[ogrisel]

While Demis Hassabis and probably some DeepMind researchers use some understandings from the neuroscience literature as an inspiration for their work, I am pretty sure that a majority of DeepMind researchers would rather use a combination of mathematics and trial and error experiments to build an intuition to guide the design of their next iteration of intelligent learning systems.

[shahbaby]

The point is that when mainstream ML begins to realize that there are limitations to the current cartoonish representations of neural networks, they then go back to the biology to see what they're missing.

However there are other companies, like Numenta, which realized decades ago that the current techniques will not be sufficient for general intelligence.

Numenta is not trying to emulate the brain like the Human Brain Project, they are aiming to learn the principles behind the neocortex and replicate it in software.

Again, I don't think most people know enough about the neocortex because if they did, we probably wouldn't be so quick to discard the only real example of intelligence we have.

[ganeshkrishnan]

>there are limitations to the current cartoonish representations of neural networks

How? on a broad level Deep Learning is the same as natural neural network. Signal in and then neuron decides to fire a signal out. The algorithms inside is what differentiates a human from a machine. As long as the algorithm can make intelligent decisions who cares how the human algorithm works. We are not trying to build a human brain, we are trying to build a better than human brain

[ogrisel]

To be more factual: a majority of papers published by researchers at DeepMind do not cite any result from the neuroscience literature in their bibliography. Instead they cite other papers from the Machine Learning community.