3 ms·

The problem with current approaches to AI using backpropagation is that while gradient descent is effective for learning a task, the brain is not a task learnin

The problem with current approaches to AI using backpropagation is that while gradient descent is effective for learning a task, the brain is not a task learning machine (parts of it are but not the parts that produce human intelligence) and does not do gradient descent. Achieving human level intelligence (strong AI) will require going beyond the gradient descent paradigm. The new paradigm that must be followed is building an agent with a neural network that evolves according to the 'neurons that fire together wire together' approach combined with reinforcement learning.

solveit 7y ago
I'm not saying you're wrong, but I can't see how in the world you could possibly know any of what you said. The human brain doesn't (as far as we know) seem to use backpropagation. Everything else is wide, wide open.
- corporate_shi11 7y ago
  I think it's accepted that neurons which interact exhibit increased axon growth between themselves. Given the local nature of biological processes I think this must therefore be the dominant process behind connection building in the brain. It's really a very general statement about neural development. I have no idea how important the high level structure of the brain is for human thought. It's possible that most brain regions are irrelevant and have more to do with regulating bodily processes than anything else, mere machinery for running a body. But who knows. The layered residual and wide reaching connections of neocortical neurons are probably equivalent to the 'airplane' in the bird flight analogy.
  - hprotagonist 7y ago
    > I think it's accepted that neurons which interact exhibit increased axon growth between themselves. hebbian learning seems real, but also incomplete. As normally stated it’s a positive feedback loop — if that’s all that was going on, we would be incapable of change. There are a number of non-hebbian learning paradigms (e.g., volume learning) that seem true too.

solveit 7y ago

I'm not saying you're wrong, but I can't see how in the world you could possibly know any of what you said. The human brain doesn't (as far as we know) seem to use backpropagation. Everything else is wide, wide open.

corporate_shi11 7y ago
I think it's accepted that neurons which interact exhibit increased axon growth between themselves. Given the local nature of biological processes I think this must therefore be the dominant process behind connection building in the brain. It's really a very general statement about neural development. I have no idea how important the high level structure of the brain is for human thought. It's possible that most brain regions are irrelevant and have more to do with regulating bodily processes than anything else, mere machinery for running a body. But who knows. The layered residual and wide reaching connections of neocortical neurons are probably equivalent to the 'airplane' in the bird flight analogy.
- hprotagonist 7y ago
  > I think it's accepted that neurons which interact exhibit increased axon growth between themselves. hebbian learning seems real, but also incomplete. As normally stated it’s a positive feedback loop — if that’s all that was going on, we would be incapable of change. There are a number of non-hebbian learning paradigms (e.g., volume learning) that seem true too.