"The whole vision behind Track in the first place," says Veritone CEO Ryan Steelberg, was "if we're not allowed to track people's faces, how do we assist in trying to potentially identify criminals or malicious behavior or activity?" In addition to tracking individuals where facial recognition isn't legally allowed, Steelberg says, it allows for tracking when faces are obscured or not visible. The product has drawn criticism from the American Civil Liberties Union, which--after learning of the tool through MIT Technology Review--said it was the first instance they'd seen of a nonbiometric tracking system used at scale in the US. They warned that it raises many of the same privacy concerns as facial recognition but also introduces new ones at a time when the Trump administration is pushing federal agencies to ramp up monitoring of protesters, immigrants, and students. Veritone gave us a demonstration of Track in which it analyzed people in footage from different environments, ranging from the January 6 riots to subway stations.

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.

Media mogul Barry Diller urged all parties to reach a resolution by September 1 amid ongoing Hollywood strikes during a Sunday interview on'Face the Nation.' Since the publication of the Bible, good vs. evil has long been a universal theme in literature - and in Hollywood storytelling. But could the same perceived evil force, also be good? As of May 2 of this year, 11,500 Hollywood screenwriters, represented by the Writers Guild of America (WGA) have been on strike over a three-pronged fight that boils down to money, autonomy, and Artificial Intelligence (AI). The writers are asking for increased and commensurate pay, for a guaranteed number of writers per room, and for regulated use of artificial intelligence in the writing process.

Recording advertisements and product endorsements can be lucrative work for celebrities and influencers. But is it too much like hard work? That's what US firm Veritone is betting. Today, the company is launching a new platform called Marvel.AI that will let creators, media figures, and others generate deepfake clones of their voice to license as they wish. "People want to do these deals but they don't have enough time to go into a studio and produce the content," Veritone president Ryan Steelberg tells The Verge.

Ten years ago, I was engaged in the writing of an energy power grid report that was part of a national initiative to assess the health of our electrical energy grid and its resilience. Assets like wind farms and contemporary fossil and nuclear fuel systems were in place for energy distribution, but to my surprise there was also equipment in the grid that dated back to the 1890s and was still in production. I began to understand the challenges of using renewable energy such as wind and solar when it came to assessing energy supply and demand and ensuring there is enough on-hand energy to power the homes and businesses that are relying on it. When utilities were using gas, coal, or nuclear energy to power the grid, the in-flow of that fuel from its source was consistent, so it was easy to assess supply and demand on any given day and to deliver the energy needed to power homes and businesses. What if the wind gusted to 40 mph one day, and was perfectly still on the next day?

Deep learning, the branch of AI that uses artificial neural networks to build prediction and pattern matching models from large datasets relevant to a particular application, is having a sizable impact on both consumer and enterprise software. Whether for enabling home appliances to understand and respond to vocal commands or identifying hidden patterns endemic to all malware, deep learning algorithms allow machines to mimic and even improve upon human cognition in ways that are impossible with imperative or declarative programming. Unfortunately, developing deep learning software isn't easy since the models are customized for a particular use. Indeed, developing models is more like making a custom-fitted suit, not off-the-rack clothing in standard sizes. Deep learning encompasses a large category of software, not a general-purpose solution, and describes a broad range of algorithms and network types, each better suited to particular types of problems and data than others.

It's becoming ubiquitous and invisible,

This article is part of CMO.com's December series about 2018 trends, predictions, and new opportunities. We're all aware of the immense impact that technology has had on the ways in which consumers interact with brands. Think about some early inventions--such as the telephone in 1876 and the television in 1926--which gave businesses an easy way to connect with consumers on a more intimate level, from the comfort of their homes. Fast-forward to the late 1970s, when people could purchase personal computers, and next, 1991, when the Internet (World Wide Web) became available to the world. Both brought with them dramatic changes.

With thousands of artificial intelligence solutions available to apply to a myriad of business functions, the issue for enterprises seeking to innovate with AI is not that the market doesn't have options -- it's that there are too many, creating confusion on which technologies provide actual business value, and which are better in theory than in practice. Today, the AI tech ecosystem is awash with narrow AI solutions, cognitive engines built to perform one task well - engines in categories like natural language processing, image recognition and facial detection. According to Gartner, "Small, unconstrained market players specializing in AI core technologies will cause sporadic business disruption in the near future. In the long term, the main disruptive effect of AI core technologies will arise through revolutionary business ideas enabled by those technologies." Veritone, a recent IPO out of Costa Mesa, Calif., caught my attention as one such business, grasping the idea that many AI brains are better than one when used together to solve business problems.