Poison Attacks Against Machine Learning

Poison Attacks Against Machine Learning: With AI systems becoming more common, we have to start worrying about security. A network intrusion may be all the more serious if it is a neural net that is affected. New results indicate that it may be easier than we thought to provide data to a learning program that causes it to learn the wrong things.

Cornell computers spot 'opinion spam'

If you read online reviews before purchasing a product or service, you may not always be reading the truth. Review sites are becoming targets for "opinion spam" -- phony positive reviews created by sellers to help sell their products, or negative reviews meant to downgrade competitors.

The bad news: Human beings are lousy at identifying deceptive reviews. The good news: Cornell researchers are developing computer software that's pretty good at it. In a test on 800 reviews of Chicago hotels, a computer was able to pick out deceptive reviews with almost 90 percent accuracy. In the process, the researchers discovered an intriguing correspondence between the linguistic structure of deceptive reviews and fiction writing.

Source: Cornell News via ACM Tech News

Computer learns language by playing games

In 2009, at the annual meeting of the Association for Computational Linguistics (ACL), researchers in the lab of Regina Barzilay, associate professor of computer science and electrical engineering, took the best-paper award for a system that generated scripts for installing a piece of software on a Windows computer by reviewing instructions posted on Microsoft’s help site. At this year’s ACL meeting, Barzilay, her graduate student S. R. K. Branavan and David Silver of University College London applied a similar approach to a more complicated problem: learning to play “Civilization,” a computer game in which the player guides the development of a city into an empire across centuries of human history. When the researchers augmented a machine-learning system so that it could use a player’s manual to guide the development of a game-playing strategy, its rate of victory jumped from 46 percent to 79 percent.

Source: MIT News Office via ACM Tech News

Improving recommendation systems

Devavrat Shah, the Jamieson Career Development Associate Professor of Electrical Engineering and Computer Science in MIT’s Laboratory of Information and Decisions Systems, thinks that the most common approach to recommendation systems is fundamentally flawed. Shah believes that, instead of asking users to rate products on, say, a five-star scale, as Netflix and Amazon do, recommendation systems should ask users to compare products in pairs. Stitching the pairwise rankings into a master list, Shah argues, will offer a more accurate representation of consumers’ preferences.

Source: MIT News Office via ACM Tech News