If those tokens were to be passed on they'd have to be encoded somewhere, like DNA. And in DNA, there's no room. So ... where are they?
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" reproduced thousands of times for different X. 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.
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 seeded from our DNA.
At least Penrose had the good manners to make relevant contributions (meaning Chomsky effectively managed to steer his field in entirely the wrong direction, take everybody along with nothing to show for it. Nobody uses his contributions today in study. Yes he's celebrated ... but why? He's like a prime minister that presided over a country causing an economic crisis, throwing everyone into misery. I guess those are exactly the kind of politicians celebrated. Bush is celebrated while he destroyed half the economy, Clinton is forgotten (well, except for that one thing) despite being the best president of the last 50+ years economically)
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.
Why wouldn't DNA just encode "good feelings" when IT gets what it wants? Right temperature -> ahhh. Food -> ahhhhhh. Sex -> aaaaaahhhhhhhhhh. And then just let reinforcement learning take care of all the details in between. No need for storage space for fire making or for "Hey girl, how are you doing?" ... and therefore cheaper.
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)
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?