In 1987 Professor Kappen received his PhD in theoretical physics from Rockefeller University in New York. After having conducted research at Philips for two years, he returned to academia in 1989. In a quest to understand human-kind better, he changed his research area to neural networks and machine learning.
Professor Kappen’s research interests lie at the interface between statistical physics, computer science, computational biology, control theory and artificial intelligence. For years, he has argued that computing, memory, and energy consumption will be the main challenges for AI’s future growth. Instead of limiting his research by these challenges, professor Kappen recently published a revolutionary article in Nature titled ‘An atomic Boltzmann machine capable of self-adaption’. In this work, he has demonstrated the possibility of training a neural network at the nuclear level. This finding may be the precursor of a new generation of algorithms consuming only a fraction of current models’ energy absorption. During this interview, we will discuss what professor Kappen found, the potential impact of his findings, and whether we can draw links between his findings and humans.
Professor Kappen, do you primarily consider yourself to be a physicist or a computer scientist?
That is a difficult question. Most of my publications are in machine learning journals and conferences. In that sense, I am a computer scientist. However, the methods and methodologies that I use mostly, stem from physics. So, I consider myself primarily a physicist in the end.
For instance, much of my work has been on approximate inference, which efficiently approximates computations for low-order statistics in very large probability models. This problem is dominated by various techniques such as variational methods and Monte Carlo sampling, all rooted in physics. In much of my work, I transfer these technologies from the physics community to machine learning and AI.
Why did you decide to transition from physics to computer science?
I did my PhD in theoretical high energy particle physics and was always fascinated by the sophistication of the field’s mathematics. It has incredible beauty, we have had incredible achievements, and it has been a privilege to learn about it.
At the same time, I felt that it was very remote from my personal daily life.
While pursuing my PhD, I was always very interested in how my brain works. The notion of the ‘I’…
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