To support MIT Technology Review's journalism, please consider becoming a subscriber. But critics are starting to push back, arguing that the approach is wrong-headed. Asking computers a question and getting an answer in natural language can hide complexity behind a veneer of authority that is not deserved. "We got too bogged down by what we could do; we haven't looked at what we should do," says Chirag Shah at the University of Washington, who works on search technologies. On March 14, Shah and his University of Washington colleague Emily M. Bender, who studies computational linguistics and ethical issues in natural-language processing, published a paper that criticizes what they see as a rush to embrace language models for tasks they are not designed to address.

If you go to this page on my blog, you'll find an introductory post about the use of artificial intelligence in generating written content, marketing messages, and personalized email. It won't win any Pulitzers, but it's easy enough to understand. What's remarkable about this post is that I didn't write it. Instead, it was created by LongShot, an AI-powered software-as-a-service application that generates written content – including long-form articles – entirely from a few keywords. Similar products include Jasper, Kafkai, Copysmith, Writesonic, and ContentBot.

The project, funded by Fonds de recherche du Québec – Nature et technologies (FRQNT), in partnership with the Ministère de l'Énergie et Ressources naturelles (MERN), under its second sustainable mining partnership research program, will analyze all airborne image data to predict the nature of the subsoil. The algorithms generated can be used as guides to target mineral exploration sites. This new approach to artificial intelligence aims to train parallel neural networks, each processing one type of variable, such as aeromagnetic or electromagnetic data. When processing is complete, a system will combine each network's predictions to generate a final prediction. Professor Gloaguen's team is working with experts in artificial intelligence applied to mining for this network development project, the first phase of which is already underway.

In her 2018 book "Double Negative: The Black Image and Popular Culture," Racquel Gates explores the disruptive potential of stereotypical or so-called negative images of Black people onscreen: Flavor Flav on VH1's "Flavor of Love," for example, and the stars of "ratchet" reality shows such as "Basketball Wives." These images, Gates argues, intervene against narratives of racial uplift that are overly tethered to white and middle-class definitions of respectability. In her acknowledgments section, Gates, a professor of film and media studies at Columbia, invokes a scene from "Love & Hip Hop," in which an aspiring singer tells an entertainment manager, "I want to be on your roster." Gates writes, "While I was tempted to quote this bit of dialogue to my editor, Ken Wissoker, during our first meeting, I erred on the side of caution." Wissoker, who has been an editor at Duke University Press since 1991, has a formidable roster, and one could easily imagine a reality show about junior scholars fighting for a chance to work with him.

Register today in order to add your own comment, it barely takes a minute.

Such methods as machine learning and deep learning have become popular design tools for development of photonic devices.

Emerging techniques in artificial intelligence (AI) and deep learning–based analysis could provide new avenues for speeding drug discovery, they …

Every year for the last six years, Unanimous AI has been more accurate than movie critics at predicting Oscar winners. It uses swarm intelligence the power of interactive group decisions enhanced by AI – to transform regular people into expert decision-makers. How did it do this year? Unanimous AI took a group of regular movie fans and created a'hive mind' in which their combined choices are smarter than those of any individual member. "We can take a group of people and turn them into a super organism," founder Louis Rosenberg told IoT World Today's sister publication AI Business.

In machine learning, the three methodologies are reinforcement learning (RL), supervised learning, and unsupervised learning.

Tristan Glatard, co-director of Applied AI Institute at Concordia University (left) and Fenwick McKelvey, co-director of Applied AI Institute at Concordia University (right).Supplied It wasn't long ago when, for the average person, the words "artificial intelligence" conjured up visions of futuristic fiction – the androids of Blade Runner, say, or Steven Spielberg's A.I. In fact, artificial intelligence is very much here-and-now, and not only with a human façade (think of virtual assistants Siri and Alexa, those smartphone genies ready to do our bidding), but in a myriad of forms. From Google searches to medical diagnoses, Netflix's personalized "Top Picks" to self-driving cars, AI technology has become pervasive. It's predicted that the AI market will contribute as much as $15.7-trillion per year to the global economy by the end of this decade. Its applications will be increasingly integrated into our lives and play a role in shaping our society.