Neuromorphic AI/low-fidelity whole-brain emulation, is likely to precede hi-fidelity whole-brain emulation.
Therefore, if we want to understand possible pathways to AI, I think it pays to focus attention on technologies which are inspired by neuroscience but either emulate the human mind in an inexact way or use somewhat neuron-like computing elements in new ways.
The level of detail to choose for simulated neurons seems very controversial.
The Blue Brain Project models neurons with a lot of complicated internal state, while the DARPA Synapse Project is going for very simple neurons.
The projects have different goals- DARPA wants to create devices for applications, while The European Brain Initiative is motivated more by trying to understand biological phenomena.
At 256 Million synapses per chip, the DARPA project could string together less than one million processors to reach the number of synapses in the brain. That goal is within reach, but how much can their simple neurons really do?
If simpler neuron-like components are sufficient, then far less computing capacity is required to build some kinds of neuromorphic AI.
Neuromorphic AI/low-fidelity whole-brain emulation, is likely to precede hi-fidelity whole-brain emulation.
Therefore, if we want to understand possible pathways to AI, I think it pays to focus attention on technologies which are inspired by neuroscience but either emulate the human mind in an inexact way or use somewhat neuron-like computing elements in new ways.
The level of detail to choose for simulated neurons seems very controversial.
The Blue Brain Project models neurons with a lot of complicated internal state, while the DARPA Synapse Project is going for very simple neurons.
http://www.eecs.berkeley.edu/~phj/cs267/hw0/
http://www.artificialbrains.com/darpa-synapse-program
The projects have different goals- DARPA wants to create devices for applications, while The European Brain Initiative is motivated more by trying to understand biological phenomena.
At 256 Million synapses per chip, the DARPA project could string together less than one million processors to reach the number of synapses in the brain. That goal is within reach, but how much can their simple neurons really do?
If simpler neuron-like components are sufficient, then far less computing capacity is required to build some kinds of neuromorphic AI.
Is there literature I could read on the differences between the performance of the neurons DARPA uses and the neurons Blue Brain uses?
I have a few scattered links, but I would also welcome a more detailed bibliography on the topic of new neurologically-inspired AI tools.
Both of these systems are doing something vastly different than the neural networks used in machine learning.
Deep Mind is yet another thing.