Some of the most dazzling recent advances in artificial intelligence have come thanks to resources only available at big tech companies, where thousands of powerful computers and terabytes of data can be as copious as free granola bars and nap pods. A new project aims to show this needn't be the case, by cobbling together the code, data, and computer power needed to reproduce one of the most epic--and potentially useful--AI algorithms developed in recent years. Eleuther is an open source effort to match GPT-3, a powerful language algorithm released in 2020 by the company OpenAI that is sometimes capable of writing strikingly coherent articles in English when given a text prompt. Eleuther is still some way from matching the full capabilities of GPT-3, but last week the researchers released a new version of their model, called GPT-Neo, which is about as powerful as the least sophisticated version of GPT-3. Open sourcing big AI projects could make the technology more accessible and widespread at a time when it has become increasingly entrenched at big tech firms.

The 2019 NFL season quickly evolved into the Lamar Jackson show, every week delivering a different story, usually involving a highlight touchdown, a gaudy stat line, or a charming news conference. One story, however, was different: following a San Francisco 49ers loss at the hands of Jackson's Baltimore Ravens on Dec. 1, Tim Ryan, the radio color analyst for the 49ers, suggested that Jackson was successful in part because his dark skin helped him disguise a dark football. The public backlash was swift and loud, even if the fallout was mild (Ryan was suspended for one game). Instead of an honest conversation about why we talk about certain athletes using racialized language, the sports world settled for an apology and the next news story in the cycle. It is society's inability to adequately address issues of race and bias that motivated Mohit Iyyer, an assistant professor of computer science at the University of Massachusetts Amherst, to apply artificial intelligence and "big data" analytics toward answering a central question: Do sports commentators demonstrate bias in how they discuss athletes from different racial backgrounds?

Image recognition has progressed in leaps and bounds over the years. Not too long ago, a challenging recognition task involved asking an AI "Is there a human in this image?" More recently, however, the bar has been raised -- and a new research project carried out at the University of Maryland and University of Colorado has another recognition task in its sights: whether or not an AI can read comic books. In some ways, this is deeply ironic. For a long time, comics were dismissed as a junk medium for kids and barely-literate adults.

Image recognition has progressed in leaps and bound over the years. Not too long ago, a challenging recognition task involved asking an AI "Is there a human in this image?" More recently, however, the bar has been raised -- and a new research project carried out at the University of Maryland and University of Colorado has another recognition task in its sights: whether or not an AI can read comic books. In some ways, this is deeply ironic. For a long time, comics were dismissed as a junk medium for kids and barely-literate adults.