Is it feasible to create Strong AI and how can we get there? Comparing today’s AI with biological intelligence, one of the most remarkable differences is the ability of animal brains to somehow understand the ‘essence’ of things: Even a small child can easily recognize a cat after having seen only a few examples. We recognize a song even if played on different instruments or in a different key. And we recognize a story independent of whether we see it as a movie, hear it as an audio play or read it in a book. In other, more technical words: Brains are able to create invariant representations of real-world entities – and today’s algorithms are not. I will argue that the problem of invariant representations is the key to progress towards Strong AI and that the brain can serve as an inspiration for the next generation of machine learning algorithms. Even though today there is a very limited understanding of how the brain works conceptually, i.e. what the ‘brain algorithm’ is, I’ll explain why there is hope that we can reverse-engineer this biological algorithm and that we can implement it in a computer. I’ll explain why a highly interdisciplinary approach is needed from neuroscience, computer science, mathematics and physics to make progress in this important question.
ABOUT THE SPEAKER:
Dr. Helmut Linde is the Global Head of Data Science at the vibrant science and technology giant Merck KGaA. He leads an organization of expert data scientists who drive innovation in the field of advanced analytics and artificial intelligence. Before joining Merck KGaA in 2017, Helmut has held various leadership roles at the software company SAP in the field of data science, overseeing the development and implementation of customer-specific forecasting and optimization solutions. Helmut has an academic research background in mathematics and physics.
