A Florida man is suing several law enforcement agencies for his arrest and prosecution for allegedly luring a child after he was wrongly identified using faulty AI facial recognition software. According to the Jacksonville Beach police department, an algorithm returned a 93% probability that Robert Dillon was the man caught on security cameras at a McDonald's in the town attempting to persuade an unaccompanied girl, aged younger than 12, to leave with him. Dillon, however, lives in Fort Myers, more than 300 miles and a five-hour drive away, and told detectives he had never been to Jacksonville Beach in his life. The case was dismissed and charges dropped last year over the August 2024 incident. Now the 52-year-old has filed a lawsuit against the police department, the Jacksonville sheriff's office, and Bob Gualtieri, the sheriff of Pinellas county, whose agency maintains and operates the Faces (Face Analysis Comparison and Examination) system and leases it to other law enforcement.

The ACLU is suing two Florida police departments over the arrest of a Fort Myers man in a child-abduction case, saying officers treated a flawed face recognition match as a near-certain ID. A Florida man was wrongfully arrested for attempting to illegally lure a child after police relied on a face recognition match that was inaccurate, according to a lawsuit filed on Wednesday, even though he lived more than 300 miles from the scene and says he had never set foot in the city where the crime took place. Robert Dillon, a 52-year-old commercial crabber from Fort Myers, was arrested after FACES--a face recognition system operated by Florida's Pinellas County Sheriff's Office--matched his face against a photo of a man on a computer screen taken with a cellphone. The system returned a "93 percent match on facial features," according to police investigatory notes. The scores it emits represent how much two images look alike to the algorithm.

At Springwood library in the Blue Mountains, a librarian appears with a cat carrier in each hand. About 30 children gather around in a semicircle. Inside each carrier, a pair of beaming, sci-fi-like eyes peer out at the expectant crowd. "That is the funniest thing ever," one child says. The preschoolers have just finished reading The Truck Cat by Deborah Frenkel and Danny Snell for the annual National Simultaneous Storytime.

Sam Altman, the CEO of artificial intelligence lab OpenAI, told a Senate panel he welcomes federal regulation on the technology "to mitigate" its risks. A software company is looking to use artificial intelligence (AI) to help companies mitigate and avoid human rights risks in their supply chain. "When it comes to transparency in supply chains, there is such an enormous amount of data that is being spread not just in spreadsheets but also through social that we can start to use to identify and zero in," Justin Dillon, CEO and Founder of FRDM, told Fox News Digital, adding that it's "early, early days" for the technology and methods his company uses. Any AI technology requires significant amounts of data to analyze and process, and Dillon pointed to a treasure trove of data available on social media that his company can use to help map out problematic hotspots in supply chains -- areas that companies can then work to avoid and help create more ethical routes. Dillon related a story from a father in Australia who was talking about using "social listening," which is the analysis of conversations and trends related to different brands.

Infants outperform artificial intelligence in detecting what motivates other people's actions, finds a new study by a team of psychology and data science researchers. Its results, which highlight fundamental differences between cognition and computation, point to shortcomings in today's technologies and where improvements are needed for AI to more fully replicate human behavior. "Adults and even infants can easily make reliable inferences about what drives other people's actions," explains Moira Dillon, an assistant professor in New York University's Department of Psychology and the senior author of the paper, which appears in the journal Cognition. "Current AI finds these inferences challenging to make." "The novel idea of putting infants and AI head-to-head on the same tasks is allowing researchers to better describe infants' intuitive knowledge about other people and suggest ways of integrating that knowledge into AI," she adds. "If AI aims to build flexible, commonsense thinkers like human adults become, then machines should draw upon the same core abilities infants possess in detecting goals and preferences," says Brenden Lake, an assistant professor in NYU's Center for Data Science and Department of Psychology and one of the paper's authors.

Infants outperform artificial intelligence in detecting what motivates other people's actions, finds a new study by a team of psychology and data science researchers. Its results, which highlight fundamental differences between cognition and computation, point to shortcomings in today's technologies and where improvements are needed for AI to more fully replicate human behavior. "Adults and even infants can easily make reliable inferences about what drives other people's actions," explains Moira Dillon, an assistant professor in New York University's Department of Psychology and the senior author of the paper, which appears in the journal Cognition. "Current AI finds these inferences challenging to make." "The novel idea of putting infants and AI head-to-head on the same tasks is allowing researchers to better describe infants' intuitive knowledge about other people and suggest ways of integrating that knowledge into AI," she adds. "If AI aims to build flexible, commonsense thinkers like human adults become, then machines should draw upon the same core abilities infants possess in detecting goals and preferences," says Brenden Lake, an assistant professor in NYU's Center for Data Science and Department of Psychology and one of the paper's authors.

After opening at $40.25, the stock stumbled, slipping about 20%. But it regained much of its loss to close at $40. "I guess it was a rough awakening to life as a public company for a few hours, but we are optimistic," Chief Financial Officer Pat Dillon said. Top news and in-depth analysis on the world of logistics, from supply chain to transport and technology. Chief Executive Cheng Lu said the company is planning to conduct a "driver-out" pilot program without anyone at the wheel in the fourth quarter on a roughly 100-mile run between Tucson and Phoenix. The company has a fleet of 50 trucks it is testing in the U.S. Southwest and approximately 20 more in China, running with two people in the cab.

In discriminative settings such as regression and classification there are two random variables at play, the inputs X and the targets Y. Here, we demonstrate that the Variational Information Bottleneck can be viewed as a compromise between fully empirical and fully Bayesian objectives, attempting to minimize the risks due to finite sampling of Y only. We argue that this approach provides some of the benefits of Bayes while requiring only some of the work.

Key point: There is an ongoing co-evolution between indispensable human cognition and decision-making and AI-enabled autonomy. As the armed soldier's clear rooms and transition from house to house in a firefight, how quickly would they need to know that groups of enemies awaited them around the next corner? Getting this information to soldiers in seconds can not only decide victory or defeat in a given battle but save lives. What if AI-enabled computer programs were able to instantly discern specifics regarding the threat such as location, weapons and affiliation by performing real-time analytics on drone feeds and other fast-moving sources of information, instantly sending crucial data to soldiers in combat? While current technology can today perform some of these functions, what if this data was provided to individual dismounted soldiers in a matter of seconds?

Infantry Soldiers with 1st Battalion, 8th Infantry Regiment, 3rd Armored Brigade Combat Team, 4th Infantry Division, fire an FGM-148 Javelin during a combined arms live fire exercise in Jordan on August 27, 2019, in support of Eager Lion - file photo. Envision a scenario wherein dismounted infantry soldiers are taking heavy enemy fire while clearing buildings amid intense urban combat -- when an overhead drone detects small groups of enemy fighters hidden nearby, between walls, preparing to ambush. As the armed soldiers clear rooms and transition from house to house in a firefight, how quickly would they need to know that groups of enemies awaited them around the next corner? Getting this information to soldiers in seconds can not only decide victory or defeat in a given battle but save lives. What if AI-enabled computer programs were able to instantly discern specifics regarding the threat such as location, weapons and affiliation by performing real-time analytics on drone feeds and other fast-moving sources of information, instantly sending crucial data to soldiers in combat?