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Evolutionary computation: The next deep learning

Risto Miikkulainen (Sentient.ai)
11:05am-11:45am Friday, September 7, 2018
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Location: Continental 5 Level: Intermediate
Secondary topics:  Deep Learning models, Reinforcement Learning
Average rating: *****
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Who is this presentation for?

  • CIOs, CTOs, and data scientists

Prerequisite knowledge

  • Familiarity with deep learning

What you'll learn

  • Learn how DL systems are becoming too complex to scale, why EC is the best solution for industrializing AI, and how EC brings creativity to AI
  • Explore breakthroughs being made in the commercialization of EC

Description

Deep learning (DL) has transformed much of AI and demonstrated how machine learning can make a difference in the real world. With DL, the massive expansion of available training data and compute gave neural networks a new instantiation that significantly increased their power. Evolutionary computation (EC) is on the verge of a similar breakthrough. Risto Miikkulainen explains why.

While DL is focused on modeling what we already know, EC addresses a different but equally far-reaching problem: creating solutions that do not yet exist. EC accomplishes this not by following a gradient (like most DL and reinforcement learning approaches) but by doing massive exploration: using a population of candidates to search the space of solutions in parallel, emphasizing novel and surprising solutions. Thus, EC makes a host of new AI applications possible, from designing more effective and economical buildings, vehicles, and websites to discovering more effective and efficient behaviors for robots and virtual agents to more effective and cheaper health interventions and growth recipes for agriculture. In that sense, EC is the next frontier of AI beyond deep learning.

Photo of Risto Miikkulainen

Risto Miikkulainen

Sentient.ai

Risto Miikkulainen is CTO at Sentient Technologies and a professor of computer science at the University of Texas at Austin. His recent research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing and vision. Risto has published over 370 articles in these research areas and has 16 patents pending. He is an IEEE fellow and a recipient of the 2017 Gabor Award of the International Neural Network Society. Risto holds an MS in engineering from the Helsinki University of Technology, Finland, and a PhD in computer science from UCLA.