Machine Learning

While we tend to think of ourselves as being at the pinnacle of evolution, in reality humans are barely a step up from monkeys. Our only real differentiation from them is that we have language, which allows us to communicate knowledge to others and to preserve that knowledge through time. Language gives us a huge advantage, allowing us to progressively accumulate new knowledge by building up on previous discoveries, but in the end we are just animals who have evolved to survive, like all other animals. We are not adapted to having civilizations and technology, we evolved to find food and procreate and the results of this can be seen all over - from how tech companies use simple tricks like noises and bright colors and intermittent rewards to keep us hooked, to how food companies load their food with salt, fat and sugar to keep us eating unhealthy food, to the cognitive biases and heuristics we use to make decisions under uncertainty. The point of all of this is that humans evolved to find food and avoid predators, and our brains are incredibly ill-suited to processing the large amounts of data that are required to make evaluations about the types of issues that we face everyday in today's complex world.

Computers on the other hand are designed for processing large amounts of data - they can do this very efficiently if programmed correctly. However they lack our creativity - the ability to combine seemingly unrelated ideas into new ideas, and to come up with novel solutions to problems. Machine learning combines our creativity with the ability of computers to handle large amounts of data, specifically the ability to find patterns in data. In 2015 a journalist ran a study on chocolate, the results of which were that chocolate helps you lose weight. The study was commissioned as an example of "junk science" and only had 15 participants with 18 measurements for each participant. The author said “here’s a dirty little science secret: If you measure a large number of things about a small number of people, you are almost guaranteed to get a ‘statistically significant’ result.” Unfortunately much of science is conducted like this - the authors start a study to proof a hypothesis and maybe they'll ignore some results which contradict the hypothesis if other results confirm it. No one likes to be wrong and if the results are a bit ambiguous you can maybe just cherry pick the numbers you like. In Darrell Huff's 1954 book "How to Lie with Statistics" he says "if you torture the data long enough it will confess to anything" and this is in fact the case. 

Machine learning is about letting the data speak for itself. With machine learning you set up a system of symbolic equations for transforming data into predictions and then feed the data into that system and see what happens. If you don't like the results you can change the system or the data, but the process is far too complex to be able to cherry pick the data you like and discard the rest. This combines the strengths of humans with the strengths of computers - the humans use their creativity and domain knowledge to create the system which they hope will find patterns in the data and the computers run the data through the system. While technically possible to do so, the process of analyzing the data is far more complex than could ever be done without a computer, and the computers can only do what they are told to do - they can not create novel ideas from nothing.

In my opinion, machine learning is the most important scientific technology in recent history. Just like electricity allowed energy to become uncoupled from the previous sources - fire and animal energy - machine learning uncouples the ability to process data from the constraints of the human brain. Properly used, I think machine learning will be as revolutionary as electricity was.

Libellés: machine_learning