Risto Miikkulainen

Risto Miikkulainen is Associate VP of Evolutionary AI at Cognizant and a Professor of Computer Science at the University of Texas at Austin. His current research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing and vision, and he is an author of over 400 articles in these research areas.

At Cognizant, he is scaling up these approaches to real-world problems. Prior to Cognizant, he was CTO of Sentient Technologies. Risto is an IEEE Fellow, and his work on neuroevolution has recently been recognized with the Gabor Award of the International Neural Network Society and Outstanding Paper of the Decade Award of the International Society for Artificial Life.

Risto received an MS in engineering from Helsinki University of Technology (now Aalto University) in 1986, and a Ph.D. in computer science from UCLA in 1990. He can be reached at Risto.Miikkulainen@cognizant.com and https://www.linkedin.com/in/risto-miikkulainen-ab43b9b8/

Vaccinations Are Coming – and AI Is Needed More than Ever (Part 2)

As COVID-19 infects more people worldwide than ever before in its nearly year-long rise to global pandemic proportions, hope is also emerging. Several vaccine trials have proved safe and effective, and vaccination programs are moving forward globally that could eventually end the pandemic. This makes our Pandemic Response Challenge with XPRIZE even more timely and relevant […]

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Even Inexact Data Can Be Meaningful to AI – and Other COVID Lessons Learned (Part 1)

COVID-19 has turned our professional and personal lives upside down. But it’s also giving us a crash course in using artificial intelligence (AI) to predict and navigate an uncertain future. While there’s still a lot to learn, we believe so strongly in the potential for AI to lessen the social and economic impacts of COVID-19 […]

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Four Ways Evolutionary AI Can Extend AI’s Problem-Solving Capacity

Deep neural networks (DNN) have produced groundbreaking results in many complex applications of AI, such as natural language processing, facial recognition, sentiment analytics and object recognition. For instance, the accuracy of Google’s machine translation system improved 60% using a DNN approach. Finding the right network architecture – that is, the components of the network and […]

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