[ouid]

Perhaps the problem simply lies in calling them neural networks.

[schoen]

This terminology goes back to McCulloch and Pitts in 1943, who said they were making an analogy or model based on the behavior of biological neurons.

https://en.wikipedia.org/wiki/Artificial_neuron#History

There are many things that are inexact about this analogy or model, and many of them were known to be inexact in 1943, but that was the direct inspiration.

Apparently there are lots of different mathematical models available about biological neuron behavior:

https://en.wikipedia.org/wiki/Biological_neuron_model

[TremendousJudge]

turns out it's very hard to model a thing that we don't know how it actually works

[curiousgal]

To be fair, we do understand how neurons work, at least on a singular level. Perceptrons model that quite well.

[philipkglass]

Implementing a basic perceptron classifier is an undergrad homework assignment. Biological modeling of neurons is a work of decades:

http://www.genesis-sim.org/

https://www.neuron.yale.edu/neuron/what_is_neuron

[robotresearcher]

McCulloch's argument was that perhaps the gross behaviour of a NN as layers of simple transfer functions is where the real action is, and the rest of the details are just gravy.

The fact we now give this to undergrads as homework suggests that there was some value to this idea.

[philipkglass]

Students in computer science may implement a perceptron as a homework problem. Students in biology don't do that, nor do they use perceptrons to learn about brains, because perceptrons bear only faint resemblance to biological neurons. Reproducing important biological features of real neurons requires much more complicated software.

I'm not denigrating perceptrons or other neuro-inspired approaches to classification. I'm just pointing out that perceptrons are not a faithful model of neurons.

[Klockan]

But how does a neuron decide to grow new axons or how to change input weights? Biological neurons does this when solving tasks and not just during training. Isn't it possible that human-like intelligence depends on the network being dynamic? For example, when you play a game for the first time a lot of things suddenly starts to click, couldn't that be the result of new connections forming or at least some weights being changed? If this is true then it would be impossible to create a general game playing AI with human-like performance using our current model.

[bitL]

Biological neurons are fundamentally different to models used in deep learning. They can have multiple outputs, can span over whole brain and do local protein-based computations we don't really understand yet. What we have in perceptron is just a very simple model based on what we observed using rudimentary electricity detectors.

[SomeStupidPoint]

Don't fully connected layers do exactly what you describe?

[0xdeadbeefbabe]

As well as the title "artificial intelligence".