Lisbon
Joined January 2017
Michael Schartner retweeted
The outpouring of interest in our paper has been … a bit insane. May I make four points? 1. We have not shown that “consciousness has a frame rate.” I can see why people draw this conclusion: we report that inferotemporal and prefrontal cortex alternate between conscious and suppressed states roughly every 60 ms during binocular rivalry. Consciousness depends on many brain areas, and our paper reports results from only a few select nodes. We still don’t know how conscious access works--the actual recipe for generating conscious experience. What we have shown is that deep in the brain there is an extremely strong signal, rich enough to reconstruct a high-dimensional face in detail, that is inaccessible to consciousness. As I argued in the original tweet, this makes the problem of conscious access a lot more experimentally tractable. But to claim “consciousness has a frame rate,” one would need to have actually solved the mechanism for conscious access and shown that this mechanism operates at a frame rate. While we currently don’t know conclusively one way or the other (see VanRullen & Koch, 2003 for a review of positive evidence), I personally am skeptical that consciousness has a frame rate. I believe the brain, within posterior parietal cortex, has continuous tracking mechanisms that actively represent the persistence of matter, including ourselves as physical bodies. The coupling between these persistent tracking processes and feature representations across the brain (like those in face patches) is, I think, the essential mechanism of conscious access. This coupling need not happen at the rate of inferotemporal alternations (and inferotemporal cortex itself may not even alternate outside of the artificial situation of binocular rivalry). As an analogy: imagine the area crucial for conscious access holding a steady drone note, while inferotemporal cortex alternates between two notes, one in harmony and one dissonant. Inferotemporal content might couple to the drone only at moments of harmony, yet experience would still be continuous, because the drone itself (the tracking process that I believe is at the core of conscious perception) never stops. And areas without conflicting content, like auditory cortex during visual rivalry, could stay in continuous harmony with the drone. In sum, there is no reason to conclude from our study that consciousness is discrete, with a "frame rate." 2. Our results resemble Jezek et al. 2011 (as @jjsakon notes). Jezek et al. trained rats in two boxes that were identical except for their lighting cues, so that hippocampal place cells formed two distinct maps, one per box. They then “teleported” the rat by abruptly switching the lights. For a few seconds afterwards, CA3 place-cell activity flickered between the two maps, synced to the theta cycle (~125 ms). @StanDehaene first pointed this paper out to me, and reading it blew me away. To my mind it is the strongest evidence for a bistable attractor in the brain, because the competing maps are stored within the hippocampus itself. I think the phenomenon we see may be very similar at the circuit level: a bistable attractor network within inferotemporal cortex that alternately converges to one of two patterns, as cells in face patches work together to build a coherent representation of the input. In our case, both images are present in the input, so the competition could arise within IT or be inherited from earlier areas such as V1--recording there is an important next step. (It also still puzzles me that we didn’t see strong coherence between face patches ML and AM.) More broadly, what’s different about our paper is the brain area (inferotemporal cortex, often modeled as a feedforward DNN) and the ability to relate the two competing patterns to consciousness, with one granted conscious access and the other not. 3. Please comment on bioRxiv! The Twitter attention has been cool, but I really hope colleagues will be generous enough to post detailed commentary on bioRxiv, where there are currently 0 comments. As @seemaychou points out, peer review doesn’t have to come from just three anonymous reviewers. We would be truly grateful for your critical feedback. 4. Stay tuned This work was done at Caltech, Berkeley, in collaboration with @EngelTatiana at Princeton, funded by NIH and HHMI. I hope to share our vision soon for how we can solve consciousness @AsteraInstitute
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Michael Schartner retweeted
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness. New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
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Michael Schartner retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Michael Schartner retweeted
1. CRISPR came from basic research on bacteria. 2. Optogenetics started with a discovery in algae. 3. The GFP protein comes from jellyfish. The biggest breakthroughs all trace to scientists follow their curiosity. This is one huge reason why we must keep funding basic research!
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Michael Schartner retweeted
Replying to @KrzakalaF
Au contraire. C'est une nouvelle ère qui s'ouvre pour les mathématiques. Une ère où la démonstration formelle est largement automatisée et où l'accent sera reporté sur le développement de nouveaux concepts, nouvelles abstractions, nouvelles définitions, et nouvelles conjectures. L'invention du bateau a réduit l'importance de la nage, mais a permis la découverte de nouvelles terres.
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Michael Schartner retweeted
Replying to @ShahabBakht
If someone had simply published an academic paper on transformers, no one would have cared. The impact has come from scaling it to solve actual problems. In neuroscience we similarly tackle problems at such a small scale (describing the neural correlate of percept X or task Y) that it's hard to make people care. It's hard to even know if any proposed algorithm is right. But I feel so inspired by what's happened in AI...if we also scale our efforts and aim for understanding the *full structure of the brain's model of world and self*, there actually is hope to solve the entire problem in our lifetime...and when we do, we will not only figure out intelligence (as LLMs have), but consciousness. What is the brain's algorithm for tracking a persistent piece of matter? What is the brain's algorithm creating programs with variable arguments? What is the brain's data structure for representing "I"? I am dying to know. Re the neural foundation model program: I think intelligently stitching data together across sessions and subjects is important, and foundation models are a good tool for that. But I absolutely disagree that they are an answer to understanding the brain.
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Michael Schartner retweeted
You don't have to commit your entire life to optimism, just try it on for a week. Start thinking "what if it all worked out?" and "what a time to be alive!". Then compare your state of mind to the week before filled with pessimism and doomer nonsense.
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Michael Schartner retweeted
Lots of people are asking me how to register - you don't need to. Just show up.
I’ve organized many conferences before, but I’m extra happy to invite you to this one🥶. It’s special: Francis Crick controversially proposed that the genetic code was a “frozen accident”: a chance choice that became difficult to change because so much had evolved around it. Join us on November 20th in Paris, at Galerie Colbert (INHA), for a conference organized by HiCSA_UP1 SorbonneParis1, to explore this idea across art, science and culture. It matters in very different areas, from the marble Michelangelo used to sculpt David to borders between countries. The choices we’re making now about AI and energy might become frozen accidents that future generations struggle to change. Was Crick wrong? Do frozen accidents really exist? What role does chance play, and what would it take to unfreeze them? We have amazing speakers, including Nick Lane, @Biol4Ever, @SefyHendler and Carolina Mangone, and many others (see poster). Please share this widely, especially with people outside your field. Be there or be frozen!!!
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Michael Schartner retweeted
Very excited to announce Gemini 4 Argon, our new Frontier AI model, and a huge step forward across key capabilities. We’re focused on rolling it out responsibly starting with government and trusted cyber defenders through our Fairwind Program today, before wider availability soon
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Michael Schartner retweeted
SpaceX has released stunning shots from today’s orbital Starship launch 🤩
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Michael Schartner retweeted
BREAKING 🚨: Starship has reached orbit for the first time View from Starbase, Texas moments ago
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Michael Schartner retweeted
Friendly reminder: we are hiring a Full Professor👇. Get in touch if you have any questions.
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Michael Schartner retweeted
Gripping
Whoever gets 3 cones is the winner 👌🏾❤️
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Michael Schartner retweeted
I don’t necessarily believe AI systems have subjective experience. But I don’t understand how anyone can be certain that they don’t. The truth is that nobody knows what makes consciousness. People have different opinions but there is no definitive answer yet.
Artificial intelligence systems do not think, feel, want or understand. Avoid language that gives them human characteristics. This is called anthropomorphizing, when we ascribe human traits, emotions or behaviors to non-human things, such as animals or inanimate objects. Instead, explain what a system does, how well it performs, who built it and who could be affected by it. apnews.com/article/openai-sa…
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Michael Schartner retweeted
We have to defeat doomerism. Rage, rage against the dying of the light. I stand on the side of a humanity armed with strong AI.
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Michael Schartner retweeted
Leo Tolstoy has the best diary entries ever. Sept 19th, 1858. “Decided that one must love and work, and that’s it.” Sept 20th: “Tired. Didn’t love and didn’t work.” March 31st, 1852. “Woke up at 6 o’clock, woke everyone up; I did not get up and slept until 9.” January 25th 1851 “I’ve fallen in love or imagine that I have; went to a party and lost my head. Bought a horse which I don’t need at all.”
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NASA releases stunning new view of Mars The new Curiosity rover “postcard” celebrates its 5,000th Martian day, combining panoramas captured on the Red Planet in late August and early September
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Michael Schartner retweeted
Here’s what I’m watching tonight: 4 hours of Figure 03 robots doing home chores. Long, uncut scenes of housework in 30 different homes. Figure introduced Helix 2.5, its most advanced humanoid AI yet, built to answer one question: can a humanoid walk into a home it has never seen and get to work on its own? The setup One foundation model pretrained on Index, Figure’s global dataset of human behavior, then fine-tuned into three whole-body behaviors: tidying living rooms, folding towels, and making beds. Tested across 30 Bay Area homes with zero data collected in any of them, and with objects it had never seen. Results • 56% zero-shot success vs. 9% trained from scratch. Index pretraining was the only variable. • No partial credit in success rate. Every toy in the basket, every towel folded, the whole bed made. • Half the task data of a comparable Helix 02 behavior, and 30× wider generalization. • A human-to-robot transfer scaling law: doubling Index data improved action prediction predictably enough to forecast the largest run’s loss to four decimals. • Whole-body self-correction. The robot steps back, repositions, and walks around the bed to fix a fold. Index now generates ~35 minutes of new human experience every second, and Figure has committed $3.5B of compute to Helix.
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Michael Schartner retweeted
1/3 Conscious AI and Biological Naturalism. A veeery loooong time in the making, but I'm happy to say that the full collection is now out in @BBSJournal 🔥 I deeply thank all commentators - of fifty commentaries! - for their wise, broad, and deep opinions on this critical topic. I also thank them for their saintly patience. 🙏🏽 I remain highly skeptical about the prospects for conscious AI (for silicon digital systems), but I've learned an enormous amount from the privilege of engaging with these commentaries. Hands down one of the most rewarding experiences of my professional life. My hope is that - whether you agree with me or not - having such a diversity of views collected in one place will serve a useful resource for discussions about consciousness and AI. The original target article is available here: cambridge.org/core/journals/… My response "The stuff matters: consciousness, computation, and biology" was published just now, and is here: cambridge.org/core/journals/… And the set of 50 commentaries are collected here: cambridge.org/core/journals/… Unfortunately these are not open access. I will try to remedy that, at least for the target & response. FWIW there's a more accessible version of my view on the topic here in @NoemaMag noemamag.com/the-mythology-o… and also a short @TEDTalks from earlier this year ted.com/talks/anil_seth_why_…
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Michael Schartner retweeted
Thrilled that #paper2agent is published in @nature today! Scientific knowledge is traditionally stored in passive papers. Paper2Agent transforms papers into virtual authors that answer questions, apply its methods and collaborate w/ other paper agents to make new discoveries 🧵
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