A Nashville woman was heard screaming for help by witnesses before she was found dead – police were able to track her alleged killer down using dashcam footage from a helpful civilian and a detective who had worked a case involving his twin. Last week, the Metro Nashville Police Department announced the arrest of 29-year-old Paul Park in connection with the death of 34-year-old Alyssa Lokits. The woman was exercising on the Mill Creek Greenway trail in Nashville on Monday, Oct. 14. Security cameras show Park allegedly emerging from between two parked vehicles and "following her at a brisk pace," the department wrote in a press release. After the two left the view of the camera, witnesses heard a woman scream "Help! Then, police said, the witnesses heard gunfire. Paul Park, 39, was arrested by the Metro Nashville Police Department on Oct. 15 in the death of Alyssa Lokits. Park was seen a short while later with scratches on his arms and blood on his clothing as he returned to his gray BMW sedan. Detectives didn't get a break in the case until a local resident provided them with dashcam footage, which showed part of Park's license plate and a clearer image of his face. A homicide detective who reviewed the footage recognized Park as the identical twin brother from a suicide case that she had worked in December 2021, CBS News reported. "I pray that we don't have an incident where we don't have a dashcam, or we don't have someone helping us like we had in this case," MNPD Chief John Drake said at a press conference. "I'm so thankful that our people got on this – we need technology." Even without the helpful civilian's footage, new technology pioneered by artificial intelligence software can help police investigate cases like the Nashville killing. Veritone is one of the companies spearheading that movement. The license plate of Paul Park's gray BMW sedan wasn't captured on surveillance footage – but thanks to a partial license plate number captured by a hiker's dashcam, police were able to arrest the accused killer. Veritone Track, one of several functions in a suite of services for law enforcement, uses artificial intelligence to run one photo or video of a vehicle – like the video captured on the park's surveillance footage – against stoplight cameras, body-worn cameras and other municipal surveillance footage available to police to find a match. "Both federal and local law enforcement have a major data problem," Veritone CEO Ryan Steelberg told Fox News Digital. "They are now capturing body camera [footage] and dashcams.

Several police departments nationwide are debuting artificial intelligence that writes officers' incident reports for them, and although the software could cause issues in court, an expert says, the technology could be a boon for law enforcement. Oklahoma City's police department was among the first to experiment with Draft One, an AI-powered software that analyzes police body-worn camera audio and radio transmissions to write police reports that can later be used to justify criminal charges and as evidence in court. Since The Associated Press detailed the software and its use by the department in a late August article, the department told Fox News Digital that it has put the program on hold. "The use of the AI report writing has been put on hold, so we will pass on speaking about it at this time," Capt. Valerie Littlejohn wrote via email.

Quick, efficient and with a bird's eye view of any scene, more police departments are embracing the use of drones to carry out law enforcement work, with some blue cities now even using them to respond to 911 calls. Around 1,500 police departments across the country are currently using drones in some form, according to a report by the Electronic Frontier Foundation, a digital privacy group, with agencies deploying the technology for crowd control purposes, missing people searches, tracking fleeing suspects or mapping crime scenes. Steep budget cuts and dwindling staff numbers in blue cities, in particular, make drones both an effective and cost-saving tool for police in Democratic strongholds. A law enforcement official sets up a drone during a manhunt for suspect Robert Card following a mass shooting on Oct. 27, 2023, in Monmouth, Maine. Today's police drones are much bigger than regular drones commonly used for recreational purposes, with much longer battery lives and features such as thermal sensors, loudspeakers, spotlights or beacons.

Meta-solver approaches exploit many individual solvers to potentially build a better solver. To assess the performance of meta-solvers, one can adopt the metrics typically used for individual solvers (e.g., runtime or solution quality) or employ more specific evaluation metrics (e.g., by measuring how close the meta-solver gets to its virtual best performance). In this paper, based on some recently published works, we provide an overview of different performance metrics for evaluating (meta-)solvers by exposing their strengths and weaknesses.

SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY is based on the k-nearest neighbors algorithm and enables one to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as, a prototypical algorithm selector based on SUNNY for ASlib scenarios. A major improvement of sunny-as, called sunny-as2, was then submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work we present the technical advancements of sunny-as2, by detailing through several empirical evaluations and by providing new insights. Its current version, built on the top of the preliminary version submitted to OASC, is able to outperform sunny-as and other state-of-the-art AS methods, including those who did not attend the challenge.

SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems, and its parallel version won many gold medals in the Open category of the MiniZinc Challenge -- the yearly international competition for CP solvers. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as2, an algorithm selector based on SUNNY for ASlib scenarios. A preliminary version of sunny-as2 was submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work, we present the technical advancements of sunny-as2, including: (i) wrapper-based feature selection; (ii) a training approach combining feature selection and neighbourhood size configuration; (iii) the application of nested cross-validation. We show how sunny-as2 performance varies depending on the considered AS scenarios, and we discuss its strengths and weaknesses. Finally, we also show how sunny-as2 improves on its preliminary version submitted to OASC.

John C. Mauro is Professor of Materials Science and Engineering at the Pennsylvania State University. John earned a B.S. in Glass Engineering Science (2001), B.A. in Computer Science (2001), and Ph.D. in Glass Science (2006), all from Alfred University. He joined Corning Incorporated in 1999 and served in multiple roles there, including Senior Research Manager of the Glass Research department. Mauro is the inventor or co-inventor of several new glass compositions for Corning, including Corning Gorilla Glass products. Mauro joined the faculty at Penn State in 2017 and is currently a world-recognized expert in fundamental and applied glass science, statistical mechanics, computational and condensed matter physics, thermodynamics, and the topology of disordered networks.

Every sibling relationship has its clichés. In the Microsoft family of social-learning chatbots, the contrasts between Tay, the infamous, sex-crazed neo-Nazi, and her younger sister Zo, your teenage BFF with #friendgoals, are downright Shakespearean. When Microsoft released Tay on Twitter in 2016, an organized trolling effort took advantage of her social-learning abilities and immediately flooded the bot with alt-right slurs and slogans. Tay copied their messages and spewed them back out, forcing Microsoft to take her offline after only 16 hours and apologize. A few months after Tay's disastrous debut, Microsoft quietly released Zo, a second English-language chatbot available on Messenger, Kik, Skype, Twitter, and Groupme.

AI will create more jobs than it destroys was the not-so-subtle rebuttal from tech giants to growing concern over the impact of automation technologies on employment. Execs from Google, IBM and Salesforce were questioned about the wider societal implications of their technologies during a panel session here at Mobile World Congress. Behshad Behzadi, who leads the engineering teams working on Google's eponymously named AI voice assistant, claimed many jobs will be "complemented" by AI, with AI technologies making it "easier" for humans to carry out tasks. "For sure there is some shift in the jobs. There's lots of jobs which will [be created which don't exist today]. Think about flight attendant jobs before there was planes and commercial flights. No one could really predict that this job will appear. So there are jobs which will be appearing of that type that are related to the AI," he said.

Maluuba is an increasingly rare beast in the burgeoning world of artificial intelligence. Founded in 2012, the Waterloo-headquartered startup has yet to be acquired by one of the many massive tech companies investing heavily in AI. Nor has Maluuba been raided for its talent, which is very much in demand. Rather, the small artificial intelligence startup is now home to more than 50 employees and growing -- and for prospective hires weighing opportunities with larger firms, co-founder Mohamed Musbah thinks he has a pretty good pitch. Maluuba is trying to train computers to understand language, whether in conversation with a human, or when reading a document or other source of text.