A Returned-Probability Artificial Neural Network - The Quantum Artificial Neural Network
Artificial Neural Networks associated with Deep Learning, Machine Learning using supervised and unsupervised learning are fairly good at figuring out deterministic things. For example they can find an open door for a robot to enter. They can find patterns in a given matrix or collection, or field.
However, sometimes there is no evident computability function. In other words, suppose that you are looking at an event or action that results from a whole bunch of unknown things, with a random bit of chaos thrown in. It is impossible to derive a computable function without years of study and knowing the underlying principles. And even then, it still may be impossible to quantify with an equation, regression formula or such.
But Artificial Neural Nets can be trained to identify things without actually knowing anything about the background causes. If you have a training set with the answers or results of size k (k being a series of cases), then you can always train your Artificial Neural Networks or Multilayer Perceptrons on k-1 sets, and evaluate how well you are doing with the last set. You measure the error rate and back propagate, and off you go to another training epoch if necessary.
This is happening with predicting solar flares and the resultant chaos that it cause with electronics and radio communications when these solar winds hit the earth. Here is a link to the article, where ANN does the predicting:
In this case, the ANN's have shown that there is a relationship between vector magnetic fields of the surface of the sun, the solar atmosphere and solar flares. That's all well and dandy for deterministic events, but what if the determinism was a probability and not a direct causal relationship mapped to its input parameters? What if there were other unknown or unknownable influence factors?
That's were you need an ANN (Artificial Neural Network) to return a probability as the hypothesis value. This is an easy task for a stats package working on database tables, churning out averages, probabilities, degrees of confidence, standard deviations etc, but I am left wondering if it could be done internally in the guts of the artificial neuron.
The artificial neuron is pretty basic. It sums up all of the inputs and biases multiplied by their weights, and feeds the result to an activation function. It does this many times over in many layers. What if you could encode the guts of the neuron to spit out the probability of the results of what is being inputted? What if somehow you changed the inner workings of the perceptron or neuron to calculate the probability. It seems to me that the activation function is somehow ideally suited to adaptation to do this, because it can be constructed to deliver an activation value of between 0 and 1, which matches probability notation.
Our human brains work well with fuzziness in our chaotic world. We unconsciously map patterns and assign probabilities to them. There is another word for fuzzy values. It is a "quantum" property. The more you know about one property of an object, the less you know about another. Fuzziness. The great leap forward for Artificial Neural Networks, is to become Quantum and deliver a probability. Once we can get an Artificial Neural Net machine to determine probability, then we can apply Bayesian mechanics. That's when it can make inferences, and get a computer on the road to thinking from first principles -- by things that it has learned by itself.