The Future of Machine Intelligence

Chapter 4. Risto Miikkulainen: Stepping Stones and Unexpected Solutions in Evolutionary Computing

Risto Miikkulainen is professor of computer science and neuroscience at the University of Texas at Austin, and a fellow at Sentient Technologies, Inc. Risto’s work focuses on biologically inspired computation such as neural networks and genetic algorithms.

Key Takeaways

• Evolutionary computation is a form of reinforcement learning applied to optimizing a fitness function.

• Its applications include robotics, software agents, design, and web commerce.

• It enables the discovery of truly novel solutions.

Let’s start with your background.

I completed my Ph.D. in 1990 at the UCLA computer science department. Following that, I became a professor in the computer science department at the University of Texas, Austin. My dissertation and early work focused on building neural network models of cognitive science—language processing and memory, in particular. That work has continued throughout my career. I recently dusted off those models to drive towards understanding cognitive dysfunction like schizophrenia and aphasia in bilinguals.

Neural networks, as they relate to cognitive science and engineering, have been a main focus throughout my career. In addition to cognitive science, I spent a lot of time working in computational neuroscience.

More recently, my team and I have been focused on neuroevolution; that is, optimizing neural networks using evolutionary computation. We have discovered ...