Google Brain's Co-inventor Tells Why He's Building Chinese Neural Networks

An anonymous reader writes "Here's an interview with Andrew Ng, former leader of Google Brain, discussing Baidu, Deep Learning, computer neural networks, and AI. An interesting excerpt from the interview on biological vs. computer neural networks: "A single 'neuron' in a neural network is an incredibly simple mathematical function that captures a minuscule fraction of the complexity of a biological neuron. So to say neural networks mimic the brain, that is true at the level of loose inspiration, but really artificial neural networks are nothing like what the biological brain does."

Comments

Biggest difference is timing.

[aberglas]

Certainly biological neurons are much more complex than artificial neural net neurons. The simplest "Integrate and Fire" (IF) model of a biological neuron perform a leaky integration over *time*, and if the voltage ever reaches the trigger value the fire. So the timing of stimulations is critical, whereas most Artificial Neural Networks (ANNs) does all its calculations (logically) at the same time. The ANN is both simpler and cleaner to work with. Biological synapses are very complex, but much of that complexity just reflects the wet technology that they are made from.

If you want to understand how the brain works, study biological neurons. If you want to understand how to build an intelligent machine, engineer ANNs.