As deep learning systems get more complex and their applications get more ambitious, researching and engineering deep learning networks is becoming harder and harder. Sentient Technologies has pioneered a different approach that helps mitigate those difficulties: namely, evolutionary algorithms.
Sentient has built the largest evolutionary algorithm system in the world, scaling to 2 million CPU cores and 4,000 GPUs. Risto Miikkulainen explains how to use massively distributed evolutionary algorithms to evolve the actual architectures of deep networks. It’s AI building AI, and it’s an essential step on the path to industrializing and scaling the application of deep learning.
Risto Miikkulainen is vice president of research 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.
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