Artificial Intelligence job titles, simplified.

Artificial Intelligence is one of the phenomena having the most impact on our lives and economy during the modern technological era. However, an interesting phenomena could be observed; Since it is relatively new, there’s a sort of confusion regarding artificial intelligence skills, jobs and expectations. When the AI abrupt high demand faced the talent shortage reality, a misunderstanding mist has scattered wrong LinkedIn titles and multiple project disappointments.

Through an example, I will share a simple way that could help both of the talent seekers and the AI providers to communicate better their role and manage their expectations.

Machine learning as a Service: the car driver

Let’s start with a factual observation. Technically, we don’t need to look for statistics to affirm with a high level of certainty that there are more car drivers than people building or repairing cars. It’s natural.

Cars are useful. For those who need it, it makes their lives easier and efficient. So in order to drive a car, basically you need some learning and a driving licence. To benefit of its services, you go to your neighbourhood car dealer and depending on your budget, you can acquire a car. Let’s see the situation from an AI perspective. Machine Learning as a service that Google, Amazon or Microsoft provide (the car dealer) is very useful. We need it simply because not all the people are able to build a car. Moreover, not all of them are motivated to understand how a car engine works or have enough of patience to build their own in their backyard.

In the AI professional jungle, it could be not appropriate to say: “Yes, I do AI” just because you can drive a car. You can even be a cab driver (AI services Integrator), but again, it doesn’t automatically mean that you have the right skills that make you able to assemble a car or design one. Many people are not able to drive, so the cab and Uber drivers area real need in our streets.

The AI developer: the car mechanic

The car you drive is assembled by some human and many robotic hands. When it breaks, the most evident reaction is to call the tow truck that will take your car to the mechanic. The mechanic (AI developer) is someone who knows how your car works and why it’s not. To be there, he got the right theoretical understanding about engines and has the right knowledge that makes him able to classify the different part of your car. The mechanic or the AI developer understands how each part is interacting with the other. Your mechanic owns some tools (wrench, car lift, diagnostic computer…). He/She should master to make his/her life easy. In the artificial intelligence world, those tools a commonly know as the frameworks like Torch, Tensorflow or Caffe.

Your mechanic could do an amazing work with your car. He / She could assemble, disassemble your car or even tune its performance. But imagine if I ask him to invent a new kind of Turbo engine. Do you think he /she will agree? I don’t think so. If he / she agrees, I’ll make my distance. If you need someone to design an AI model that solves your untapped problem (there are many of them), you need an AI Scientist.

The AI scientist: the car designer

How many car brands do exist? Not as much as mechanist I guess. The car designer the person who draws your car, pushes the limits of its engine and thinks your future of your car-related usage. Behind a big sheet of paper and a computer screen, his job is to read, understand, model, try, test, observe and define a concrete approach that improves your state of the art. As a simple driver or passenger, I don’t think that you really need to know who he/she is. You generally don’t have a direct relation with him/her and you even need to master all the details about every line he / she draws.

Indeed, the car designer knows also how mechanic works. He/She has the right skills that make him / her able to use the different machine learning frameworks in order to test and observe the work. It’s rare to have a job where other people execute and test your models during each trial. It will be long and costly.

Indeed, the car designer should have a good understanding of the existing models and their limits. He/She needs a deep understanding of the field. You probably understood that the Car designer is rarely a one unique person. I doubt that the person that improved the gas consumption of your car is the same one that worked on the cruise control. It’s almost impossible to find a car designer that masters deeply every tiny aspect about a car. So the car designer is more a specialist job. They have a good understanding of the general mechanisms but they own one or two applications subfield (computer vision, natural language processing…).

Why are AI scientists still so highly demanded?

In artificial intelligence reality, AI scientists and researchers are still highly demanded for two main reasons:

• Market maturity: AI technology is powerful and transformative. However, it’s fresh. It needs much more time to set up standards and automatic services that fulfill the needs of every industry. The market is full of untapped use cases what could justify the direct implication of an AI scientist.
• AI nature: a learning algorithm owes its intelligence to the data it observes. In reality, your data has a different structure than mine. Apart from rare cases where we share almost the same data structure, semantics and usage, in most of the case, there’s no turnkey learning model ready to perform in my organization. We’re discovering more approaches that could fill this gap (eg: Transfert Learning), but its performance is still not automatic or guaranteed.

Owning the car is not enough

Owning a car is not enough. You need probably road infrastructures, traffic laws, parking, etc. There’s multiple essential roles roaming around the AI ecosystem without “doing AI”. First, your car needs a proper street (like those in Montreal :) ). Same for your learning model; to perform, it requires a minimum of proper data. This data is made practicable and available thanks to the precious contribution of the road works that we call data workforce (data developer, data scientist, ETL developer, DBA…).

So, Data scientist, data engineers, back-end developers, user experience designer, … all of them are rarely an option. They are critically important to drive your artificial intelligence project to the success.