Darwin Machines

> This layering forms the dominant narrative of how intelligence may work and is the basis for deep neural nets. The idea is, stimulus is piped into the "top" layer and filters down to the bottom layer, with each layer picking up on more and more abstract concepts.

popular deep artificial neural networks (lstms, llms, etc.) are highly recurrent, in which they are simulating not deep networks, but shallow networks that process information in loops many times.

> columns.. and that's about it.

recommend not to oversimplify structure here. what you describing is only high-level structure of single part of brain (neocortex).

1. brain has many other structures inside basal ganglia, cerebellum, midbrain, etc. each with different characteristic micro-circuits.

2. brain networks are highly interconnected on long range. neurons project (as in send signals) to very distant parts of the brain. similarly they get projections from other distant parts of brain too.

3. temporal dimension is important. your article is very ML-like focusing on information processing devoid of temporal dimension. if you want to draw parallels to real neurons in brain, need to explain how it fits into temporal dynamics (oscillations in neurons and circuits).

4. is this competition in realm of abeyant (what you can think in principle) or current (what you think now) representations? what's the timescales and neurological basis for this?

overall, my take it is a bit ML-like talk. if it describes real neurological networks it got to be closer and stronger neurological footing.

here is some good material, if you want to dive into neuroscience. "Principles of Neurobiology", Liqun Luo, 2020 and "Fundamental Neuroscience", McGraw Hill.

more resources can be found here:

http://neuroscience-landscape.com/

Big-picture, the idea is that different modalities of sensory data (visual, olfactory, etc.) are processed by different minicolumns in the brain, i.e., different subnetworks, each outputting a different firing pattern. These firing patterns propagate across the surface area of the brain, competing with conflicting messages. And then, to quote the OP, "after some period of time a winner is chosen, likely the message that controls the greatest surface area, the greatest number of minicolumns. When this happens, the winning minicolumns are rewarded, likely prompting them to encode a tendency for that firing pattern into their structure." And this happens in multiple layers of the brain.

In other words, there's some kind of iterative mechanism for higher-level layers to find which lower-level subnetworks are most in agreement about the input data, inducing learning.

Capsule-routing algorithms, proposed by Hinton and others, seek to implement precisely this idea, typically with some kind of expectation-maximization (EM) process.

There are quite a few implementations available on github:

https://github.com/topics/capsules

https://github.com/topics/em-routing

https://github.com/topics/routing-algorithm

The domain of Artificial Life is highly related and has had an ongoing conference series and journal going, might be worth mining for more inspiration:

https://en.wikipedia.org/wiki/Artificial_life https://direct.mit.edu/artl https://alife.org

FYI Evolutionary Algorithms have been an active area of research for decades.[1]

Among the many uses, they have been applied to ‘evolving’ neural networks.

Famously a guy whose name I can’t remember used to generate programs and mutations of programs.

My recommendation if you want to get into AI: avoid anything written in the last 10 years and explore some classics from the 70s

[1] https://en.m.wikipedia.org/wiki/Evolutionary_algorithm

I think this is over-simplified and possibly misunderstood. I haven't read the book this article references but if I am understanding the main proposal correctly then it can be summarised as "cortical activity produces spatial patterns which somehow 'compete' and the 'winner' is chosen which is then reinforced through a 'reward'".

'Compete', 'winner', and 'reward' are all left undefined in the article. Even given that, the theory is not new information and seems incredibly analogous to Hebbian learning which is a long-standing theory in neuroscience. Additionally, the metaphor of evolution within the brain does not seem apt. Essentially what is said is that given a sensory input, we will see patterns emerge that correspond to a behaviour deemed successful. Other brain patterns may arise but are ignored or not reinforced by a reward. This is almost tautological, and the 'evolutionary process' (input -> brain activity -> behaviour -> reward) lacks explanatory power. This is exactly what we would expect to see. If we observe a behaviour that has been reinforced in some way, it would obviously correlate with the brain producing a specific activity pattern. I don't see any evidence that the brain will always produce several candidate activity patterns before judging a winner based on consensus. The tangent of cortical columns ignores key deep brain structures and is also almost irrelevant, the brain could use the proposed 'evolutionary' process with any architecture.

I don't think it matters so much how the brain is made, what matters is the training data. And we obtain data by searching. Search is a great concept, it covers evolution, intelligence and creativity, it's also social. Search is discrete, recursive, combinatorial and based on some kind of language (DNA, or words, or just math/code).

Searching the environment provides the data brain is trained on. I don't believe we can understand the brain in isolation without its data engine and the problem space where it develops.

Neural nets showed that given a dataset, you can obtain similar results with very different architectures, like transformer and diffusion models, or transformer vs Mamba. The essential ingredient is data, architecture only needs to pass some minimal bar for learning.

Studying just the brain misses the essential - we are search processes, the whole life is search for optimal actions, and evolution itself is search for environment fitness. These search processes made us what we are.

I’ve been noodling on how to combine neural networks with evolution for a while. I’ve always thought that to do this, you need some sort of evolvable genetic/functional units, and have had trouble fitting traditional artificial neurons w backprop into that picture.

My current rabbit hole is using Combinatory Logic as the genetic material, and have been trying to evolve combinators, etc (there is some active research in this area).

Only slightly related to the author’s idea, its cool that others are interested in this space as well.

Fantastic speculation here, explains a lot, and has testable hypotheses.

For example, there should be a relationship between rate of learning and the physical subcolumns - we should be able to identify when a single column starts up / is fully trained / is overused

Or use AI to try to mirror the learning process, creating an external replica that makes the same decisions as the person

Marvin Minsky was spot on about the general idea 50 years ago, seeing the brain as a collection of 1000s of atomic operators (society of mind?)

I read the followup:

Lingua ex Machina: Reconciling Darwin and Chomsky with the Human [2000]

https://www.amazon.com/Lingua-Machina-Reconciling-Darwin-Cho...

Completely changed my worldview. Evolutionary processes every where.

My (turrible) recollection:

Darwinian processes for comprehending speech, the process of translating sounds into phenomes (?).

There's something like a brain song, where a harmony signal echoes back and forth.

Competition between and among hexagonal processing units (what Jeff Hawkins & Numenta are studying). My paraphrasing: meme PvP F4A battlefield where "winning" means converting your neighbor to your faction.

Speculation about the human brain leaped from proto-language (noun-verb) to Chomsky language (recursively composable noun-verb-object predicates). Further speculation how that might be encoding in our brains.

Etc.

> These connections result in a triangular array of connected minicolumns with large gaps of unconnected minicolumns in between. Well, not really unconnected, each of these are connected to their own triangular array.

> Looking down on the brain again, we can imagine projecting a pattern of equilateral triangles - like a fishing net - over the surface. Each vertex in the net will land on a minicolumn within the same network, leaving holes over minicolumns that don't belong to that network. If we were to project nets over the network until every minicolumn was covered by a vertex we would project 50-100 nets.

Around this part I had a difficult time visualizing the intent here. Are there any accompanying diagrams or texts? Thanks for the interesting read!

This reminds me a little of Jeff Hawkins book, 1000 brain theory. His company numenta has done this kind of research and they have a mailing list. I'm not an expert but I've read Jeff's book and noodled at the mailing list

Correction: it is generally accepted that DNA was confirmed as genetic material in the Hershey-Chase experiment (https://en.m.wikipedia.org/wiki/Hershey%E2%80%93Chase_experi...), which predates the determination of the structure of dna by about a year

The image of the flattened out brain could use some illustrations, or more specific instructions on what we should be visualising.

> First, if you look at a cross-section of the brain (eye-level with the table)

I thought it was flat on the table? Surely if we look at it side-on we just see the edge?

Without a clear idea of how to picture this, the other aspect (columns) doesn't make sense either.

There's lots of room for cross-pollination between bio/life sciences and ML/AI. One key insight is the importance of what you pick as your primary representation of data (is everything a number, a symbolic structure, a probability distribution, etc). I believe a lot of these bio-inspired approaches over-emphasize the embodied nature of intelligence and how much it needs to be situated in space and time, which downplays all the sub-problems that need to be solved in other "spaces" with less obvious "spatiotemporal" structure. I believe space and time are emergent, at least for the purposes of defining intelligence, and there are representations where both space and time arise as dimensions of their structure and evolution.

The book "Cerebral Code" is made available for free by the author on his website: http://williamcalvin.com/bk9/

For a more modern treatment on the subject, read this paper: An Attempt at a Unified Theory of the Neocortical Microcircuit in Sensory Cortex https://www.researchgate.net/publication/343269087_An_Attemp...

I took more notes on this blog post than anything else I've read this month.

This project employs a Darwinian approach. Initially, it was an experiment in traditional program and user interface generation that incorporated evolutionary feedback into the mutation process. A combination of PG and AL. It has achieved some success with small programs and is now exploring the potential combination of LLMs

https://youtu.be/sqvHjXfbI8o?si=7qwpc15Gn42mUnKQ&t=513

I don't think this is true as stated. Evolutionary algorithms are not the most efficient way to do most things because they, handwavily, search randomly in all directions. Gradient descent and other gradient-based optimizers are way way faster where we can apply them: the brain probably can't do proper backprop for architectural reasons but I am confident it uses something much smarter than blind evolutionary search.

I think in some ways by considering the brain as a Darwin Machine, we can explore new dimensions of how our minds work

Nitpick: lots of text descriptions of visual patterns - this article could use at least 5 visual aid images.

This post analogizes between a specific theory of human intelligence and a badly caricatured theory of evolution. It feels like better versions of the arguments for Darwin Machines exist that would not: a) require an unsupportable neuron-centric view of evolution, and b) don't view evolution through the programmers lens.

> Essentially, biology uses evolution because it is the best way to solve the problem of prediction (survival/reproduction) in a complex world.

1. This is anthropocentric in a way that meaningfully distorts the conclusion. The vast majority of life on earth, whether you count by raw number, number of species, weight, etc. do not have neurons. These organisms are of course, microbes (viruses and prokaryotes) and plants. Bacteria and viruses do not 'predict' in the way this post speaks of. Survival strategies that bacteria use (that we know about and understand) are hedging-based. For example, some portion of a population will stochastically switch certain survival genes on (e.g. sporulation, certain efflux pumps = antibiotic resistance genes) that have a cost benefit ratio that changes depending on the condition. This could be construed as a prediction in some sense: the genome that has enough plasticity to allow certain changes like this will, on average, produce copies in a large enough population that enable survival through a tremendous range of conditions. But that's a very different type of prediction than what the rest of the post talks about. In short, prediction is something neurons are good at, but it's not clear it's a 'favored' outcome in our biosphere.

> It relies on the same insight that produced biology: That evolution is the best algorithm for predicting valid "solutions" within a near infinite problem space.

2. This gets the teleology reversed. Biology doesn't use anything, it's not trying to solve anything, and evolution isn't an algorithm because it doesn't have an end goal or a teleology (and it's not predicting anything). Evolution is what you observe over time in a population of organisms that reproduce without perfect fidelity copy mechanisms. All we need say is that things that reproduce are more likely to be observed. We don't have to anthropomorphize the evolutionary process to get an explanation of the distribution of reproducing entities that we observe or the fact that they solve challenges to reproduction.

> Many people believe that, in biology, point mutations lead to the change necessary to drive novelty in evolution. This is rarely the case. Point mutations are usually disastrous and every organism I know of does everything in its power to minimize them. Think, for every one beneficial point mutation, there are thousands that don't have any effect, and hundreds that cause something awful like cancer. If you're building a skyscraper, having one in a hundred bricks be laid with some variation is not a good thing. Instead Biology relies on recombination. Swap one beneficial trait for another and there's a much smaller chance you'll end up with something harmful and a much higher chance you'll end up with something useful. Recombination is the key to the creativity of evolution, and Darwin Machines harness it.

3. An anthropocentric reading of evidence that distorts the conclusion. The fidelity (number of errors per cycle per base pair) of the polymerases varies by maybe 8 orders of magnitude across the tree of life. For a review, see figure 3 in ref [1]. I don't know about Darwin Machines, but the view that 'recombination' is the key to evolution is a conclusion you would draw if you examined only a part of the tree of life. We can quibble about viruses being alive or not, but they are certainly the most abundant reproducing thing on earth by orders of magnitude. Recombination doesn't seem as important for adaptation in them as it does in organisms with chromosomes.

4. There are arguments that seem interesting (and maybe not incompatible with some version of the post) that life should be abundant because it actually helps dissipate energy gradients. See the Quanta article on this [0].

[0] https://www.quantamagazine.org/a-new-thermodynamics-theory-o... [1] Sniegowski, P. D., Gerrish, P. J., Johnson, T., & Shaver, A. (2000). The evolution of mutation rates: separating causes from consequences. BioEssays, 22(12), 1057–1066. doi:10.1002/1521-1878(200012)22:12<1057::aid-bies3>3.0.co;2-w

This strongly reminds me of the algorithm used by swarming honeybees (if anyone's interested I'd highly recommend reading honeybee democracy). I reckon there's something to this.

I might have a go implementing something along these lines.

The title of the referenced book by Erwin Schrödinger is “what is life”, I believe.

https://archive.org/details/whatislife0000erwi

I'm obsessed with the idea of Darwin Machines (and I think you should be too).

I've been tinkering with the idea in python but I just don't have enough ML experience.

If you, or anyone you know, is interested in Darwin Machines please reach out!

There is a lot of quibbling over details, but this is a 1-2 page high level elevator pitch, so will have some things glossed over. To that end, it seems like some interesting concepts for further exploration.

Oh dude this is so cool. I think you’re dead right.

If you’ll pardon some woo, another argument I see in favour of message passing/consensus, is that it “fits” the self similar nature of life patterns.

Valid behaviours that replicate and persist, for only the reason that they do.

Culture, religion, politics, pop songs, memes… “Egregore” comes to mind. In some ways “recombination” could be seen as “cooperation”, even at the level of minicolumns.

(Edit: what I mean to say is that it kinda makes sense that the group dynamics between constituent units of one brain would be similar in some way to the group dynamics you get from a bunch of brains)

isn't this the same as genetic algorithms ?