Michael Green: Deep probabilistic neural networks – A way forward

Abstract:

In a world where deep learning and other massively scalable perception machines are at our disposal, allowing us to build amazing applications, the time is now ripe to move beyond the concept of pure perception and into broader Artificial Intelligence (AI). The path towards AI goes through what’s missing in many applications today; Inference. Only when we combine Inference machines and Perception machines can we truly talk about AI. The benefit will be a machine that knows what to expect before observing it’s environment and that can take prior information into account. With ever more mature Probabilistic programming languages available, we can express this marriage of perception and inference. In this talk, we will scrape the surface of how to build Bayesian predictive inference machines using Probabilistic programming.

Bio:

An ex special forces operative turned theoretical physicist with a passion for ingesting artificial intelligence solutions into all aspects of our lives. Especially interested in the concept of uncertainty applied to generalized graphical models. As Chief AI Office at Blackwood Michael oversees the development of the next generation AI engine for media analytics, optimization and planning. He lives in Copenhagen with his son Hamiltonian and girlfriend Martina, and enjoys martial arts and outdoor activities when not in front of a computer.

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