In this lecture, DeepMind Research Scientist and UCL Professor Thore Graepel explain DeepMind’s machine learning-based approach towards AI. He examples of how deep learning and reinforcement learning can be combined to build intelligent systems, including AlphaGo, Capture The Flag, and AlphaStar. This is followed by a short introduction to the different topics and speakers coming up in the subsequent lectures.

Thore Graepel is a research group leader at DeepMind and holds a part-time position as Chair of Machine Learning at University College London. He studied physics at the University of Hamburg, Imperial College London, and Technical University of Berlin, where he also obtained his PhD in machine learning in 2001. After postdoctoral work at ETH Zurich and Royal Holloway College, University of London, Thore joined Microsoft Research in Cambridge in 2003. At DeepMind since 2015, Thore leads the multi-agent research team and contributed to AlphaGo, the first computer program to defeat a human professional player in the full-sized game of Go.

The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. Over the past decade, Deep Learning has evolved as the leading artificial intelligence paradigm providing us with the ability to learn complex functions from raw data at unprecedented accuracy and scale. Deep Learning has been applied to problems in object recognition, speech recognition, speech synthesis, forecasting, scientific computing, control and many more. The resulting applications are touching all of our lives in areas such as healthcare and medical research, human-computer interaction, communication, transport, conservation, manufacturing and many other fields of human endeavour. In recognition of this huge impact, the 2019 Turing Award, the highest honour in computing, was awarded to pioneers of Deep Learning.

In this lecture series, research scientists from leading AI research lab, DeepMind, deliver 12 lectures on an exciting selection of topics in Deep Learning, ranging from the fundamentals of training neural networks via advanced ideas around memory, attention, and generative modelling to the important topic of responsible innovation.

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