Machine learning has become alchemy (2017) [video]

>> Those massive gains have yet to considered reliable enough to be considered trusthworthy.

We're using them at Generic Health Insurance Megacorp in production - lots of enterprises are. If you are in the IT industry, it might be useful to spend some lab time with ML. Possibly you have a misconception of ML and/or confuse it with AI.

It is a misconception that ANNs are based on human visual processing. Maybe "inspired by" is a better term. The 1959 paper "What the Frog's Eye Tells the Frogs Brain" disproved the belief that ANNs have anything to do real neural networks.

In my mind, machine learning is simply one class of algorithm primarily oriented towards multidimensional fuzzy pattern matching. This technique can easily be classed as a subset of digital signal processing concerns. Everything tacked onto the sides of this (LTSMs, etc.) in some attempt to increase the cleverness or scope of the ML network (aka self-driving cars and other assorted high magickery), seek to band-aid something that is fundamentally flawed for these purposes.

A ML network does not have intrinsic, real-time mutability in how it is defined, outside the scope of memory-based node weights, inputs, outputs or graphs over time. These nodes are added, removed or modified based on a predefined set of input, output and internal mappings. How would intermediate layers be defined in a dynamic way between input and output in such a network in an attempt to achieve these higher powers? Driving a car, for instance, is a task that requires learning entire new subsets of skills, many times ad-hoc, that require intermediate models to be dynamically developed which are potentially outside the capabilities of our understanding. The biggest challenge I see today is that we don't necessarily have a good way to dynamically construct models of the intermediate layers (such that we can map them to other layers), especially if these layers are being added and removed dynamically by algorithms at the edge of our capability to understand.

I've always felt that there needs to be some internal processing occurring at rates far higher than the input sample rates such that higher-order intelligence may emerge by way of adjusting the entities noted above multiple times per input sample (and potentially even in the absence of input). The problem is also going to come down to how a person would define outcomes vs how an AI/ML network would. For the future to really begin we will need an AI that can understand and reason with what success and failure feel like in our abstract terms. This will require it to have the capacity to dynamically construct abstractions which we would have no hope of modelling ourselves, as we do not have very deep insight into the abstractions upon which the biological human brain implements virtually any behavior today. There is no amount of discrete math in the universe which can accurately model and assess the higher-order outcomes of decisions made in our reality. You can run ML disguised as AI in simulations and environments with fixed complexity all day, but once you throw one of these "trained" networks out into the real world without any constraints, you are probably going to see very unsatisfactory outcomes.

Even granting the claim that ML has become alchemy, consider what alchemy was: Misguided in its quasi-magical underpinnings and goals, but nonetheless an extremely important step in the founding of modern chemistry. And so what started out with poor understanding and numerous misconceptions evolved over a period of time, through trial and error and hard won knowledge into a massively important branch of science. And so if ML is indeed comparable to alchemy, the only salient question is how long, and what will it take, to turn into chemistry.