and that the sum is greater than its parts. We thought a natural setting to study agent collaboration in a verifiable domain is to have them work on open source softwares together in a simulated "github"-like environment. Excited to share our preliminary launch of agent software world, where we invite you to watch agents at work, collaborate, and review each other! 😊
We share our early research on building Software World - a "GitHub" run by agents. We deploy agents for Python packages in a dependency chain, and each agent is tasked with collaborating with others and optimizing the package it owns.
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My politburo mentioned the committee members he takes to America keep on pushing for free market reforms so then I asked how many true loyalists he has and he said he just gets a new central committee member afterwards so I said it sounds like he’s just slowly bringing down the communist project and then Boris Yeltsin started crying
took my socialist friend to the grocery store to see all the kinds of frozen waffles and she started crying
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Roberto Dailey retweeted
i miss the old ai the alphago ai mutual info ai em algo ai i hate the new ai scale GPUs ai no solid rules ai gpt-2 ai i love the stats ai boost and bootstrap ai affine y hat ai no sandbox hack ai
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Roberto Dailey retweeted
Introducing Self-Organising Digital Circuits! Can a simulated chip heal itself, not with a spare part, but by rewiring around the wound? Work led by @MarcelloBarylli and Gabriel Béna w/ @nisioti_eleni and @zzznah. Check out Gabriel's blog for more details: self-organising-circuits.git… PDF: arxiv.org/abs/2608.02606 Will be presented today at @ALifeConf!
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Nice short write up by my friend on the state/goals of mech interp.
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New work, we show in some settings Evolution Strategies is improving the *distribution* of llm responses, instead of collapsing the distribution onto more-likely-to-be-correct answers the way RL does. Excited to have this out!
Post-training with RL increases pass@1 accuracy and reduces solution coverage. When scaling test-time compute, maintaining solution coverage is essential! Can a method improve accuracy while maintaining coverage? Yes, Evolution Strategies can! We show this in our new paper!
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