Lately I've been thinking about A.I. and Statistics a lot (you could say that the amount of time spent thinking about these topics is significantly higher at alpha = 0.05!). This is partly because my Stats article managed to get more traction than any other article I've written in the past few months, and partly because A.I. is becoming more and more relevant in our field. So, the question of whether A.I. is one day going to replace Stats altogether remains a very relevant one. The key advantage of A.I. methods is that they are assumptionfree. This by itself enables them to tackle the problems they are aiming to solve, in a very methodical and efficient way. Of course, certain assumptions might speed things up, but they might obstruct the discovery of the optimal solutions to the problem at hand. Statistical inference models lost the war against machine learning models because of that, especially when artificial neural networks (ANNs) entered the scene. Also, the fact that many ML models could be combined in an ensemble setting allowed them to become even more robust, attaining F1 scores that were unfathomable for statistical prediction models. So, the possibility of other methods of statistics becoming outsourced to alternative systems is quite real. On the other hand, statistics are very easy to use and interpret, since most of them were designed from a user’s perspective. There are doctors out there (the medical kind), who don’t know much about data analytics but can easily work a statistical model for figuring out if a certain drug has a positive influence on certain patients, and derive some scientific conclusions based on that. That doctor may not be able to write a script to save his life, but he can make use of the data he gathers and advance his scientific field, using just statistics. It’s quite unlikely that this kind of person, who is usually too busy or just not technically adept enough, will take up an A.I. approach to this kind of analysis any time soon. Of course, A.I. constantly evolves so the blackbox issue that makes many ANNbased systems unfavorable, may wane in the future. Already there are A.I. professionals talking about A.I. systems that offer some kind of interpretability. So, even if statistical systems are easier to understand and communicate, it could be that A.I. hasn't said its final word yet. Whatever the case, I prefer to remain agnostic on this matter. Just like with programming, it’s best to keep one’s options open, when it comes to data science. I’m not a fan of statistics (and never was), but I see value in them and I’m happy to use them to the extent that they offer value to the projects I work on. A.I. may be more of a novel and exciting framework, but if an A.I. system is hard to communicate to the client, or doesn't lend itself to interpretation, then I may not use it everywhere. Just like you don’t take your fancy fringe science book to the beach, you don’t need to show off your A.I. knowhow at every opportunity. Perhaps the humble historic novel is more suitable for reading while sunbathing, just like the humble statistics are fine for describing if sample A is significantly different from sample B.
0 Comments
Leave a Reply. 
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests. Archives
November 2017
Categories
All
