Starting a Career in AI

A member of AIDL asked: “How do I start my career in artificial intelligence?”

Answered by Arthur:

“As I promised. Here are few thoughts on the topic. I will focus on more commercial applications of ML. Of course you can be a professor, but you should know thats a moonshot.

I’d like to organize them as four parts: should you?, learning, starting out, and first 2-5 years.

should you?

I like Race Vanderdecken’s comment the most. Being MLE is not for everybody. If you go through general CS education, chances are you would be trained as a competent programmer. But being MLE means you need to sit model training, analyze results. Your instinct of finishing something fast as learned from competitive programming is useless. MLE favors slow and deliberate thinking which is not the norm in our fast-paced world.

learning

ML is a topic you should learn and can learn in great detail first before practice it. So if you are in school, take as many ML classes as possible. You should spend at least 50% of time to learn the practice of ML. So like train a classifier? Think of ways to improve its classification rate? Improve its inference speed? You should think about these issues every time you work on a new project. And your worth has a lot of do with this experience you accumulate.

starting out

So how do you actually get hired as MLE then? You go to seek one. The idea is similar to any job seeking process: present your resume to your potential employers, then pitch yourself to them. There are other routes: Someone might recommend you. You might have done an internship in the company before so they like you. It can be a placement program. But in any cases, you got to build up your skillset, and present it well in a resume.

What do people look for in candidates? First off, it’s your project portfolio. Suppose you want to work for a computer vision company, you really want to have some compelling projects on image processing. So if you tell me you train MNIST, I would think, “Okay this guy went through the basics”. But if you told me you train the whole Imagenet at home, then I would think, “ah, that’s not easy”.

Then it is your general knowledge in ML. In an interview, senior engineers would usually probe holes on your understanding and In ML, there are many misconceptions. e.g. Many people will give you silly and unsubstantiated reasonings on what deep learning is, like “it uses big data”, like “DL is just deeper than ML”. Those answers are hand-waiving and it doesn’t quite explain what deep learning is.

Another thing I do in interview: I just go ahead to look the projects quoted in a candidate resume, and asked detailed questions on each of them. Very quickly, you would realize if someone worths one’s salt.

first 2-5 years

If you are successful and got hired, you will start to go through the daily chores of being MLE. So what do MLE does? For the most part you try to make a living through machine learning. The key metric here: Do something you make being used? What that entails is you want to create an ML product, and refine it to a point that a company can sell it. There are many things to unpack here. Because just to create something in ML is hard, but usually the prototype performance would be too bad for production. Or if something is good for production, the company might just decide they don’t want to sell it.

So whether you can start out has everything to do with hardwork plus a lot of luck. My suggestion there is to start with small projects within a company, then build up your reputation. Make sure you are employed, because if you want to get better in ML, you have to keep educating yourself and that cost time and $.

after 5 years

I also have advices for people who stay in the business for around 5 years. But this comment is getting long, so let’s leave it next time?”

Artificial Intelligence and Deep Learning Weekly