In the News

August 24, 2017

UCI Applied Innovation

Husky or wolf? Using a black box learning model to avoid adoption errors

Say you want to adopt a dog, from a picture, and you task your machine learning system to classify the image as either a husky, which would be safe to adopt, or a wolf, which probably is not a good idea. Can you get that photograph classified with certainty? “Because researchers don’t have insights into what is going on they can easily be misled,” said Sameer Singh, assistant professor in the UCI Department of Computer Science. “Classification is core to machine learning,” said Singh, describing ‘black box’ machine learning predictions at the Association for Computing Machinery (ACM) July 12 meeting at the Cove. Machine learning is pervasive in our lives—from email to games. “It’s in our phones,” said Singh, a machine learning and natural language processing expert. “It is in our houses. It is basically everywhere.”One of his students created a wolf/dog classifier in a few hours that seemed to work—at first.

Read the full story on the UCI Applied Innovation website.


Husky or wolf? Using a black box learning model to avoid adoption errors

< Previous
Dutt, Levorato awarded NSF grant for healthcare IoT research
Next >
Big Data and Jazz Hands: How One Company Aims to Bring Silicon Valley to Broadway (ICS alumnus Tim Kashani quoted)