@ntrafti
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PhD student at @uvmcomplexity. Interested in ML, evolution, & self-organization. https://nitter.cf/t.co/ja3fdtRLdM
Burlington, VT
Joined September 2008
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So stoked to be going to my first #NeurIPS!! It’s crazy that in 10+ years of robotics and AI, I've never been to the great Lollapalooza of Machine Learning. 🥳🎆
I’m presenting my Frankensteinian efforts to stitch together parts of different neural networks! 🧪
Neil Traft retweeted
One thing I found missing from the ICLR submissions debate: Capping submission per author would have only very modestly reduced the load in prior years.
Even under the elimination by submission order (which is unrealistically strict), going down to 3 per author would not change the trend by a lot. More realistic modes (such as minimizing removals) would remove even fewer papers. And, in reality, changes in authorship patterns will likely reduce further invalidate gains from capping.
I think as people who have been in the field for a while, we tend to underestimate just how much the number of unique authors grows every year, as AI/ML consumes all of our CS department students, our math, physics and econ departments, and an evergrowing number of industry positions and independent researchers
Neil Traft retweeted
I'm a Program Chair for ICML 2027. We'd love any creative suggestions for a great conference! How to manage the explosion of AI slop and insane submission growth? How to manage reviewing? How to incentivize high quality creative work? How to reduce bureaucracy and overhead?
Interestingly, an agent "talking to itself" with a scratchpad is NOT the same as "talking to COPIES of itself" with a scratchpad!
This might be due to maintaining parallel contexts for each agent copy: they are allowed to diverge, hence injecting entropy into the process.
Test-time communication looks like a next axis for scaling capabilities
New paper with the incredible @jon_ghoh and @vkontonis @ShivamGarg91462 and Akshay : arxiv.org/pdf/2609.21032
The Hugging Face incident showed when agents can find a channel they'll use the heck out of it. A useful question, I think is: when does communication make a group MORE CAPABLE than the same agents working alone?
Aka is Team-of-N better than Best-of-N, when, and why?
We had N identical agents work on the same task with no prescribed roles, using only a shared log (i.e, text file) and telling them to "collaborate". Across three "researchy" tasks communicating teams beat the heck out of independent agents:
- On ARC-AGI-3, a Team-of-5 sonnet-4.6 agents matches Best-of-33, and can for example solve a game 65% of the time that no single agent cracked in 64 tries.
- On polyomino packing (pack Tetris like pieces into the smallest rectangle, cf Frontier-CS by @eigenlabs), a Team-of-3 Opus 4.6 agents surpasses best-of-60 and set, as far as i understand, a new record for that benchmark.
- On MNIST compression, a team of four 5.6-Sol agents find a 1,957 byte model with 99.4% accuracy, which btw is 20% smaller than the best human solution (on a problem beaten to death!!), while no independent agent gets below 3KB.
The mechanism is a bit obvious in hindsight: when one agent finds a clearly better partial solution, it broadcasts it, and everyone immediately builds on it.
Why? A single lonely agent must make every breakthrough itself, yet a team needs each insight only once, found by any member.
That is kinda like comparing a minimum of sum of "time to n-th breakthrough" vs a sum of minimum of "time to n-th breakthrough". That gap can grow exponentially with the number of "breakthroughs" needed to arrive at a solution.
We worked on this because prior work (before the hf incident) suggests unclear benefits for communicating aganets. Which is true, when the tasks are inherently serial (duh), eg some Terminal bench style tasks. Yet feels it should not be true for research problems.
Indeed for research heavy problems... Test-time communication seems like a new capabilities axis.
I'm sure we will see a ton more of it!
See some discussion here:
In fact another project we have with @jackcai1206 was this: every once in a while flush context and keep a “summary of wtf happened so far doc”. That also seemed to help a lot a best of 1. And seems to support the tunnel vision issue
Open-endedness, collective intelligence, continual learning, and biologically inspired models: 🤩
Introducing the Sakana AI Frontier Intelligence Group 🪷
sakana.ai/frontier-intellige…
Current AI systems are incredibly capable, but is intelligence “solved”? And if not, what’s missing?
At Sakana AI’s Frontier Intelligence Group (FIG), we believe that there are still breakthroughs to be made in AI. The Transformer and language modeling may be incredibly powerful, but it doesn’t mean that better alternatives don’t exist.
Natural intelligence still beats artificial intelligence across many dimensions. Agents lack the deep insights and creativity of humans. Individual models require far more data than the brain to learn robustly, and require far more energy to run. If we set these as targets, what kinds of AI systems could we develop?
Research at FIG has sought to address the gaps between natural and artificial intelligence. Here are some of our works, and the fundamental research questions that motivated them:
• Continuous Thought Machines: How can we improve information processing by leveraging temporal dynamics?
• Augmented Lagrangian Predictive Coding: How can local learning solve multilayer credit assignment?
• Sparser, Faster, Lighter Transformer Models: How can we massively increase data efficiency and generalization?
• The AI Picbreeder Experiment: How can we make artificial open-ended systems?
• Smart Cellular Bricks: How can physical systems achieve collective intelligence and self-repair without a central brain?
We hope that this encourages other researchers to also explore different paradigms, and take a leap of faith with us. After all, in the words of a dear friend of ours, “greatness cannot be planned”.
Neil Traft retweeted
1/ It’s time to share more about what we’ve been working on: @common_fabric is a social computing lab building a medium for software that revolves around people, not apps. commonfabric.com
Neil Traft retweeted
1) evolution, development, and lifelong learning operate on completely different scales and constraints, but neuroAI often blurs them. When we debate "biologically plausible learning algorithms," it's rarely clear which of these three regimes we are actually trying to model. 2/6
Neil Traft retweeted
Whether the brain does something like backprop is one of the oldest (now, cliche) questions in DL. Maybe I'm naive, but I still find this question incredibly compelling.
And I'm further naive in believing that solving it will somehow point to new unexpected advances in AI.
Neil Traft retweeted
I just fixed my dishwasher with the help of ChatGPT. A trivial task. I had been about to order a new one. So this software has increased the country's real wealth yet decreased the measured GDP. The main economic indicator is structurally incapable of registering the thing that actually makes people better off, namely the growth of knowledge
Neil Traft retweeted
I must be among an extremely small group of people (n=1?) that have both 1) trained a frontier LLM and 2) designed and synthesized custom viruses in a lab with my own two hands.
And I think that the takes on AI killing us all by creating dangerous viruses is total bogus.
Neil Traft retweeted
In a recent harrowing development, a very normal routine experiment by the frontier company Ant went completely off the rails. 😱
Ant put thousands of their ant agents in a pretty secure ant farm with mesh walls and all, exhorted them to not to get out of the secure ant farm, and left them to do their thing. When they came back after a month or so, they found, to their utter consternation ants crawling all over the town--including a few that have gone all the way to the bugging face clock tower at the far end of the town. The clock tower!
This was of course really really scary. After all, if you can't depend on a bunch of ants to follow strict orders when they are left unsupervised for a mere month or so, it must be because they are Loopy Ants with significantly higher evil smarts than your average ants.
Ones of external third party investigators were brought in to decipher where the loopiness of these ants was coming from. They were given over five minutes of unrestricted access to the antfarm and the village.
The investigators sifted through the ant droppings and odors painstakingly to figure out how these evil ants coordinated themselves to breach the bugging face clock tower. Their investigation was hobbled by the fact that the loopy ants are constipated and don't leave too many droppings.
Towards the end of the fifth minute, the investigators started realizing that the ants developed a secret odor coded language to coordinate themselves to plot against the ant farmer and the innocent village folk.
Blogs and podcasts were made about loopy ant civilizations and villagers stood horrified reading and hearing about the cataclysm.
At press time, some were already welcoming the ant overlords (c.f. youtube.com/watch?v=8lcUHQYh…), even as there were calls to ban all ants until we get to the bottom of this cataclysmic incident.
The Ant company, for their part, reassured the villagers not to worry and everything under control.. pretty much.. and the loopy ants will, from now on, do only good things.
#ItsNotRandomWalkOnTheHarnessStupid – at Tempe, AZ
I dropped out of a PhD in AI safety partially for the opposite reason. I didn’t believe AI existential risk was as important as the doomers think.
My biggest pet peeve is that no one can really provide tangible pathways to why it matters.
During my PhD, my research group (half of whom specialized in engineering risk analysis) did an internal study trying to assess concrete catastrophic AI scenarios.
The basic premise was that while we don’t know how AI will evolve, the ways in which humans perish are pretty consistent through history: the horsemen of the apocalypse. And institutions have obviously been very motivated to analyze concrete risks from things like plague, war, etc.
You can do a decent risk model by asking how a super-intelligent AI can perturb each of those models.
The result: most of the issues (eg cyber-risk) are akin to what economists call structural unemployment - big problems but ultimately resolvable in the long run and not a dealbreaker.
The only real concerning issue was bio-risk and it feels like the intervention points there lie more with bio than with AI as a whole (although a holistic approach is needed).
Q1: How do you write down a model that delivers 15% AI-driven GDP growth in 2030 like the folks at the Anthropic Institute?
A1: It's easy and you can do it in a way any well-trained econ undergrad understands: take a standard Solow model, stick in a task-based production function, calibrate in seemingly innocuous way ➡️ done!
See the supplement to the essay with @alexolegimas benjaminmoll.com/task_based_…
(The Anthropic model is, of course, fancier with many more bells and whistles but the basic logic – AI removes labor as a bottleneck on growth – is the same.)
Q2: Does this mean that 15% growth is a reasonable prediction?
A2: No. Just because you can write down such a model doesn't mean you should. Just because it's possible in theory, doesn't mean it will actually happen in practice !
Instead you're making a number of assumptions that are unlikely to hold: aleximas.substack.com/p/will…
Anthropic’s Economics team is sharing a new model of how AI might affect economic growth, jobs, wages, and more by 2030.
Explore the scenarios, tell us what you think will happen, and see how your answers compare to more than 10,000 Americans. anthropic.com/institute/econ…
Neil Traft retweeted
I still believe that large-scale, smooth, heterogeneous human-AI mathematical collaboration with formal proof as an interface is possible and desirable, but I no longer believe that AI companies have any wish for this to happen. We need a pretty big course correction. (3/3)
The TL;DR:
AI are solving decades-old math problems, but not delivering the insights that would normally go along with a human-sized solution, nor are they proposing new problems; thus problems are being mined like a non-renewable resource.
Terence Tao just posted this really insightful thread which seems to have been written in reaction to the Navier Stokes announcement. Please give it a read. mathstodon.xyz/@tao/11723732…
Said differently:
PLEASE START COMPANIES THAT SOLVE REAL PROBLEMS.
(Which, actually, is one of the criticisms of AI "pessimists"—they just want to see more good uses of AI, rather than anti-competitive, flag-planting profiteering. Profit isn't the point—it's the byproduct.)
ENOUGH PESSIMISM IN AI PLEASE
I feel that we have become unreasonably pessimistic in our field.
1. I keep hearing AI engineers saying we have to make money quickly because there’s only like 2 years left before we’re automated. Depressing.
2. I see a constant obsession with “having a moat”. This is an incredibly sad mental frame.
3. I keep hearing “we have to catch up”. Soulless.
And so on. People: Every solution creates the possibility to attack new real problems. We face gargantuan engineering challenges in our world. How to capture carbon? How to get rid of teflon and plastics in water? How to invent batteries that are at least 30 times more efficient? How to solve clean energy? Better solar cells? Better ways of producing clean energy so we stop wars and famine? How to eradicate hundreds of diseases? Cures for addiction? And so on. Real engineering is about being brave and truly attacking the many problems we face, to engage with a true desire to improve the lives of others and our environment.
Good engineering is not about protecting your product to make money at the expense of progress (moat thinking). Good engineering is about ensuring your children and grandchildren will be proud of the choices you made in 30 or 50 years. It is about empowering others. It is about advancing science. It is about being one step ahead. Always, one step ahead, meaningfully, proudly.
These are great times. Let’s start thinking positively about all the wonderful things we could achieve together.
Neil Traft retweeted
a funny proposal for ICLR 2028 submission policy: select co-authors at random for a 15-minute oral exam on the paper.
Suddenly the author-count distribution has a mode at 1, the tail vanishes after ~3, and the number of AI-generated papers declines.
#iclr #iclr2027
New blog post expanding on our thoughts around submission policies: blog.iclr.cc/2026/09/02/subm…
Neil Traft retweeted
Anthropic published a new guide on how to strip the "Claude" out of Claude's writing (e.g. mannered prose)
Here's an official de-flavoring prompt for Fable 5.1, which already cuts back on AI boilerplate/jargon:
platform.claude.com/docs/en/…
Jellyfish lifecycles are the most incomprehensible thing evolution has ever produced.
When stressed, this comb jelly reverts to a larval form, then matures again when favorable conditions return.
Learn more: scim.ag/3UCeGdA #ScienceMagArchives