AI all the time and tacos. Staff Data Scientist at ConocoPhillips. Geophysicist. U. Texas at Austin, BS. Columbia University, MA, MPhil, PhD.
Houston, TX
Joined June 2008
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Visiting Fellow, Ph.D. retweeted
Waxing gibbous almost full moon (or Harvest Moon) this evening above Fonts Point. Note the people in front of the moon (Photo: Sicco Rood).
Visiting Fellow, Ph.D. retweeted
A snapshot from the relatively "small" thunderstorm that moved over the Salton Sea on September 28th. The last sunlight still reached the cloud tops (just under 20,000 feet) 10 minutes after the sun had set locally at the Salton Sea (Photo: Sicco Rood).
Visiting Fellow, Ph.D. retweeted
Announcing No Limit: a bot building tournament hosted by @Sequoia, @citsecurities and @OpenAI.
Teams of 1 or 2 get 60 minutes to build a pokerbot from scratch.
Agents play thousands of games across six scored rounds.
Open to university students and recent grads (‘25 or ‘26).
Regionals in Boston, Pittsburgh and the Bay Area in early November. Top five teams from each fly to the Finals. $100K grand prize.
🤖 Made with AI
Visiting Fellow, Ph.D. retweeted
Another world-class robotics lab coming out of stealth, exciting results in in-context learning!
Ed is one of the leading UK robotics researchers, I remember seeing him demo Instant Policy at a tiny room in his Imperial lab early last year, amazing to see the progress since!
Today we’re out of stealth, and we’ve raised a $40m seed round co-led by @creandum and @usv, to bring Instant Robot Learning to life.
For the past few months, we‘ve been busy building a new company.
Machenta.
This follows results from a new kind of robot foundation model we’ve built, which I’m so excited to share with you today!
Usually, it takes hours/days of data collection and training to teach a robot.
We’ve built a model that learns tasks instantly: one demonstration, no further training.
A model trained entirely in simulation, without a single pixel of real-world data.
A model where teaching robots is so quick and easy, it feels like physical vibe coding.
A model for robots that learn like humans do, from someone like you.
Welcome to the era of Instant Robot Learning.
Thank you @Kathi_Wilhelm, @rebeccakaden, @hkanji, @jonno_evans and all of our angels for your support!
@Machenta_
Learn more: machenta.com/blog/instant-ro…
See below for what you can do with Instant Robot Learning!
Visiting Fellow, Ph.D. retweeted
Miguel and Carlos are veteran engineers who are building environments to help AIs master real world engineering (aerospace, mechanical, electrical, etc).
This kind of data will do for physical engineering what coding environments have done to software.
Proud to be backing them.
Visiting Fellow, Ph.D. retweeted
(1/4) @flappyairplanes raised $1B to develop megakernels for unusual, data-efficient learning.
Since I don't have $1B, I built Latch: a compiler that generates training megakernels from PyTorch. It's open source!
Visiting Fellow, Ph.D. retweeted
"With customization, and with partners in the ecosystem that we work with, you could turn these models into the frontier. And as a result, turn your proprietary data into sovereign intelligence."
Thanks, @JensenHuang and @NVIDIAAI, for highlighting our work with the team at @coderabbitai on post-training open-source models to reach frontier capability!
Visiting Fellow, Ph.D. retweeted
Neat case study on AI for systems development. It turns out you just need (0) smart humans to understand and communicate solutions, (1) good metrics and cost models, (2) super basic swarms of agents to write code and tests to optimize these metrics
On a side note: now that AI has gotten so good, finally systems PhD students & academics can, and should, build extremely ambitious systems that cover big surface areas, and evolve these systems out in the open over time (1+ year)
I built bbtree, the foundation of databases and filesystems, and the fastest concurrent btree.
Code: github.com/XiangpengHao/bbtr…
Blog post & demo: bbtree.xiangpeng.systems/
There is so much more to be discovered
In language models, turning a generative model into a critic is simple: pass in the output and prompt for a score. Robot policies can't do this - they output physical actions & there's no built-in vocabulary to predict scores.
So when it comes to RL on robot policies, we typically train a separate critic from sparse rewards - blind to everything the policy learned from the actions in the pretraining data.
We think this has it backwards - can a critic learn from those actions the way the policy does? (1/n)
Visiting Fellow, Ph.D. retweeted
Generative value classifiers are a new parametrization of Q-functions that don't require action conditioning.
We think these may combine gracefully with large, pre-trained policies and learn/generalize better from a richer output signal.
Paper: alexanderswerdlow.github.io/…
In language models, turning a generative model into a critic is simple: pass in the output and prompt for a score. Robot policies can't do this - they output physical actions & there's no built-in vocabulary to predict scores.
So when it comes to RL on robot policies, we typically train a separate critic from sparse rewards - blind to everything the policy learned from the actions in the pretraining data.
We think this has it backwards - can a critic learn from those actions the way the policy does? (1/n)
Visiting Fellow, Ph.D. retweeted
Just met an engineer at a top AI lab.
Claims nobody goes to the office anymore, they just have agents running the work while they manage everything from a phone.
Man is literally "working" from coffee shops and the beach. The future is wild.
Visiting Fellow, Ph.D. retweeted
The next frontier is AI that improves itself, and we believe everyone should know how to build it.
This semester, Ao @ao_qu18465 and I are teaching 'How to AI an AI for Almost Anything' @MIT @medialab -- a new course on self-evolving AI systems that recursively improve from experience, feedback, memory, and exploration over long interaction horizons.
The key idea: intelligence shouldn't stop improving when training ends.
We cover four levels of evolution:
(1) context evolution through prompt optimization and context management,
(2) harness evolution through memory, tools & skills, workflow optimization, and multi-agent systems,
(3) model evolution through RL, distillation, test-time training, self-play, and continual learning,
(4) task evolution through generating increasingly complex synthetic tasks, simulations, evaluations, and environments.
Slides, readings, and videos available: mit-mi.github.io/how2ai-cour…
We thank @Kimi_Moonshot and @PrimeIntellect for generously sponsoring API credits!
Visiting Fellow, Ph.D. retweeted
Happy to share RSI-top: a framework that helps autonomous research agents escape exploration collapse 📈
RSI-top outperforms OpenEvolve and ShinkaEvolve by up to 15% on kernel optimization tasks and achieves a 77.3% medal rate on MLE-Bench Lite.
We developed metrics for retrieving useful past trajectories and proposed 3 diversity control mechanisms that encourage new ideas, perspectives, and starting points when progress stalls.
📄 Blog: rsi-top.github.io
Work led by @Mao_Yuzhen, with incredible collaborators @talaei_shayan and @Azaliamirh
Visiting Fellow, Ph.D. retweeted
Finally got to put together a complete deck on the state of open models.
What they are, licenses, how competitive they are, signs people are using them, distillation's impact, and how we balance the risks and benefits of openness!
Will record a video lecture of these soon, and hopefully re-do them a few times a year.
