The human genome contains around 1.5GB of information and DeepSeek v3 weighs in at around 800GB, so it's a bit apples-to-oranges. As you say, what's been evolved over hundreds of millions of years is the learning apparatus and architecture, but we largely learn online from there (with some built-in behaviours like reflexes). It's a testament to the robustness of our brains that the overwhelming majority of humans learn pretty effectively. I suspect LLM training runs are substantially more volatile (as well as suffering from the obvious data efficiency issues).

If you'd like an unsolicited recommendation, 'A Brief History of Intelligence' by Max Bennett is a good, accessible book on this topic. It explicitly draws parallels between the brain's evolution and modern AI.

The comparison is weird as we don't think with the Genome. There are something like ~100 billion neurons with ~100 trillion connections in an adult human brain . I don't know how many bytes of sourcecode deepseek has, but I don't think it helps in determining the amount of reasoning it can do.

> The comparison is weird as we don't think with the Genome

The genome determines how your brain learns, so yeah we do. We don't solve short easy tasks via learning, no, but longer tasks that involves learning involves our DNA.

> longer tasks that involves learning involves our DNA

Longer tasks that involve learning also involve caloric consumption and respiration, that doesn't mean we think with the sun and the air.

No. Not in any way, no.

Learning is a physical process in which neurons form new connections to one another. It has nothing to do with DNA.

This is like measuring LLM performance based on CPU microcode size. Completely nonsense.

Learning also happens on the species level. The species "learns" (thru natural selection) which genes produces brain structures that lead to survival and reproduction.

The human genome isn't its own thing, the genome as a static sequence is really just an abstraction. What actually functions as the heritable unit includes epigenetic marks, non-coding RNA regulation, 3D chromatin structure, and mitochondrial DNA. In the real biological world there are very few sharp edges - systems bleed into one another and trying to define something like 'the number of bits in the human genome' is very difficult, but it's undoubtedly way bigger than you posit here.

> The human genome contains around 1.5GB of information and DeepSeek v3 weighs in at around 800GB, so it's a bit apples-to-oranges.

The apples-to-apples comparison is comparing the human genome to the code behind a particular LLM. The genome defines the structure that learns and thinks, just like the code for the LLM.

And that same information contained in an LLM is a compression of how many terabytes of training data? Maybe in the future there will be models an order of magnitude smaller and still better performing.

What I'm saying is you can't judge the data in the genome by purely counting the bytes of data.

Also interesting to consider how much "compute" has to be spent by humans are learning something like that. Like, do we need to see more examples if learning from pictures of cats and dogs than seeing them in person? How many more examples? What if we're seeing them all in sequence, or spread out across hours or days?

I've probably seen... at least a dozen pictures of aardvarks and anteaters and maybe even see one of them at the zoo but I don't think I could reliably remember which was which without a reminder.

If you see one picture of a zebra, fly to Africa, see a real zebra, you recognize it as a zebra. But zebras are really unmistakable.

If you see a picture of an oryx and a picture of a kudu, maybe you remember the shape of their horns and a picture is enough.

Enter waterbucks and steenboks. That starts to require a little more training.

Go all the way from mammals to insects. Bees and wasps and ants are still in the one picture is enough category. But what species of ants those on the wall of my house belong to?

I believe that ease of detection depends on how much things stand out on their own. Anyway, we do use a fundamentally different way of training than neural nets because we don't rebuild ourselves from scratch. However birds and planes fly in totally different ways but both fly. Their ways of flying are appropriate for different tasks, reach a branch or carry people to Africa to look at zebras.

Humans can learn to recognize the difference between male and female newborn chickens, not sure if you can train an AI to do that since we humans don't know how we tell the difference we just learn how to by practicing enough. It is a skill any human can learn quite quickly, it isn't hard we just don't know how it works.

i think evolution meta-learns the architecture, hyperparams. some domain knowledge too (for ex, we all perceive the world as 3d) but not much. if you compare the text consumed by human vs AI (and i think this is fair b/c even with evolution text is a pretty recent invention for humans), the gap is many orders of magnitude.

Tangentially, some scientists think humans may have hardwiring for detecting snakes https://en.wikipedia.org/wiki/Snake_detection_theory

I think my toddler saw roughly 100 dogs and cats before she was able to reliably tell the difference.

That happened at toddler stage of brain development and of knowledge buildup.

Let's suppose that you meet adults that never saw cats and dogs. You show them a picture a cat and a dog. Do you expect that they need to see 100 of them before telling the difference?