Backpropagation is a leaky abstraction

I do think the complaint on having to write the backward pass seems especially shallow; finding out they were working with numpy makes it even more so (since numpy takes the pain out of the matrix operations). IIRC, when I took the ML Class in 2011, we used Octave, but Ng had us first write stuff "the hard way" - so we'd understand what was going on later when we used Octave's methods.

Something about this article as a whole, though, does raise a question that I've been wondering about, and I want to present it here for a bit of discussion (maybe it needs its own thread?):

Does anyone else here think that the current approach to neural networks has some fundamental flaws?

Now - I'm not an expert; call me an interested student right now, with only the barest of experience (beyond the ML Class, I also took Udacity's CS373 course, and I am also currently enrolled in their Self-Driving Car Engineer nanodegree program).

I understand that what we currently have and know does work. What I mean by that is the basic idea of an artificial neural network using forward and back-prop, multiple layers, etc (and all the derivatives - RNN, CNN, deep learning, etc). I understand the need and reasoning behind using activation functions based around calculus and derivatives and the chain-rule, etc (though I admit I need further education in these items).

But something nags at me.

All of this, despite the fact that it works and works well (provided all your tuning and such is right, etc), just seems like it is over-complicated. Real neurons don't use calculus and activation functions, nor back-propagation, etc in order to learn. All of those things in an ANN are just abstractions and models around what occurs in nature.

Maybe (probably?) I am wrong - but it seems like what nature does is simpler. Much less power is used, for instance, and the package is much more compact. I just have this feeling that in some manner we may have gone down a path that while it has produced a working representation, that representation is overly complex, and had we took another approach (whatever that might be?), our ANNs would look and work much differently - perhaps even more efficiently.

About the only alternatives I have heard about otherwise have been things like spike-train neural networks, and some of the other "closer to nature" simulation (of ion pumps and real synapses, etc). Still, even those, while seemingly closer, also have what appears to be too much complexity.

I'm probably just talking out of my nether regions as a general n00b to the field. I do wonder, though, if there might be another solution, seemingly out in "left-field" that might push things forward, if someone was willing to look and experiment. It is something I plan to look into myself, as I find time and such between lessons and other work for my current learning experience.

Agreed. The most important lesson I took away from my ML class is that I have to learn more of optimization theory.

Edit: hmm not sure why this comment is getting downvoted.

Backprop isn't just a leaky abstraction. There's not really any evidence yet that biological neural networks use anything like backprop. So its important that students be taught the low-level aspects of the current state of the art so that better architectures can be invented in the future.

(Note: at the very end of this comment, I am leaving a link to one hypothesis about how something like backprop may happen at the dendritic level.)

I've been doing a deep dive on ML and bio neurons in the last week or so to try and get up to speed. The feedforward networks with backprop that are doing such awesome work right now share almost nothing in common with actual neurons.

Just some differences: (neuron == bio, node == artificial)

Terms: Neuron resting potential is about -70mV. Action potential (threshold) is about -55mV.

1) Neurons are spikey and don't have weights. A neuron sums all of its inputs to reach a threshold. When that threshold is reached, an electric current (action potential) is spread equally to all synaptic terminals. A node has a weight value for each input node that it multiplies by the value received from the input node. All of these values are then summed and passed to the connected nodes in the next layer.

2) There are multiple types of neurons, but for simplification there are 2 types that called "excitatory" and "inhibatory". A neuron can mostly only emit "positive" or "negative" signals via the synapse. There are different neurotransmitters that either add or subtract to the voltage potential for each dendritic input. The total sums work together to cross the threshold voltage. I say mostly because there is some evidence for a minority of neurons to be able to release neurotransmitters from both groups.

3) Artificial networks are generally summation based with no inclusion of time. Bio networks use summation and summation over time together to determine if they should fire or not. If I recall correctly, a neuron can repeatedly fire about 300 times per second. A dendritic input can last for up to 1.5 milliseconds. So if a neuron gets enough positive inputs at the same time, or collects enough over time, it will fire.

I haven't found any hypotheses or experiments that try to explain how reinforcement learning takes place in neurons yet in the absence of back propagation. Pretty sure that information is out there but I haven't run across it.

Overall I think that there will be multiple engineering approaches to AI, just like there are to construction and flight. We understand how birds and bees fly, but we don't build planes the same way.

Its important to remember than cognition is based in physics just like any other physical system. When the principles are well understood, there are multiple avenues to using them.

Here is a short collection of links that I've been finding helpful.

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

http://neuroscience.uth.tmc.edu/s1/introduction.html

https://www.quora.com/Can-a-synapse-change-from-being-inhibi...

https://en.wikipedia.org/wiki/Dale's_principle

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

I think you are assuming the human brain is significantly simpler and better than it is. To start with: the number of neurons in the brain is ~100 billion, which is roughly 10,000 times more than in a large ANN, we train the human brain for years before it does anything remotely intelligent (as opposed the the expectation that neural nets will get somewhere useful in hours or days) and most importantly: our brains probably have strong priors due to millions of years of evolution encoded in our DNA. On top of this, not all brains are as good as each other, especially when you think about animals who do not have a real system of speech.

I don't know a huge amount about neurology (or neural nets), but...

Adult humans seem to learn faster when there is some combination of theory, examples, and experience (aka feedback). I'm a scientist and I have very little interest in neural nets for science because it contorts away the kind of equation-based systematics that we rely on to understand our world. The theory component is missing.

I'm more interested in expert system-type AI, where the learning may help us infer more about the world in the systematic way that scientists try to grapple with it--logical rules and frameworks, physics equations, statistics, etc. But these seem to be far less effective or efficient than ANNs, at least as currently implemented.

That being said, I did see a great talk last week on using ANNs to accelerate viscoelasticity calculations--those sorts of things are definitely valuable for science by reducing the time required for simulations by orders of magnitude. And some of the discussion after the talk had to do with how a graph of biases and weights is, philosophically and practically, a fundamentally different way of representing the problem and its solution than a set of differential equations. There is no doubt that it's a useful way of solving the calculations, but it's unclear how we can build upon those weights and biases in a theoretically-meaningful way. How do we learn from machine learning?