Dusan Misevic's guest appearances

Dusan Misevic

Machine Learning Archives - Software Engineering Daily

Software Daily

Zach Allen

Jeff Johnson

Simon Chan

Dario Amodei

Lukas Biewald

Kelley Rivoire

Mike Tung

Alexandr Wang

Tim Williamson

Andrew Mason

George Hotz

Xin Wang

Mike Lee

Adam Gibson

Charlie Gerard

OwlTail

Joel Lehman

Jeff Clune

Evolutionary algorithms can generate surprising, effective solutions to our problems.  Evolutionary algorithms are often let loose within a simulated environment. The algorithm is given a function to optimize for, and the engineers expect that algorithm to evolve a solution that optimizes for the objective function given the constraints of the simulated environment. But sometimes these results are not exactly what we are looking for. For example, imagine an evolutionary algorithm that tries to evolve a creature that does a flip within a simulated physics engine that mirrors the real world.  You could imagine all sorts of evolutionary traits. Maybe the creature will evolve to have legs that are like springs, and let the creature jump high enough to do a flip. Maybe the creature will develop normal legs with strong muscles that propel the creature high enough to flip. But you wouldn’t expect the creature to evolve to be extremely tall–so tall that the creature can merely lean over fast enough so that the top of its body flips upside down. In one experiment, this is exactly what happened. In another, similar experiment, the evolving creature discovered a bug in the physics engine of the simulated environment. This creature was able to exploit the problem with this physics engine to be able to move in ways that would not be possible in our real-world physical universe. Evolutionary algorithms sometimes evolve solutions in ways that we don’t expect. Researchers usually throw those results away, because they don’t contribute to the result that the researchers are looking for. The consequence is that lots of interesting anecdotes get lost. Joel Lehman, Dusan Misevic, and Jeff Clune are the lead authors of the paper “The Surprising Creativity of Digital Evolution.” The paper was a collection of anecdotes about strange results within the world of digital evolution. They join the show to describe what digital evolution is and some of the strange results that they surveyed in their paper. Joel and Jeff are engineers at Uber’s artificial intelligence division–so this topic has applicable importance to them. Machine learning is all about evolution within simulated environments, and developing safe algorithms for AI requires an understanding of what can go wrong in a poorly defined evolutionary system. The post Digital Evolution with Joel Lehman, Dusan Misevic, and Jeff Clune appeared first on Software Engineering Daily.

Evolutionary algorithms can generate surprising, effective solutions to our problems. Evolutionary algorithms are often let loose within a simulated environment. The algorithm is given a function to optimize for, and the engineers expect that algorithm to evolve a solution that optimizes for the objective function given the constraints of the simulated environment. But sometimes these results are not exactly what we are looking for. For example, imagine an evolutionary algorithm that tries to evolve a creature that does a flip within a simulated physics engine that mirrors the real world. You could imagine all sorts of evolutionary traits. Maybe the creature will evolve to have legs that are like springs, and let the creature jump high enough to do a flip. Maybe the creature will develop normal legs with strong muscles that propel the creature high enough to flip. But you wouldn’t expect the creature to evolve to be extremely tall–so tall that the creature can merely lean over fast enough so that the top of its body flips upside down. In one experiment, this is exactly what happened. In another, similar experiment, the evolving creature discovered a bug in the physics engine of the simulated environment. This creature was able to exploit the problem with this physics engine to be able to move in ways that would not be possible in our real-world physical universe. Evolutionary algorithms sometimes evolve solutions in ways that we don’t expect. Researchers usually throw those results away, because they don’t contribute to the result that the researchers are looking for. The consequence is that lots of interesting anecdotes get lost. Joel Lehman, Dusan Misevic, and Jeff Clune are the lead authors of the paper “The Surprising Creativity of Digital Evolution.” The paper was a collection of anecdotes about strange results within the world of digital evolution. They join the show to describe what digital evolution is and some of the strange results that they surveyed in their paper. Joel and Jeff are engineers at Uber’s artificial intelligence division–so this topic has applicable importance to them. Machine learning is all about evolution within simulated environments, and developing safe algorithms for AI requires an understanding of what can go wrong in a poorly defined evolutionary system. The post <a href="https://softwareengineeringdaily.com/2018/06/15/digital-evolution-with-joel-lehman-dusan-misevic-and-jeff-clune/" rel="nofollow" target="_blank">Digital Evolution with Joel Lehman, Dusan Misevic, and Jeff Clune</a> appeared first on <a href="https://softwareengineeringdaily.com" rel="nofollow" target="_blank">Software Engineering Daily</a>.

Digital Evolution with Joel Lehman, Dusan Misevic, and Jeff Clune