Re:What are the limits of deep learning?
by visarga on Wednesday January 23, 2019 @03:10PM
(#18982283)
> neurons in the network are simplified mathematical representations.
That is the important difference. A biological neuron is more like a cell in the cell automaton (Conway's game of life). It is a dynamic process, even if the dynamics change by a small factor, it could have a very different behaviour. Artificial neurons on the other hand are much more uniform and lack the time dimension. If you account for dynamics, then learning in the brain is a completely different thing than learning in neural nets. It has been proven that Hebbian learning (local plasticity of synapses) approximates gradient based learning, though in simulation it attains lower accuracies.
Another observation was that biological neural nets don't have a mechanism to communicate gradients back. But a deep learning experiment proved you can have fixed random back connections and the network will still learn. They don't actually need to be symmetrical connections.
The real mystery is about loss functions. Such a loss function would have to be computed in a distributed fashion in the brain. They are the origin of the learning signal. The brain has many such loss functions that have been discovered by evolution. We have not understood loss functions well enough, and understanding them would be a huge leap forward.
Another important observation is that the brain learns from the environment, not a fixed training set. Environments are richer sources, and our current crop of AI agents are playing all sorts of games, but they never have the same complexity as real world. Btw, tomorrow DeepMind will make a Starcraft demo.
That is the important difference. A biological neuron is more like a cell in the cell automaton (Conway's game of life). It is a dynamic process, even if the dynamics change by a small factor, it could have a very different behaviour. Artificial neurons on the other hand are much more uniform and lack the time dimension. If you account for dynamics, then learning in the brain is a completely different thing than learning in neural nets. It has been proven that Hebbian learning (local plasticity of synapses) approximates gradient based learning, though in simulation it attains lower accuracies.
Another observation was that biological neural nets don't have a mechanism to communicate gradients back. But a deep learning experiment proved you can have fixed random back connections and the network will still learn. They don't actually need to be symmetrical connections.
The real mystery is about loss functions. Such a loss function would have to be computed in a distributed fashion in the brain. They are the origin of the learning signal. The brain has many such loss functions that have been discovered by evolution. We have not understood loss functions well enough, and understanding them would be a huge leap forward.
Another important observation is that the brain learns from the environment, not a fixed training set. Environments are richer sources, and our current crop of AI agents are playing all sorts of games, but they never have the same complexity as real world. Btw, tomorrow DeepMind will make a Starcraft demo.