Jeff Clune

Jeff Clune
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Jeff is on leave from the University of Wyoming, where he is the Loy and Edith Harris Associate Professor in Computer Science and directs the Evolving AI Lab (http://EvolvingAI.org). He researches robotics and creating artificial intelligence in neural networks, either via deep learning or evolutionary algorithms. In the last three years a robotics paper he coauthored was on the cover of Nature, he won an NSF CAREER award, he received the Distinguished Young Investigator Award from the International Society for Artificial Life, and deep learning papers he coauthored were awarded oral presentations at NIPS, CVPR, ICLR, and an ICML workshop.
Evolution to running

Accelerating Deep Neuroevolution: Train Atari in Hours on a Single Personal Computer

Applying hardware acceleration to deep neuroevolution in what is now an open source project, Uber AI Labs was able to train a neural network to play Atari in just a few hours on a single personal computer, making this type of research accessible to a far greater number of people.
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Differentiable Plasticity: A New Method for Learning to Learn

Differentiable Plasticity is a new machine learning method for training neural networks to change their connection weights adaptively even after training is completed, allowing a form of learning inspired by the lifelong plasticity of biological brains.

VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution

Uber AI Labs introduces Visual Inspector for Neuroevolution (VINE), an open source interactive data visualization tool to help neuroevolution researchers better understand this family of algorithms.

Welcoming the Era of Deep Neuroevolution

By leveraging neuroevolution to train deep neural networks, Uber AI Labs is developing solutions to solve reinforcement learning problems.

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