University of Cambridge > Talks.cam > Machine Learning @ CUED > A talk in two parts: (1) AI Neuroscience: How much do deep neural networks understand about the images they classify? (2) Robots that can adapt like animals.

A talk in two parts: (1) AI Neuroscience: How much do deep neural networks understand about the images they classify? (2) Robots that can adapt like animals.

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If you have a question about this talk, please contact Zoubin Ghahramani.

Note venue: James Dyson Building

A talk in two parts: (1) AI Neuroscience: How much do deep neural networks understand about the images they classify? (2) Robots that can adapt like animals.

The first part of the talk describes our sustained effort to study how much deep neural networks know about the images they classify. Our team initially showed that deep neural networks are “easily fooled,” meaning they will declare with near certainty that completely unrecognizable images are everyday objects, such as guitars and starfish. These results suggested that deep neural networks (DNNs) do not truly understand the objects they classify, but instead latch onto a few discriminative features per class. However, our subsequent results reveal that DNNs actually have a surprisingly deep understanding of objects. These new techniques can also be applied to hidden units in the network, enabling us to study the features that each neuron has learned within a network. Our techniques also generate high-resolution, realistic images, and can thus be thought of as generative models. I will present a new, improved, unpublished, generative model that we believe may represent the state of the art in terms of generating a diverse collection of high-quality, high-resolution images. The second part of the talk describes our Nature paper on learning algorithms that enable robots, after being damaged, to adapt in 1-2 minutes and soldier on with their mission.

AI Neuroscience:

Robots that can adapt like animals (2015) Nature (video summary)

More at http://www.evolvingai.org

This talk is part of the Machine Learning @ CUED series.

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