# Issue 61  June 4th 2018

## Editorial

### Thoughts From Your Humble Curators

This week, we take a closer look at Google's involvement in project Maven, and how it ends abruptly after complaints from Google's employees.

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Artificial Intelligence and Deep Learning Weekly

## Member's Question

### I realized ML is just Statistics, should I feel demotivated?

Question: Anyone had the feeling where you feel very motivated and eager to learn machine learning, and once you actually start, you realize it's just stats which is something you really don't like, and become completely demotivated?

Answer: (By Arthur) There are two parts of your frustrations. First, it's that you equate machine learning and statistics. Second it is you feel that statistics is boring. So let's address the second part first. Then I will come back to the first part.

Is statistics boring? I guess many people who learn statistics usually learn Math first. If that's your route, then perhaps one of the reasons why statistics is boring is that it is empirical and deal with imperfect phenomenon of the world. So unlike Euclidean geometry, or solving quadratic or cubic, you can't quite come up with an exact solution.

To many people's dissatisfaction though, the world is better to be described to be uncertain, rather than certain. Unfortunately, only statistics can teach us more in the realm of uncertainty. So statistics is actually a rescue, and I personally feel grateful for the subject.

Can statistics be fun? You ask. It all depends on how you look at it. e.g. It took me a while to find a good proof of how a full covariance matrix can be estimated through maximum likelihood. In particular, in matrix form, the math is quite interesting. I end-up bought a book by Abadir and Magnus called "Matrix Algebra" and browse it from time to time. I am pretty sure it is boring to some, but it's a lot of fun to me. Btw, Matrix Algebra can be quite mathematical too. But you may say it is more technical type of Math.

My conclusion of the second part is: are you sure you see everything in statistics and machine learning? There are many deep topics in both subjects. But then you might miss nuance in your first glance. So that's that. Of course, your frustration might come from your personal philosophy. Perhaps you don't like uncertainty? Perhaps you don't like the time-consuming process of collecting data? No one can blame you for that. You just have to be honest to yourself.

Let’s go back to the first part on whether machine learning is just statistics. And this is slightly controversial. Let me just quote one prominent person. Say if you ask Prof. Nil Nilsson, he once said machine learning just the subject of "machine to learn". But statistics is clearly more focused on the data and its observation. So if the fancy image of an intelligent robot is doing things was what attracts you, yeah, ML is the subject to learn. It's just that modern theory of ML has found that statistics is very important.

So why is that the case? Oh well, it's not like people love to be statistical, it has to do with nature is better described by uncertain rules. So say speech recognition? People would love to create several rules of phonetics and do speech recognition. In fact they were tried by PhD students in 60s. So in 70s, people start to realize that's not the way to go. Ha tada, here comes HMM. Boring it is. But it is the basis of many previous generation ASR before seq2seq NN models. In fact, there are still many HMM-based system.

So, to summarize, if you are disappointed by ML. Ask whether reality is better described by certainty or uncertainty. It will help you to have a closure.

Artificial Intelligence and Deep Learning Weekly