The mathematical side of AI

The mathematical side of AI

Show a child a dog three times and a cat four times, and it will remember what they are for the rest of their lives. A computer is not that intelligent yet. Before it can make the distinction, it will need thousands of pictures of dogs and cats.

“The distinction between a dog or a cat is not too relevant. Still, if you want to teach a computer to drive a self-driving car, the distinction between a child or a rabbit is of vital importance”, says Johannes Schmidt-Hieber. He is a professor of statistics, the youngest professor at the University of Twente and an expert on AI.


Schmidt-Hieber to INN’ Twente, on “Our brains are much more efficient in image recognition. For example, a computer gets confused when there are multiple objects on a picture; our brains automatically filter that information. A student from my group had a computer develop new dishes based on many existing recipes. Computers can also make paintings based on the style of certain painters. But it is always a derivative of what already exists. Coming up with an own style is still a bridge too far.”

“Coming up with their own style is still a bridge too far for a computer.”
Johannes Schmidt-Hieber

However, the professor certainly does not want to be derogatory about the importance of AI. “Much data is available at hospitals. By using AI to link data and compare past experiences – in cancer treatment, for example – you can choose the most successful combination of medicine and radiation.”

Statistics as a starting point 

Schmidt-Hieber is considered an expert in the field of AI, and especially about the mathematical side. “Computer science has been working on this for a lot longer, especially on applications. From the mathematical side and my field of statistics, I focus on interpreting the results. With new mathematical techniques, you can analyse which methods work, which do not, and why. How large should the neural network be to ensure the best functioning? We have not yet discovered how the human brain works. It is easier to discover the functioning of computers. It has long been an underexposed aspect; everything was focused on applications. To move forward, we will also have to dive into the operations mathematically.

The young professor gives workshops and conferences, especially for academics. Not only is the academic world interested, but so are the research departments of Google and Microsoft. Given the stormy developments, he advocates for having students become acquainted with AI and deep learning early on in education.  “They are the ones that have to work with it later, so it must be integrated into education early on.”


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