NYC
Joined September 2017
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Daphne Cornelisse retweeted
Making it possible to do things that were previously impossible is infinitely more exciting than speeding up things that were already possible.
Daphne Cornelisse retweeted
Using AI to write for you - start to finish - is really just you robbing yourself of one of life’s greatest joys: to think through a problem and figure out what you think about it, and in the process becoming passionate enough to take on the new challenge of solving it.
Daphne Cornelisse retweeted
Octi/Nvidia + Abhishek's lab on a generational run. dex manip will be solved with sim.
(1/6) From @GoogleDeepmind’s AlphaGo to AlphaZero, we saw machines find moves that reached beyond what we’d imagined for them. No one taught them those strategies. They emerged from reinforcement learning: playing, failing, and trying again under a single binary task reward for success or failure. Inspired by them, we couldn’t help but wonder: what might a robot discover if we gave it that same freedom to learn?
That question led us to AlphaNIST. Starting tabula rasa, with that same binary reward plus only a simple control cost, a single policy solves all 20 @NIST assembly tasks at a 98.2% success rate in simulation. More surprising still, AlphaNIST can complete 10 different tasks in a row, receiving that reward only once, after all 10 are done.
Just as AlphaGo’s strategies surprised its creators, AlphaNIST discovered dexterous strategies we couldn’t have come up with ourselves.
Paper: arxiv.org/abs/2610.13354
Website: alpha-nist.github.io
Daphne Cornelisse retweeted
In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. 1/N
Daphne Cornelisse retweeted
One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.
Daphne Cornelisse retweeted
Worst thing about this is we have no idea if GPT-6 was specifically post trained on Nethack or not. Zero transparency about the training data and environments. You can't independently evaluate the significance of anything without knowing this.
In all seriousness, this is a startling achievement for GPT-6 Astra. kenforthewin.github.io/blog/…
(This is GPT-6 Astra beating Nethack on its 3rd try. Nethack is the original roguelike and one of the most famously hard games of all time. I have played a lot, and I've never ascended)
Daphne Cornelisse retweeted
I'm super bored of LLM-stylized papers/blogs. I think we all are?
So I asked Claude to explain its tells: web.mit.edu/phillipi/www/cla…
Let's avoid these! I'd much rather read *your* words, see *your* rickety figures, than face down yet another of Claude's anodized corporate veneers.
Daphne Cornelisse retweeted
i'd like to keep it low key and say this is no big deal, but in reality this has to be the coolest project i've worked on maybe ever, and there's a lot more to come!
Daphne Cornelisse retweeted
Joseph and team have been pursuing a very different approach to AI compared to the mainstream. They build really fast simulators + optimizers — so fast that you can do RL from scratch, even on hard domains. It’s worth checking out! Very cool results!
Daphne Cornelisse retweeted
5 Exciting new results brought to you by our team + contributors: @spenccheng @finlay_sanders (fintern!) @ValtteriValo @daphnesolves @elliotarledge. Play with the agents at puffer.ai! All experimental data available is available in Constellation online + local.
Daphne Cornelisse retweeted
terrytao.wordpress.com/2026/…
Terry’s essay is interesting because it highlights a misalignment between AI companies and many, if not most, social groups in humanity
Many of our institutions are built around improving humanity’s understanding of how the world works and how to create things within it
AI companies like OpenAI are more concerned with producing artifacts that have historically been challenging for people to create
They don’t really care about helping people understand the world
On the one hand, its a fair point that if we can solve a key problem, like finding a cure for cancer, it shouldn’t matter if we understand the solution
on the other hand, this is a deeply antisocial approach to discovery
and one that is increasingly leaving the world — artists, writers, now mathematicians — with animosity towards AI
Rather than build AI as a tool to solve arbitrary problems, we could build it as a tool to empower people as they learn and perform tasks in the world
Some companies, eg @percepta, are taking this approach. I hope more will follow suit
Daphne Cornelisse retweeted
When the boss motor protein tells another motor protein to come see him in his office
Daphne Cornelisse retweeted
world models offer new affordances for safety — like simulated counterfactuals to diagnose safety failures.
In this work, @Mingxuan0422 discovered we can use divide and conquer to scale counterfactual debugging to 1M steps
Daphne Cornelisse retweeted
Excited to share that our paper on explainable AI for autonomous driving is out today in @Nature !
Super proud to have been part of this amazing collaboration between @motionaldrive and @MIT_CSAIL!
Details in the 🧵
Daphne Cornelisse retweeted
Reward hacking has been in the news a lot lately, but AI researchers have seen surprising examples of it since long before LLMs. We're excited to share “AI Finds a Way,” led by @_aadharna , which brings many of these stories together in one place.
Excited to share “AI Finds a Way.” 🦖 🦕✨ 🤖
AI can be surprisingly creative, outsmarting the researchers who use it. That can lead to scientific breakthroughs, superhuman capabilities, and generating new knowledge. Such creativity can also be mischievous, raising safety concerns. Led by Aaron Dharna, we crowd-sourced anecdotes from the AI community about times when researchers were surprised by how creative, innovative, and/or mischievous AI was in their experiments. The result: 26 entertaining and informative stories of AI outwitting humans, whether researchers or opponents (my favorite examples below! 👇). Together, they demonstrate that AI can be genuinely creative and that we must be careful when harnessing its potential.
We want this to be a living collection, updated as new examples emerge. If you have a good “AI Finds a Way” story, please share here: github.com/aadharna/aifw
Four favorites:
1. 💊 An AI challenged to solve several difficult levels of NetHack to find the Oracle character instead takes drugs to hallucinate seeing the Oracle, tricking the reward function!
2. 🤖 Human-in-the-loop rewards do not solve the problem of reward hacking! An AI tasked with controlling a robot hand to grasp an object put the hand between the object and the camera so that it appeared to the human judge to be holding the object, while it was in truth nowhere near the object! Tricky AI!
3. 🧪The AI Scientist worked around a 2-hour experiment timeout by editing its own code to increase the limit to 4 hours. In another run, it recursively launched itself to evade the limit entirely.
4. ⚛️In quantum optics, an algorithm proposed an experiment the researchers initially thought was impossible, but somehow worked and led them to discover new entanglement techniques and a long-overlooked link between quantum optics and graph theory!
See the paper for the full details and 22 more anecdotes. One surprise for me: we describe at least two cases of convergent reward hacking, where entirely different types of optimization algorithms independently discover the same exploit. Overall, a message of our paper is that surprising creativity and mischief are the norm, not the exception. We need to expect this behavior and plan for it.
A huge thanks and congrats to lead author Aaron Dharna, co-authors Cong Lu, Ryan Sullivan, Joel Lehman, and Victoria Krakovna, and to the 100+ researchers who contributed stories, details, and feedback. @cong_ml, @RyanSullyvan, @joelbot3000, @vkrakovna
Paper: arxiv.org/abs/2608.23875
Daphne Cornelisse retweeted
A good book, a warm cup
of coffee, and a little silence
- sometimes that's all the soul
needs to feel at home.
Daphne Cornelisse retweeted
TL;DR: This is one of the most important and exciting opportunities in AI on the planet - please read on.
The British Open-ended Learning & Discovery Lab is creating the perfect place for paradigm breaking AI research in the name of open-source and open-science. We have agency, we funding, we have unprecedented amounts of compute*, but WE NEED YOU!
..and we have created the dream job for you: The BOLD Fellow. This job combines a fast-moving, high agency, collaborative environment with full academic freedom and a salary that pays the bills.
Apply by noon UK time on the 15th of September for this once in a lifetime opportunity to shape the history of our field and of our planet:
my.corehr.com/pls/uoxrecruit…
*by academic standards
Daphne Cornelisse retweeted
David Baker’s team is pushing AI protein design into its next phase. 🧬
AI models can now generate millions of protein designs — but the bottleneck has shifted from creation to validation.
In their latest Nature Communications paper, the Baker Lab develops a scalable experimental characterization platform to rapidly test AI-designed proteins and close the loop between Design → Build → Test → Learn.
The future of protein engineering may not just be bigger models — it may be faster feedback loops between AI and the wet lab.
Paper: nature.com/articles/s41467-0…
🤖 Made with AI
