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Given a fitness function that is the same as an ant's, it seems plausible that they would. The question is, what is that function?

malux85 9y ago
Balanced Homeostasis? Maybe we could just start with that - it might give an organism life, but it would be interesting to see if social emergent behaviors came from a group of agents optimizing homeostasis alone
halflings 9y ago
> Given a fitness function that is the same as an ant's, it seems plausible that they would. > The question is, what is that function? Not necessarily. Everything points to the fact that the humain brain (at least, I don't know much about ants) does not work in any way similar to neural networks, and as such there's no guarantee that you can represent its behaviour by the current algorithms. For example, real neurons have no supervision signal. Memory is also a big issue (see the work being done by DeepMind and FAIR on differentiable neural computers, and memory networks) and Reinforcement Learning still struggles with long-term planing, switching strategies, etc.
- catamorphismic 9y ago
  > Not necessarily. Everything points to the fact that the human brain (at least, I don't know much about ants) does not work in any way similar to neural networks, and as such there's no guarantee that you can represent its behaviour by the current algorithms. Yes, the brain is most likely not simply modelling one giant ANN, but it's much more plausible that ANN-like components have a role in it.
- canjobear 9y ago
  If there is a space of optimal policies for the ant, then both the ANN and real neural systems will be attracted there. There are certainly a lot of local minima, but it would be surprising to me if ANNs and real neural systems could only find totally disjoint local minima with no similarities.
- zardo 9y ago
  >and as such there's no guarantee that you can represent its behaviour by the current algorithms. Doesn't the universal function approximation theorem provide just such a guarantee? It doesn't guarantee any algorithm will converge on that representation, but the capability to represent it is there.
bryananderson 9y ago
It’s almost certainly not just one function. An ant’s fitness function is different from its ancestor’s fitness function, which is different from its own ancestor’s fitness function, which is different from... You don’t get from zero to complexity with a single fitness function. The question we should ask is: How do we vary the fitness function over time in order to evolve something complex? I have no idea what the answer is, but... “If you know the question, you know half.” -Herb Boyer, geneticist

malux85 9y ago

Balanced Homeostasis? Maybe we could just start with that - it might give an organism life, but it would be interesting to see if social emergent behaviors came from a group of agents optimizing homeostasis alone

halflings 9y ago

> Given a fitness function that is the same as an ant's, it seems plausible that they would. > The question is, what is that function? Not necessarily. Everything points to the fact that the humain brain (at least, I don't know much about ants) does not work in any way similar to neural networks, and as such there's no guarantee that you can represent its behaviour by the current algorithms. For example, real neurons have no supervision signal. Memory is also a big issue (see the work being done by DeepMind and FAIR on differentiable neural computers, and memory networks) and Reinforcement Learning still struggles with long-term planing, switching strategies, etc.

catamorphismic 9y ago
> Not necessarily. Everything points to the fact that the human brain (at least, I don't know much about ants) does not work in any way similar to neural networks, and as such there's no guarantee that you can represent its behaviour by the current algorithms. Yes, the brain is most likely not simply modelling one giant ANN, but it's much more plausible that ANN-like components have a role in it.
canjobear 9y ago
If there is a space of optimal policies for the ant, then both the ANN and real neural systems will be attracted there. There are certainly a lot of local minima, but it would be surprising to me if ANNs and real neural systems could only find totally disjoint local minima with no similarities.
zardo 9y ago
>and as such there's no guarantee that you can represent its behaviour by the current algorithms. Doesn't the universal function approximation theorem provide just such a guarantee? It doesn't guarantee any algorithm will converge on that representation, but the capability to represent it is there.

bryananderson 9y ago

It’s almost certainly not just one function. An ant’s fitness function is different from its ancestor’s fitness function, which is different from its own ancestor’s fitness function, which is different from... You don’t get from zero to complexity with a single fitness function. The question we should ask is: How do we vary the fitness function over time in order to evolve something complex? I have no idea what the answer is, but... “If you know the question, you know half.” -Herb Boyer, geneticist