The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail--making recommendations based on products popping up in social-media posts, for instance. For instance, one group of Amazon researchers based in Israel developed machine learning that, by analyzing just a few labels attached to images, can deduce whether a particular look can be considered stylish. An Amazon team at Lab126, a research center based in San Francisco, has developed an algorithm that learns about a particular style of fashion from images, and can then generate new items in similar styles from scratch--essentially, a simple AI fashion designer. The event included mostly academic researchers who are exploring ways for machines to understand fashion trends.

Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Kosinski, an assistant professor of organizational behavior, said he was studying links between facial features and political preferences, with preliminary results showing that AI is effective at guessing people's ideologies based on their faces. That means political leanings are possibly linked to genetics or developmental factors, which could result in detectable facial differences. Facial recognition may also be used to make inferences about IQ, said Kosinski, suggesting a future in which schools could use the results of facial scans when considering prospective students.

If there aren't enough examples of a particular accent or vernacular, then these systems may simply fail to understand you (see "AI's Language Problem"). "If you analyze Twitter for people's opinions on a politician and you're not even considering what African-Americans are saying or young adults are saying, that seems problematic," O'Connor says. Solon Barocas, an assistant professor at Cornell and a cofounder of the event, says the field is growing, with more and more researchers exploring the issue of bias in AI systems. Shared Goel, an assistant professor at Stanford University who studies algorithmic fairness and public policy, says the issue is not always straightforward.

They say their decoder significantly outperforms existing approaches. These included a Long Short Term Memory Network, a recurrent neural network, and a feedforward neural network. "For instance, for all of the three brain areas, a Long Short Term Memory Network decoder explained over 40% of the unexplained variance from a Wiener filter," they say. But Glaser and co deliberately reduced the amount of training data they fed to the algorithms and found the neural nets still outperformed the conventional techniques.

The Stanford Heuristic Programming Project: Goals and Activities

Utterance and Objective: Issues in Natural Language Communication

Natural Language Understanding

Research in Progress at the Massachusetts Institute of Technology Artificial Intelligence Laboratory

Elon Musk has hired a new director of AI research at Tesla, and it may signal a plan to rethink the way its automated driving works. This week, Musk poached Andrej Karpathy, an expert on vision, deep learning, and reinforcement learning, from OpenAI, a nonprofit that Musk and others are funding that's dedicated to "discovering and enacting the path to safe artificial general intelligence." After Stanford, Karpathy interned with DeepMind, where reinforcement learning is a major focus. Appointing Karpathy a Tesla's director of AI research indicates something else about the challenge of autonomous driving: there's some distance left to go before it's solved (see "What to Know Before You Get in a Self-Driving Car").

In March the company bought a startup cofounded by Geoffrey Hinton, a University of Toronto computer science professor who was part of the team that won the Merck contest. Extending deep learning into applications beyond speech and image recognition will require more conceptual and software breakthroughs, not to mention many more advances in processing power. Programmers would train a neural network to detect an object or phoneme by blitzing the network with digitized versions of images containing those objects or sound waves containing those phonemes. A team led by Stanford computer science professor Andrew Ng and Google Fellow Jeff Dean showed the system images from 10 million randomly selected YouTube videos.