Drone detection technology has led to an increase in airborne smuggling operations over U.S. prisons, putting authorities in a tough spot as federal regulations prevent drones from being brought down.

Alex Shieh is a student at Brown University. He is making waves and facing charges for asking the school's non-faculty employees what they do all day. A sophomore at Brown University is facing the school's wrath after he sent a DOGE-like email to non-faculty employees asking them what they do all day to try to figure out why the elite school's tuition has gotten so expensive. "The inspiration for this is the rising cost of tuition," Alex Shieh told Fox News Digital in an interview. "Next year, it's set to be 93,064 to go to Brown," Shieh said of the Ivy League university.

As part of a collaboration between Better Images of AI and Cambridge University's Diversity Fund, Hanna Barakat was commissioned to create a digital collage series to depict diverse images about the learning and education of AI at Cambridge. Hanna's series of images complement our competition that we opened up to the public at the end of last year which invited submissions for better images of AI from the wider community – you can see the winning entries here. Hanna provides her thoughts on the challenges of creating images that communicate about AI histories and the inherent contradictions that arise when engaging in this work. As outlined by the Better Images of AI project, normative depictions of AI continue to perpetuate negative gender and racial stereotypes about the creators, users, and beneficiaries of AI. The lack of diversity--and the problematic interpretation of diversity--in AI-generated images is not merely an'output' issue that can be easily fixed.

We describe the steps of developing the MDMT (Multi-Dimensional Measure of Trust), an intuitive self-report measure of perceived trustworthiness of various agents (human, robot, animal). We summarize the evidence that led to the original four-dimensional form (v1) and to the most recent five-dimensional form (v2). We examine the measure's strengths and limitations and point to further necessary validations.

Did you ever try to measure a smell?

U.S. senators and technology experts met for the second of Senate Majority Leader Chuck Schumer's AI Insight Forums Oct. 24. Among the 21 invitees were venture capitalists, academics, civil rights campaigners, and industry figures. The discussion at the second Insight Forum, which was closed to the public, focused on how AI could enable innovation, and the innovation required to ensure that AI progress is safe, according to a press release from Schumer's office. In the previous forum, attended by the CEOs of most of the large tech companies, Schumer asked who agreed that some sort of legislation would be required. This time, he asked for a show of hands to see who agreed whether significant federal funding would be required to support AI innovation. Again, all hands were raised, according to Suresh Venkatasubramanian, a professor of data science and computer science at Brown University, who attended the forum.

When the Titan submersible, carrying five sightseers to the wreck of the Titanic, blew up thousands of metres under the ocean surface in June, it underscored why humanity knows more about the surface of some other planets than about the depths of the Earth's oceans. Oceans cover more than 70 percent of the earth's surface. Yet, this underwater world is a challenging place to explore, as the Titan disaster showed. The deepest point under water, the Challenger Deep in the Pacific Ocean, is 11,000 metres deep, more than the height of Mount Everest. The light doesn't penetrate to such depths.

A team of researchers has developed a new framework which utilizes advanced machine learning and statistical algorithms to predict rare events without the need for large data sets. Scientists can use a combination of advanced machine learning and sequential sampling techniques to predict extreme events without the need for large data sets, according to researchers from Brown and MIT. When it comes to predicting disasters brought on by extreme events (think earthquakes, pandemics, or "rogue waves" that could destroy coastal structures), computational modeling faces an almost insurmountable challenge: Statistically speaking, these events are so rare that there's just not enough data on them to use predictive models to accurately forecast when they'll happen next. However, a group of scientists from Brown University and Massachusetts Institute of Technology suggests that it doesn't have to be that way. In a study published in Nature Computational Science, the researchers explain how they utilized statistical algorithms which require less data for accurate predictions, in combination with a powerful machine learning technique developed at Brown University.

The race to remove CO2 from our atmosphere is on. In an effort to draw down carbon at a meaningful scale, people are looking to the ground. The top meter of the world's soil holds over three times the amount of carbon currently in our atmosphere--and if we treat our land better, it could suck up even more. This is good news for farmers. Companies and individuals desperate to offset their emissions by purchasing carbon credits are willing to pay farmers to use sustainable agricultural practices and sequester carbon in their fields.

Kusanagi Motoko, Johnny Mnemonic, Takeshi Kovacs, John Perry, Lenny Nero -- the practice of melding biological minds with electronics hardware is a cornerstone technology of modern cyberpunk literature. And, if certain medical device startup companies are to be believed, accomplishing similar cybernetic feats -- from downloadable memories to "Whoa, I Know Kung Fu"-style instantaneous learning -- could become reality sooner than we think. BCIs are, essentially, devices that read the electrochemical firing of the brain's myriad synapses, interprets and translates that signal into a digital format that can be understood by computers. Research on the technology began in the 1970s at the Brain Research Institute of University of California at Los Angeles under the watch of pioneering neurologist, Dr. Jacques J. Vidal. It took researchers more than two decades to sufficiently lay the basic technological groundwork needed to progress from animal models but by the mid-1990s the very first BCI prototypes were being installed in human craniums.