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> when scaled sufficiently Whilst technically correct, this obscures the truly collosal scale at which evolution computes. If we consider each organism as a p

> when scaled sufficiently Whilst technically correct, this obscures the truly collosal scale at which evolution computes. If we consider each organism as a processor executing a single fitness observation, we can fit hundreds of thousands of such processors (e.g. bacteria) on the few square millimetres of a pin head. These processors are spread (less densely) across the 510 million square kilometre surface of the Earth. They are suspended in oceans, which have an average depth of 3.7 kilometres; and in the atmosphere, at least up to 10 kilometres (based on our observations so far). Whilst the generation time (AKA execution time) of such organisms varies wildly, it is very commonly less than an hour. Yet even with these truly staggering resources at its disposal, evolution still took 3.3 billion years of wall clock time to come up with multicellular organisms. Relying on evolution to come up with meta-level optimisation mechanisms like intelligence is also rather flaky; we only have one example of biological evolution to study, acting on one representation (DNA + RNA + proteins). This just-so-happened to stumble upon aerobic respiration, multicellularity, neurons and brains, but it's certainly not a "goal" of the system, and sampling bias prevents us knowing how likely an outcome that is (i.e. whether it's an attractor). Still, organisms with nervous systems are a vanishingly small proportion of all lifeforms; and in fact, every "advance" is dwarfed by the success of single celled microbes. We could always leave an evolving system running for longer, but setting up conditions which lead to open-ended evolution is still an unknown area; simulations always tend to level off/saturate at some point. As a "last resort" we could provide the system with a mechanism to sample organisms completely at random, and use that as proof that anything is possible given enough time; but in that case why use evolution at all, when we can just perform that sampling directly, or alternatively just enumerate organisms?

ewjordan 10y ago
I nodded along with pretty much everything you said, so I think we're arguing different points but not necessarily disagreeing. I agree, evolution's scale is colossal, but restrict your view to the tiny bit of the evolutionary machine that is implicated in figuring out how to arrange neurons (which evolution "discovered" previously, to be fair, with incredible effort) in a fashion that renders their behavior intelligent, and it becomes a vastly smaller effort. These beings are all rather large, at least worm-sized or greater. Their lifecycles are measured in days to years, not minutes or hours. And they only had 250 million years or so to make the leap from "bunch of neurons hard-coded to do specific jobs" to "generally intelligent arrangement of neurons capable of higher level thought". All of this cuts the scale by at least 5-10 orders of magnitude compared to what you correctly point out as the overall colossal scale of evolutionary computation. And to me, it says that compared to all the other amazingly difficult stuff that evolution has discovered, intelligence was a damn easy find, especially since there's almost no fitness benefit - hell, worms probably do better than we do as far as evolutionary fitness goes, they're tiny, numerous, and reproduce like crazy. My money is on the fact that human intelligence guiding a search cuts another couple orders of magnitude off of how much of a long-shot discovering an intelligent algorithm would be via a random search. I could definitely be wrong, it's very hard to be precise when you're vaguely arguing about orders of magnitude, but to me it doesn't seem like the algorithmic "magic" in the brain can possibly be very complex if evolution was able to get there, there just aren't many other circumstances where evolution stumbles on something that damn clever.
- chriswarbo 10y ago
  > My money is on the fact that human intelligence guiding a search cuts another couple orders of magnitude off of how much of a long-shot discovering an intelligent algorithm would be via a random search. I certainly agree that directing/biasing evolutionary processes can be a reasonably efficient search strategy. The difficulty is that it still seems to be a fallback, suited to problems where we don't have a more informed strategy. For example, if we can calculate gradients, we're probably better off doing gradient descent. If we can perform deduction/induction on symbols, we're probably better off doing that. Those problems where evolutionary processes seem well suited are those where we might not know how to bias the search. I think the best place for such approaches at the moment is meta-level algorithms, where "smarter" algorithms (like gradient descent) are applied to the underlying problems we care about, but the parameters, policies, etc. evolve on top (e.g. step sizes, which algorithms to use, when to restart, scheduling concurrent attempts, etc.). That's the approach taken by NEAT for example, where a genetic algorithm comes up with neural network topologies, whilst those neural networks are trained using backpropagation.
  - ewjordan 10y ago
    FWIW, I'm in no way suggesting that evolutionary strategies are the way forward in the search for AGI, though I suppose they might be part of the solution. My 95% bet is that a random or evolutionary search might be how people optimize an AGI, not how they find it. They'll get there via a minor but innovative twist on the RNN work we've already seen (first people need to realize that backdrop through time is a complete dead end, and they should be looking closer at reinforcement learning methods and figuring out how to bring them online).

ewjordan 10y ago

I nodded along with pretty much everything you said, so I think we're arguing different points but not necessarily disagreeing. I agree, evolution's scale is colossal, but restrict your view to the tiny bit of the evolutionary machine that is implicated in figuring out how to arrange neurons (which evolution "discovered" previously, to be fair, with incredible effort) in a fashion that renders their behavior intelligent, and it becomes a vastly smaller effort. These beings are all rather large, at least worm-sized or greater. Their lifecycles are measured in days to years, not minutes or hours. And they only had 250 million years or so to make the leap from "bunch of neurons hard-coded to do specific jobs" to "generally intelligent arrangement of neurons capable of higher level thought". All of this cuts the scale by at least 5-10 orders of magnitude compared to what you correctly point out as the overall colossal scale of evolutionary computation. And to me, it says that compared to all the other amazingly difficult stuff that evolution has discovered, intelligence was a damn easy find, especially since there's almost no fitness benefit - hell, worms probably do better than we do as far as evolutionary fitness goes, they're tiny, numerous, and reproduce like crazy. My money is on the fact that human intelligence guiding a search cuts another couple orders of magnitude off of how much of a long-shot discovering an intelligent algorithm would be via a random search. I could definitely be wrong, it's very hard to be precise when you're vaguely arguing about orders of magnitude, but to me it doesn't seem like the algorithmic "magic" in the brain can possibly be very complex if evolution was able to get there, there just aren't many other circumstances where evolution stumbles on something that damn clever.

chriswarbo 10y ago
> My money is on the fact that human intelligence guiding a search cuts another couple orders of magnitude off of how much of a long-shot discovering an intelligent algorithm would be via a random search. I certainly agree that directing/biasing evolutionary processes can be a reasonably efficient search strategy. The difficulty is that it still seems to be a fallback, suited to problems where we don't have a more informed strategy. For example, if we can calculate gradients, we're probably better off doing gradient descent. If we can perform deduction/induction on symbols, we're probably better off doing that. Those problems where evolutionary processes seem well suited are those where we might not know how to bias the search. I think the best place for such approaches at the moment is meta-level algorithms, where "smarter" algorithms (like gradient descent) are applied to the underlying problems we care about, but the parameters, policies, etc. evolve on top (e.g. step sizes, which algorithms to use, when to restart, scheduling concurrent attempts, etc.). That's the approach taken by NEAT for example, where a genetic algorithm comes up with neural network topologies, whilst those neural networks are trained using backpropagation.
- ewjordan 10y ago
  FWIW, I'm in no way suggesting that evolutionary strategies are the way forward in the search for AGI, though I suppose they might be part of the solution. My 95% bet is that a random or evolutionary search might be how people optimize an AGI, not how they find it. They'll get there via a minor but innovative twist on the RNN work we've already seen (first people need to realize that backdrop through time is a complete dead end, and they should be looking closer at reinforcement learning methods and figuring out how to bring them online).