MMachine Learning: art or science? (Part one)

Abstract
There is a popular joke circulating the world of artificial intelligence at the moment: “When you’re fundraising, it’s AI. When you’re hiring, it’s ML. When you’re implementing, it’s logistic regression.” Now, behind every joke there is at least an ounce of truth. But the punchline of this joke is only funny if one understands that there is quite a serious difference between deep learning and a simple statistical analysis.

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Deep Learning & Statistics, What’s the Difference?
The mathematics that we are more commonly familiar with; statistics regressions, a dependent variable and correlation between different factors, are all what we might typically think of as explainable, but they are done entirely differently to machine learning.

For example, we might calculate a relationship between inflation and unemployment, or education and life-time earnings, and therefore draw conclusions based on those relationships.

Under good scientific analysis these regressions will be repeatable, with the assumptions made in calculating them will be explainable, and therefore we have the possibility of high-level transparency if we make decisions based on these regressions.

Machine learning, on the other hand, is often described as more art than science. What do you think?
Instead of picking factors by reason of their logical relationship (i.e. not data mining), machine learning will often use a huge number of variables that may not necessarily have a direct cause or relationship and will adjust the model based purely on "what works".

The process of machine learning is not necessary to provide a cause or relationship between two things, but instead to be able to make a prediction. Therefore, a machine learning model might identify what constitutes a cat not by the features that we might describe to it, but by a seemingly abstract set of requirements.

In addition, although data sets might affect the outcome of machine learning (e.g. a data set trained only on a particular skin color) there is no objective best way of training on any given data set.

That is the same data set might be used to create models with different levels of validity. This can be seen in machine learning competitions where standard data sets are used, such as a data set of hand written numbers, and competitors must try and create the best machine learning algorithms to interpret, with varying levels of success.

The level of success may depend on various complex decisions they make such as the number of times the model is trained on that same data, the number of ‘hidden’ layers of neural networks there are, the rate of that propagation, to name but a few.

The real test is whether at the end ‘it works’.

Machine learning may not be explainable. For example, a model that trains based on the number of times it has gone over a particular set of data cannot simply be explained by reference to a procedure and footnotes, at least in any useful fashion.

Where the ‘machine learning’ comes into this is that by using such a method, no-one has told the machine ahead of time what answer is. It worked it out for itself. Therefore, we cannot simply open up the black box to look at the assumptions that have gone into it and work out how it makes its decision.

To be continued.