Open-endedness: A new grand challenge for AI
We think a lot in machine learning about encouraging computers to solve problems, but there's another kind of learning, called open-endedness, that's just beginning to attract attention in the field. Kenneth Stanley walks you through how open-ended algorithms keep on inventing new and ever-more complex tasks and solving them continuallyeven endlessly.
|Talk Title||Open-endedness: A new grand challenge for AI|
|Speakers||Kenneth Stanley (Uber AI Labs|
|Conference||O’Reilly Artificial Intelligence Conference|
|Conf Tag||Put AI to Work|
|Location||San Jose, California|
|Date||September 10-12, 2019|
We think a lot in machine learning about encouraging computers to solve problems, but there’s another kind of learning, called open-endedness, that’s just beginning to attract attention in the field. Open-ended algorithms keep on inventing new and ever-more complex tasks and solving them continually—even endlessly. Kenneth Stanley explores how, if you could genuinely program such open-endedness, the longer your algorithms ran, the more interesting and powerful the results they would produce. Endless designs, expanding repertoires of skills, exploding intelligence—these are the rewards of open-endedness. Interestingly, open-ended phenomena are all around—the history of human invention, the unfolding odyssey of art and music, the career of a creative trailblazer, and most dramatic of all, the awesome divergence of natural evolution into all the diversity of life on earth—the open-ended processes can offer an entirely different level of automated creation. Beyond deep learning and beyond the benchmarks of today, open-endedness offers a new path and a new quest for the future.