It is not. There are many biological examples of random processes generating patterns and weights, such tiger stripes, fingerprints, etc. DNA only encodes the reaction, but the pattern itself is seeded by initial position of random molecules, not the DNA.
> that it influences the initial state of the brain.
You've gone from "DNA is probably the seed for the random initialization", as in the deterministic seed for a PRNG, to "influences". Those are 2 different claims.
Most scientists will agree that DNA encodes the large-scale connectivity in the brain, ie from region to region, thickness of corpus callosum etc.
Most scientists will disagree that it is "probable" that DNA encodes/seeds the weights of each and every synapse.
My own reading of the poster's intent aligns with this: "DNA encodes the large-scale connectivity in the brain, ie from region to region, thickness of corpus callosum etc."
Most biologists will fully disagree with the whole premise, and support the reverse claim that we are born with random weights, we learn to walk/speak English/etc after we're born. Not from weights in our DNA.
At least Penrose had the good manners to make a relevant contributions (meaning Chomsky effectively managed to steer his field in entirely the wrong direction and nobody uses his contributions today in study)
Something that more or less guarantees you’ll get about the same capabilities as the original model, but with a different final expression of certain characteristics
I wonder if this could be a good distribution model too, selling “dried” models (almost like raisins - or the beings from the 3 body problem), and then you can buy them to fully train (raise?) with your own data
A random seed that works would be the exact opposite of that.
Not sure exactly what this means
But there’s prior art on the concept of compressing models through lossy representations
In particular, procedural generation of models, check out HyperNEAT (2009), HyperNetworks (2017) and Developmental Symbolic Encoding (2011)
It means that neural networks are a mistake, that they don't really have to be as complex as they are and animal minds could just calculate directly from the seed as opposed to building and powering this very expensive organ. The brain uses more power than any muscle in essentially all mammals and even more in humans. Only olympic-level efforts come anywhere close to power usage like the brain on a single muscle. So your body is really giving up something serious to let you think, to keep track of 100 Trillion weights and constantly evaluate against them.
This would be something very strange for DNA/evolution to do. Especially where it comes to energy use, if it can be done with 10W of power, you can bet your firstborn DNA will NEVER under any circumstances get caught using 10.01W.
I like your idea of "base" models for sale.
Or that infants with a rooting response survive much more often.
Basically, weights encoded biologically from hereditary behavior.
Instincts.
Now, the real conundrum is that if our DNA can really only encode ~12gb then how are we passing this information down?
FractalDB proves that "synthetic data useful for training AI" can be produced by a very small program.
There is "no room" in DNA for a full dataset, or a full snapshot of model weights. But there's plenty of room for "this one little trick cuts the data required to learn a desirable thing X in half". Low Kolmogorov complexity, but high performance gains.
Also as aspects of nature... Its not just genes for a system that can feel pleasure or pain but also a mapping from that system to a set of textures, temperatures, and frequencies that take that system beyond certain thresholds. Plus a large set of environmental inputs that cause the system to grow in different ways in response to different stimuli.