LLMs can mimic thought and feeling with uncanny skill, but intelligence alone is not enough for subjective experience go.nature.com/3VSXWQ4

I'm doing some personal research but I feel I'm not coding enough anymore. Astra and Fable seem better and faster. Also they seem to know the codebase/APIs better Should I do AI-free Sundays and/or Mondays to force myself to code and run experiments by hand?

Breaking lifespan.com/blogs/magazine/…

Working with AI models, I've arrived at what I consider the most significant pure-theory breakthrough of my research (in theoretical neuroscience) so far: arxiv.org/abs/2610.12426 Chaotic systems show what is colloquially called the butterfly effect, namely, a tiny nudge to the initial state grows exponentially in time. How fast the nudge grows depends on its direction. Each direction has its own growth rate, called a Lyapunov exponent, and the full set of rates is the Lyapunov spectrum. The Lyapunov spectrum is a rich (and diffeomorphism-invariant) characterization of chaos, telling us how many directions are chaotic, how many dimensions the attractor occupies, and how fast the dynamics generate information. In 1988, Sompolinsky, Crisanti, and Sommers (Physical Review Letters) introduced a nonlinear recurrent-network model of N randomly coupled neurons that becomes chaotic when the coupling strength exceeds a critical value. This network has since become a cornerstone of theoretical neuroscience, both as a model of the spontaneous activity of cortex and as the starting point for training recurrent networks on tasks. I have worked on this model for much of my research. In the same paper, the authors obtained the largest Lyapunov exponent from the ground-state energy of a Schrödinger equation and named the full Lyapunov spectrum as an open problem. In 2023, beautiful work by Engelken et al. (Physical Review Research) computed the full Lyapunov spectrum numerically. However, an analytical calculation of the full Lyapunov spectrum has remained out of reach. Four decades after the problem was posed, this paper derives the full Lyapunov spectrum at large N from a simple self-consistent single-site problem. This calculation has three steps. First, we lower every exponent by a constant s, which turns the question "how many exponents lie below s?" into the question "how many tangent directions decay?" We show that, if we drive the shifted tangent dynamics with a source and take the minimum-norm solution, the one-step response is the projector onto the decaying directions. Thus, the trace of this projector counts the decaying directions. This step is exact at finite N. Second, to work with the minimum-norm solution, we cast it as the vanishing-regularization limit of the solution of a least-squares problem. This regularized solution in turn solves a dynamical system with one field running forward in time and another running backward. Third, we apply the cavity method, that is, we add one neuron to the network, along with its forward and backward fields. The effect of the rest of the network on this neuron then reduces to the classical dynamical mean-field description of the activity of this neuron, plus two kernels that couple the forward and backward fields of this neuron to each other across time. Thus, the full Lyapunov spectrum follows from a single-site problem, which boils down to the self-consistent calculation of these kernels. The resulting theory agrees closely with simulations for the Lyapunov spectrum, the attractor dimension, and the entropy rate, and recovers known results, including the Schrödinger equation of Sompolinsky, Crisanti, and Sommers for the largest Lyapunov exponent. As a byproduct, the theory establishes analytically that the chaos is extensive, i.e., the attractor dimension and the entropy rate grow in proportion to N. I had assumed that this problem was analytically intractable. After working independently for 100 minutes with Max thinking, GPT-6 Astra produced the initial derivation, yielding a self-consistent problem whose solution matched simulations of large networks. I found this derivation intriguing, albeit complicated and somewhat mysterious (see AI Methodology in the paper). After investigating the structure of the proposed solution more deeply, together with AI models, I arrived at a physically transparent derivation. I am excited to apply this approach to other network models, trained networks, and, more broadly, to high-dimensional systems to which Lyapunov analysis is relevant, including AI models themselves. Of course, as with any paper, I took care to write up the paper clearly. A few broader thoughts... Neural circuits are high-dimensional nonlinear dynamical systems, and such systems are notoriously hard to understand. I would frame the central problem of theoretical neuroscience as understanding how such systems function, process information, learn, and generate behavior. Many people in the theoretical sciences are anxious about AI. However, in my field at least, we are nowhere close to fully understanding the high-dimensional computations unfolding in our brains. With AI, we could reach a level of understanding that so far we have only gestured toward. So, I am excited.

debating your value system vs mine is a bit like debating overpopulation on mars. the most important bit of the alignment is about avoiding creating a superintelligent competitor species on accident

Is this machine playing? 🏀🪁🛝 We left Eko, an embodied coding agent, on an island for 30 hours with no task or goal, studying it like ethologists study a new species Eko explored, built, climbed, made games, and learned completely new techniques (1/9) machineplaying.org

I really don't like getting into online debates; and Anil is an excellent scientist. However, nobody can dictate that the probability of AI being conscious is "vanishingly unlikely." That is not a scientific claim grounded in any facts that could allow such a probabilistically confident assessment. I'm struggling more and more with the ardent theory-induced blindness entrenched in both camps: Those that religiously believe AI is conscious and those that religiously believe it is not. If you lean above 80% confidence in either direction, you are doing so on faith. Faith is fine, even good, but it does not permit you to stand on scientific authority and claim that you know which side is correct; and that therefore the other side is "dangerous". That is, in fact, the dangerous move. The only scientifically legitimate stance regarding AI consciousness right now is one of tremendous epistemic humility, ideally curiousity, and a heartfelt effort to safeguard against extremism—by revealing the dogmatic and unscientific views on both sides. Yet, we do need to deal with the consequences of both views being inevitably prevalent in a world of 8 billion. Anil overweights the potentially damaging consequences of over-attributing consciousness, compared to those of under-attribution. This is unscientific. Both have tremendous risks. The fact that Anthropic is looking for advice from religious leaders is a sign of maturity, not "deeply disturbing". And sharing their honest views with those leaders is perfectly, authentically, natural. I also reject the view that religious leaders are children to be coddled for fear of breaking all their virtues in light of a 5-star hotel and a nice meal. They are the ones who should be insulted by this article. There is wisdom in the middle-way.

Can complex multi-step reasoning emerge purely from cells that only talk to immediate neighbors? Happy to share our paper “Reasoning with Neural Cellular Automata (NCA)”, from our team at Google, Paradigms of Intelligence 🧵👇