CEO @Bandago, Commissioner @SF Homeless Oversight Commission, music: @creeperlagoon, dad, husband, optimist. Posts auto-delete: I'm a person, not training data.

San Francisco
Joined April 2007
Nope. The reason: computational irreducibility. Let’s take Alphafold: it predicts the likely final structure of a protein, but not all intermediate structures. It’s a shortcut, not a complete simulation. There may be more shortcuts in the future, but some things are irreducible.
If you had an exponential amount of compute you could solve simulate biology exactly and wouldn't need to run any experiments. Some of you will think "of course, but that's an unrealistic assumption" and others will think "no, you need experiments even with that much compute. That's just How It's Done". You would of course need evaluations once you have a solution, but you don't in principle need experiments to find it: Experiments are a way to save compute, dating from a time when it was much more expensive. And I think the presentation is exponential but real systems aren't irreducibly exponential. AlphaFold worked, after all.
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Say you want to simulate a human embryo, from fertilization to birth, with accuracy. You need more than the laws of physics & compute. You also need relevant initial conditions, including molecular configs, environmental influences, and possibly stochastic quantum events.
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Smart people, particularly smart STEM people, tend to over-extrapolate. The real world is not that stable. The farther you move from your starting point, the more impact latent variables have. Latent variables are invisible at the start, that's why they are called "latent"!
Dario Amodei was so worried about Y2K that his high school teacher still remembers it decades later. He’s been a doomer for much longer than I thought!
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This is why we'll never be able to predict the weather a month in advance. You could put sensors across every inch of the planet and latent variables in the spaces between those inches will wind up disrupting the prediction.
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Noteworthy
At 7:30 PM Central Time, Dallas County has recorded 75,834 votes, which is 232.6% of its 2022 first-day total and 134.6% of its 2024 first-day total. Dallas has surpassed Presidential-year turnout on the first day of early voting in a midterm year.
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The Nathan Fielder movie is a wild ride with a jump scare, plot twists, a few paradigm shifts, and a score I adored. Must see.
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Update
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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Stunning breakthrough
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Blinking guy meme
Trump: I stood right over here and I examined his ass. I never did that. No, I never did that with a man before. I examined his ass… My hand is still recovering.
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People *PLEASE* do not spoil the movie.
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Discovery: the opener, Jackie Evans, is a major talent. Looking forward to hearing more. @vulfpeck
Vulfpeck slaying The Greek tonight
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Vulfpeck slaying The Greek tonight
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This is concerning. I was appointed the US EV Working Group (created by congress in 2022), but the group has not met since the 2024 election. I would very much like for those meetings to resume. There is work to be done.
Electric vehicle adoption is accelerating worldwide – but not in the United States:
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Curious about the US EV Working Group? driveelectric.gov/ev-working…
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This is extremely good news, and underscores what I've been saying since ~2018: you can't extrapolate all the bad things that could plausibly happen from AI, without ALSO extrapolating all the ways that AI could be used to address those bad things.
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works already-clean.goodfire.com/
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