Imo, there isn't anything fundamentally special about the brain as a cognitive substrate. Even a pool of water could have cognition (reservoir computing), i feel that it's a general property that can emerge in any sufficiently high-dimensional physical system with enough internal degrees of freedom to explore many configurations, and enough coupling and stability to remain coherent, and enough plasticity for experience to leave persistent changes in its future dynamics.

And subjecting such a system to sustained external objective pressure...where some internal organisations perform better than others and it would progressively accumulate some useful structure about its environment.

The brain is just a really old artefact of billions of years of blind environmental weathering of matter being repeatedly shaped by the requirement that its host continue to exist as a coherent, persistent entity.

In short the brain is not fundamentally special in the principles that make cognition feasible.

What i'm trying to say in response to this article is that he's working of a far higher level of abstraction than is required. It's putting software before physics.

I think the question and article is fascinating. I do think that one should not only look at the genetic representation, and the algorithm a cell runs, but also the formalized environment that runs the simulation.

I’ve been thinking about this for a few years and I truly believe these cells need to be able to “move” in either a 3d lattice similar to a cellular automata or become nodes in some cyclic graph (or maybe both).

For the genetic representation, I had an insight that DNA/RNA looks strangely similar to SKI calculus (combinators) and have been using a Church encoding to translate the SKI program to an “action” per turn (move, connect, spike, divide, etc.). It has a nice property that the cell’s “program” and the spikes between them, and the input/output to the simulation itself can be the same thing (just a string of combinators).

Either way the author is spot on when it comes to this sort of thing having the properties 1) “always on” (no separate training mode), 2) local interactions, 3) everything stems from a genome, 4) each cell is its own little mini-program with the same genetics as everyone else.

All I can say is that evolutionary program is hard and takes either massive compute or large timescales to run the simulations. Fascinating stuff though

> The program must fit in a genome-sized instruction set of about 1 gigabyte

True but this can be a lot larger, maybe even a 1 exabyte instruction size since it depends on he programming language used to express the program. And even accounting for the invariance of runtimes at scale*, the constant factor might be gigantic since we have to build up a lot of cellular machinery first. Reminds me of the Carl Sagan quote: To make a sandwich,you must first construct the universe!

* For example, to translate a program from language A to language B, you can dedicate a constant size to write a language A to B translator. Thus at large program sizes , the kolmogorov complexity (i.e instruction set size) is fairly similar between programs since the size of the program dominates the size of the translator program which is constant. But the constant factor for cellular machinery might be gigantic**

** Or not if we only need a rough simulation

One aspect missing from TFA is that in biological brains a lot of the training is performed during query time. If we assume 1 GB of DNA is enough to encode the brain's overall structure we still do need training data (e.g. visual/auditorial/tactile) to build out the strength of the synaptic connections.

You'll need a simulated environment because brain development depends a lot on the environment, it's not purely genetic.

It's if statements all the way down, except you run all of them in parallel and then flip a coin. Let me get my keyboard.

The human genome is essentially a billion-year-old quantum engineering document that we can read but can’t “yet” write.

Asimov’s positronic brain was the conceptual framework with the wrong formula.

Jeff Hawkins and his team have been trying to understand human brain and postulate a theory of intelligence based on neocortex structure.

This isn't quite the question the article is asking, but if you expand it just a bit to a brain rather than a simulation of a biological brain specifically, the specification complexity of a modern AI is not that large. You can get a good idea of the foundation of the system with some 3blue1brown videos. While just watching those and deeply understanding isn't enough to score a job at a frontier AI lab, I'd expect that a lot of what the labs do is similarly not that complicated to specify... it's coming up with the ideas, tuning them, and then trying to test them at the now-staggering scale it takes to prove them out for commercial use that is most of the issue.

The complexity of the AI itself, the final weights, is vastly higher, because that incorporates all the data that was flowed through the "brain".

In a decent encoding you could probably still fit the initial state of the frontier models comfortably in just a few kilobytes of code-golfed code, including the update functions and every necessary for the actual training. Recall the specification complexity is the smallest program that can create the initial state, not the initial state itself; the specification complexity of "give me a trillion 64-bit numbers generated from this psuedo-random number generator" is on the order of that very English sentence in size, not 8 tebibytes of specification complexity.

Is the result a biological brain? Obviously not. Biological brains do not do what the LLMs are doing. But unlike someone asking this question 20 or 30 years ago, where "yeah, but what if some other architecture could work too?" was still largely hypothetical, and neural nets were still largely toys, now it isn't. We may argue if an LLM is as smart as a human but I would say that at least on its playing field it is quite clearly smarter than most biological brains in existence on most measures we care about. I have to qualify "on its playing field" because it is fundamentally a text completion engine, so for instance even very very tiny biological systems still beat it on things like "ability to drive an ant body around to do useful tasks". That's a separate field of AI right now. Stay tuned on that matter, but it's not how things work today for sure.

There is still something to what biological brains do that we are not matching; as I like to say, humans do what they do without the entire contents of the internet being poured through their head multiple times over. But the idea that maybe we don't have to exactly match biology to get something useful is no longer just a theory.

I have this science fiction story in my head that consciousness can evolve from organic to mechanical and back to organic as it is perhaps the most energy efficient method to encode complexity.

"Consciousness" is tick based and it does not require a massive human brain. Even the smallest of animals react in real time to stimulus. There's a filter that lowers the amount of tokens the next tick has to process. That's why you can watch a video and two different people will notice entirely different things about it. Forgetting is our brains culling what they process. Being awake eventually degrades that process. You can see this in every study on tokenized llms showing a decrease in accuracy as the context fills up. So too does your brain. That is why we sleep.

Short answer yes. We can build a brain.

As for quantum effects. It's just the machine. Not the mind itself. It's sort of like asking how a CPU works and then expecting that to inform your science on cognition.

there is a theory by the brilliant Roger Penrose and extended by others that the brain is just not neurons but also quantum effect

which would make it impossible to emulate via current technology

excellent PBS Space Time on that

* https://www.youtube.com/watch?v=xa2Kpkksf3k