All the cross-generational information channels you highlight are at rough saturation, so they’re not able to contribute to the cross-generational accumulation of capabilities-promoting information.
This seems clearly contradicted by empirical evidence. Mirror neurons would likely be able to saturate what you assume is brains learning rate, so not transferring more learned bits is much more likely because marginal cost of doing so is higher than than other sensible options. Which is a different reason than “saturated, at capacity”.
Firstly, I disagree with your statement that other species have “potentially unbounded ways how to transmit arbitrary number of bits”. Taken literally, of course there’s no species on earth that can actually transmit an *unlimited* amount of cultural information between generations
Sure. Taken literally, the statement is obviously false … literally nothing can store arbitrary number of bits because of Bekenstein bound. More precisely, the claim is existing non-human ways how to transmit leaned bits to the next generation in practice do not seem to be constrained by limits how many bits they can transmit, but by some other limits (e.g. you can transmit more bits than the capacity of the animal to learn).
Secondly, the main point of my article was not to determine why humans, in particular, are exceptional in this regard. The main point was to connect the rapid increase in human capabilities relative to previous evolution-driven progress rates with the greater optimization power of brains as compared to evolution. Being so much better at transmitting cultural information as compared to other species allowed humans to undergo a “data-driven singularity” relative to evolution. While our individual brains and learning processes might not have changed much between us and ancestral humans, the volume and quality of data available for training future generations did increase massively, since past generations were much better able to distill the results of their lifetime learning into higher-quality data.
1. As explained in my post, there is no reason to assume ancestral humans were so much better at transmitting information as compared to other species
2. The qualifier they were better at transmitting cultural information may (or may not) do a lot of work.
The crux is something like “what is the type signature of culture”. Your original post roughly assumes “it’s just more data”. But this seems very unclear: a comment above yours, jacob_cannell confidently claims I miss the forest and makes a guess the critical innovation is “symbolic language”. But, obviously, “symbolic language” is a very different type of innovation than “more data transmitted across generations”.
Symbolic language likely - allows to use any type of channel more effectively - in particular, allows more efficient horizontal synchronization, allowing parallel computations across many brains - overall sounds more like software upgrade
Consider plain old telephone network wires: these have surprisingly large intrinsic capacity, which isn’t that effectively used by analog voice calls. Yes, when you plug a modem on both sides you experience “jump” in capacity—but this is much more like “software update” and can be more sudden.
Or a different example—empirically, it seems possible to teach various non-human apes sign language (their general purpose predictive processing brains are general enough to learn this). I would classify this as “software” or “algorithm” upgrade,. If someone did this to a group of apes in the wild, it seems plausible knowledge of language would stick and make them differentially more fit. But teaching apes symbolic language sounds in principle different from “it’s just more data” or “it’s a higher quality data”, and implications for AI progress would be different.
it relies on resource overhand being a *necessary* factor,
My impression is compared to your original post your model drifts to more and more general concepts where it becomes more likely true, harder to refute and less clear what the implication for AI is. What is the “resource” here? Does negentropy stored in wood count as “a resource overhang”?
I’m arguing specifically against a version where “resource overhang” is caused by “exploitable resources you easily unlock by transmitting more bits learned by your brain vertically to your offspring brain” because your map of humans to AI progress is based on quite specific model of what are the bottlenecks and overhangs.
If the current version of the argument is “sudden progress happens exactly when (resource overhang) AND …” with “generally any kind of resource” then yes, this sounds more likely, but it seems very unclear what does this imply for AI.
(Yes I’m basically not discussing the second half of the article)
I’ll try to keep it short
This seems clearly contradicted by empirical evidence. Mirror neurons would likely be able to saturate what you assume is brains learning rate, so not transferring more learned bits is much more likely because marginal cost of doing so is higher than than other sensible options. Which is a different reason than “saturated, at capacity”.
Sure. Taken literally, the statement is obviously false … literally nothing can store arbitrary number of bits because of Bekenstein bound. More precisely, the claim is existing non-human ways how to transmit leaned bits to the next generation in practice do not seem to be constrained by limits how many bits they can transmit, but by some other limits (e.g. you can transmit more bits than the capacity of the animal to learn).
1. As explained in my post, there is no reason to assume ancestral humans were so much better at transmitting information as compared to other species
2. The qualifier they were better at transmitting cultural information may (or may not) do a lot of work.
The crux is something like “what is the type signature of culture”. Your original post roughly assumes “it’s just more data”. But this seems very unclear: a comment above yours, jacob_cannell confidently claims I miss the forest and makes a guess the critical innovation is “symbolic language”. But, obviously, “symbolic language” is a very different type of innovation than “more data transmitted across generations”.
Symbolic language likely
- allows to use any type of channel more effectively
- in particular, allows more efficient horizontal synchronization, allowing parallel computations across many brains
- overall sounds more like software upgrade
Consider plain old telephone network wires: these have surprisingly large intrinsic capacity, which isn’t that effectively used by analog voice calls. Yes, when you plug a modem on both sides you experience “jump” in capacity—but this is much more like “software update” and can be more sudden.
Or a different example—empirically, it seems possible to teach various non-human apes sign language (their general purpose predictive processing brains are general enough to learn this). I would classify this as “software” or “algorithm” upgrade,. If someone did this to a group of apes in the wild, it seems plausible knowledge of language would stick and make them differentially more fit. But teaching apes symbolic language sounds in principle different from “it’s just more data” or “it’s a higher quality data”, and implications for AI progress would be different.
My impression is compared to your original post your model drifts to more and more general concepts where it becomes more likely true, harder to refute and less clear what the implication for AI is. What is the “resource” here? Does negentropy stored in wood count as “a resource overhang”?
I’m arguing specifically against a version where “resource overhang” is caused by “exploitable resources you easily unlock by transmitting more bits learned by your brain vertically to your offspring brain” because your map of humans to AI progress is based on quite specific model of what are the bottlenecks and overhangs.
If the current version of the argument is “sudden progress happens exactly when (resource overhang) AND …” with “generally any kind of resource” then yes, this sounds more likely, but it seems very unclear what does this imply for AI.
(Yes I’m basically not discussing the second half of the article)