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Thursday, May 5 • 4:45pm - 5:30pm
Panel: Do Androids Dream? Deep Visual Abstraction from Artificial Neural Networks

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************ UPDATE May 6 ********
Go to https://github.com/DoAndroidsDream for presentations and additional resources

Astonishing images, activated from deep inside machine learning models, lead us to speculate about the roots of human visual imagination.

This panel will survey leading edge applications and research directions, summarize open source tools and resources, and explore how our understanding of human visual experience may be furthered.

This panel will be comprised of experts in visual arts and sciences, including animal and computer vision researchers, art historians and visual design practitioners and/or academics.

An intense curiosity about science and human experience is the only pre-requisite for attending this panel.


In New York in the 1980s, inspired by biological models of primate animal vision, Yann LeCun developed Convolutional Neural Networks (CNNs), a machine learning technique that enabled fast, robust and practical automated image and speech recognition.

Now, thirty years later, with computing power far cheaper and faster, very deep and flexible CNNs are routinely learning to categorize vast varieties and quantities of images and videos on the Web.

Deep CNNs learn by applying simple rules over and over again to training inputs consisting of enormous sets of images and metadata. While deep CNNs routinely produce easy to understand and useful outputs, the full nature of their inner workings were, until recently, considered by most scientists to be beyond the reach of human understanding. This transcendent [unfathomable] property of CNNs was thought to be a necessary consequence of the probabilistic nature of the inputs and the explosive [exponential] complexity of repeating the inner-most steps billions of times in seemingly random order.

Then, unexpectedly, in 2012, Andrew Y. Ng, then at Stanford, along with Google scientists reported finding a somewhat abstract image of a cat buried deep within a deep learning machine model that had been running on 16,000 computers [1]. The remarkable thing was that the computer training, . .

avatar for Gizem Küçükoğlu

Gizem Küçükoğlu

PhD Candidate in Psychology, NYU
I am currently pursuing a Ph.D. at NYU with a focus on human vision. My research focuses on trying to answer questions like how does human visual system process the 3D world, colors, light in the environment and surface materials. I have a background in Computer Science so I am interested... Read More →

avatar for Nicholas Lambert

Nicholas Lambert

Head of Research, Ravensbourne, London
Dr Nick Lambert is Head of Research at Ravensbourne. Nick’s interests revolve around the digital medium and its application in contemporary art and visual culture. Through this, he engages with questions about the boundary between “fine” and “applied” arts, design and interfaces... Read More →
avatar for Gene Miller

Gene Miller

Principal Consultant, DVI Science Ltd
Mathematician, Statistician.
avatar for Cassidy Williams

Cassidy Williams

Software Engineer & Developer Evangelist, Clarifai
Cassidy is a software engineer and developer evangelist at Clarifai.  She's worked for Venmo, Intuit, Microsoft, and General Mills, and graduated with a computer science degree from Iowa State University in 2014.  She's had the honor of working with various organizations, including... Read More →
avatar for Dr. Matthew Zeiler

Dr. Matthew Zeiler

Founder & CEO, Clarifai
Matthew Zeiler is an expert in the field of neural networks and Founder and CEO of Clarifai. After having learned from pioneers of neural networks including Geoff Hinton and Yann LeCun he started Clarifai in November 2013 upon completion of his PhD from New York University. He set... Read More →

Thursday May 5, 2016 4:45pm - 5:30pm EDT
Clemente Center