Most programmable students, if they understand these two concepts, will easily switch between the two tasks, for example, I use both Machine Learning and Data Science knowledge in my work, I can fit the traffice data model on Stack Overflow to determine which users are capable of finding a job (Machine Learning), but then build summaries and images to check why this model (Data Science) works. This is an important way to detect flaws in your model and to contest algorithmal deviations. This is one of the reasons that data science programmeers are often responsible for developing machine learning components of a product.

3/ Artificial intelligence creates action

AI is the intelligence expressed by any artificial system. It is the future, the science of fiction, and a part of our daily lives. AI was born a long time ago and is widely recognized, most commonly in the programming world, and therefore quite difficult to fully understand in this field.

A common theme in the definition of “artificial intelligence” is an independent substance that performs or proposes action (e.g. Poole, Mackworth and Goebel 1998, Russell and Norvig 2003). Some of the systems I think should be mentioned when describing AI:

Gaming algorithms (Deep Blue, AIphaGo)
Theory of robotics and control (motion planning, walking with a bipedAI robot)
Optimization (Google Maps select route)
Natural language processing (bots2)
Enhanced and accelerated learning

Once again, we can see the intertwining of AI, Machine Learning and Data Science when creating a product, Deep learning has been a very active topic of AI discussion. This is an algorithm based on ideas from the human brain to the reception of multiple layers of expression. It has driven progress in areas as diverse as cognitive, automatic translation, voice recognition….

But there are also differences, if I analyze some sales data and discover that customers from specific industries are more likely to close contracts than other customers, you can give a number and graph rather than a specific action. Managers can use those conclusions to change sales strategies, but they’re not automatically available. This means I will describe my work as a data science programnist.

4 / Specific case: How to use all 3 fields in a project?

Let’s say we study the production of a self-driving car and are working on stopping at places where there are signs of stopping. We will need specific skills for these three areas:

+ Machine Learning: The car must recognize the stop sign with the camera. We build a data set of millions of street photos and set up an algorithm to predict those stop signs.

+ AI: Once the car can recognize the stop signs, it needs to decide when to take action to apply the brakes. Applying too early or too late is very dangerous and we need AI to handle different road conditions (for example, on this steep slippery road, it is recommended to slow down). This is the theoretical issue of control action.