The Blogger Behind “AI Weirdness” Thinks Today’s AI Is Dumb and Dangerous

Sure, artificial intelligence is transforming the world’s societies and economies—but can an AI come up with plausible ideas for a Halloween costume?  Janelle Shane has been asking such probing questions since she started her AI Weirdness blog in 2016. She specializes in training neural networks (which underpin most of today’s machine learning techniques) on quirky data sets such as compilations of knitting instructions, ice cream flavors, and names of paint colors. Then she asks the neural net to generate its own contributions to these categories—and hilarity ensues. AI is not likely to disrupt the paint industry with names like “Ronching Blue,” “Dorkwood,” and “Turdly.”  Shane’s antics have a serious purpose. She aims to illustrate the serious limitations of today’s AI, and to counteract the prevailing narrative that describes AI as well on its way to superintelligence and complete human domination. “The danger of AI is not that it’s too smart,” Continue reading The Blogger Behind “AI Weirdness” Thinks Today’s AI Is Dumb and Dangerous

Visualizing an AI model’s blind spots

Anyone who has spent time on social media has probably noticed that GANs, or generative adversarial networks, have become remarkably good at drawing faces. They can predict what you’ll look like when you’re old and what you’d look like as a celebrity. But ask a GAN to draw scenes from the larger world and things get weird. A new demo by the MIT-IBM Watson AI Lab reveals what a model trained on scenes of churches and monuments decides to leave out when it draws its own version of, say, the Pantheon in Paris, or the Piazza di Spagna in Rome. The larger study, Seeing What a GAN Cannot Generate, was presented at the International Conference on Computer Vision last week. “Researchers typically focus on characterizing and improving what a machine-learning system can do — what it pays attention to, and how particular inputs lead to particular outputs,” says David Bau, a graduate student at MIT’s Department of Electrical Engineering and Continue reading Visualizing an AI model’s blind spots