John Oates, of Hall & Oates, is wary of the future represented by artificial intelligence in the music industry. "Look at what's coming in with AI, the possibility that AI is going to be replacing songwriters and artists for that matter," Oates told Fox News Digital. "The idea that there could be a new… David Bowie album. AI could take David Bowie's voice and extrapolate and sample his music for his entire career and write new David Bowie songs, and the record company could put it out." He added, "A younger generation might not even know. They might not even know he's dead for that matter. So there's a lot going on and you have to pay attention."

Match Group, which operates one of the world's largest portfolios of dating apps, will soon add a new profile verification feature to its popular dating app Hinge. The feature is part of a larger effort to crack down on scammers who use fake photos and purport to be people they're not on the app, often with the intent of eventually scheming romantic conquests out of money. Jarryd Boyd, director of brand communications for Hinge, said in a written statement that Hinge will begin rolling out the feature, named Selfie Verification, next month. Hinge will ask users to take a video selfie within the app in order to confirm they're a real person and not a digital fake. Match Group then plans to use a combination of machine learning technology and human moderators to "compare facial geometries from the video selfie to photos on the user's profile," Boyd said.

Newswise -- A Florida State University professor's research could help quantum computing fulfill its promise as a powerful computational tool. William Oates, the Cummins Inc. Professor in Mechanical Engineering and chair of the Department of Mechanical Engineering at the FAMU-FSU College of Engineering, and postdoctoral researcher Guanglei Xu found a way to automatically infer parameters used in an important quantum Boltzmann machine algorithm for machine learning applications. Their findings were published in Scientific Reports. The work could help build artificial neural networks that could be used for training computers to solve complicated, interconnected problems like image recognition, drug discovery and the creation of new materials. "There's a belief that quantum computing, as it comes online and grows in computational power, can provide you with some new tools, but figuring out how to program it and how to apply it in certain applications is a big question," Oates said.

A Florida State University professor's research could help quantum computing fulfill its promise as a powerful computational tool. William Oates, the Cummins Inc. Professor in Mechanical Engineering and chair of the Department of Mechanical Engineering at the FAMU-FSU College of Engineering, and postdoctoral researcher Guanglei Xu found a way to automatically infer parameters used in an important quantum Boltzmann machine algorithm for machine learning applications. Their findings were published in Scientific Reports. The work could help build artificial neural networks that could be used for training computers to solve complicated, interconnected problems like image recognition, drug discovery and the creation of new materials. "There's a belief that quantum computing, as it comes online and grows in computational power, can provide you with some new tools, but figuring out how to program it and how to apply it in certain applications is a big question," Oates said.

William Oates, the Cummins Inc. Professor in Mechanical Engineering and chair of the Department of Mechanical Engineering at the FAMU-FSU College of Engineering, and postdoctoral researcher Guanglei Xu found a way to automatically infer parameters used in an important quantum Boltzmann machine algorithm for machine learning applications. Their findings were published in Scientific Reports. The work could help build artificial neural networks that could be used for training computers to solve complicated, interconnected problems like image recognition, drug discovery and the creation of new materials. "There's a belief that quantum computing, as it comes online and grows in computational power, can provide you with some new tools, but figuring out how to program it and how to apply it in certain applications is a big question," Oates said. Quantum bits, unlike binary bits in a standard computer, can exist in more than one state at a time, a concept known as superposition.

A Florida State University professor's research could help quantum computing fulfill its promise as a powerful computational tool. William Oates, the Cummins Inc. Professor in Mechanical Engineering and chair of the Department of Mechanical Engineering at the FAMU-FSU College of Engineering, and postdoctoral researcher Guanglei Xu found a way to automatically infer parameters used in an important quantum Boltzmann machine algorithm for machine learning applications. Their findings were published in Scientific Reports. The work could help build artificial neural networks that could be used for training computers to solve complicated, interconnected problems like image recognition, drug discovery and the creation of new materials. "There's a belief that quantum computing, as it comes online and grows in computational power, can provide you with some new tools, but figuring out how to program it and how to apply it in certain applications is a big question," Oates said.

I developed what I thought was an extremely clever method for detecting "bad" training instances. Each instance was scored, and those with the lowest scores could be removed before running C4.5 to build a decision tree with the remainder. I ran an experiment in which I removed the bottom 10 percent of the instances in a University of California, Irvine (UCI) data set. The resulting tree was smaller and more accurate (as measured by 10-fold CV) than the tree built on the full data set. Then I removed the bottom 20 percent of the instances and got a tree that was smaller than the last one and just as accurate.

Amazon isn't synonymous with high fashion yet, but the company may be poised to lead the way when it comes to replacing stylists and designers with ever-so-chic AI algorithms. Researchers at the e-commerce juggernaut are currently working on several machine-learning systems that could help provide an edge when it comes to spotting, reacting to, and perhaps even shaping the latest fashion trends. The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail--making recommendations based on products popping up in social-media posts, for instance. And it could help the company expand its clothing business or even dominate the area. "There's been a whole move from companies like Amazon trying to understand how fashion develops in the world," says Kavita Bala, a professor at Cornell University who took part in a workshop on machine learning and fashion organized by Amazon last week.

The days of journeying to a travel agency and sitting at the desk of an agent just to book a trip have all but disappeared, but technology is increasingly putting that level of personalized service in the palms of our hands. A number of travel apps have begun to incorporate artificial intelligence, technology that allows machines to analyze large amounts of data and "learn" on their own, according to Greg Oates, senior editor at travel site Skift. The technology can help machines outperform traditional search engines and real humans (including travel agents) in speed and efficiency, he said. "The holy grail for travel brands today is personalizing the guest experience," he said. "Artificial intelligence really helps with that because it can crunch so much data and connect dots around users so much faster than traditional search."

The days of journeying to a travel agency and sitting at the desk of an agent just to book a trip have all but disappeared, but technology is increasingly putting that level of personalized service in the palms of our hands. A number of travel apps have begun to incorporate artificial intelligence, technology that allows machines to analyze large amounts of data and "learn" on their own, according to Greg Oates, senior editor at travel site Skift. The technology can help machines outperform traditional search engines and real humans (including travel agents) in speed and efficiency, he said. "The holy grail for travel brands today is personalizing the guest experience," he said. "Artificial intelligence really helps with that because it can crunch so much data and connect dots around users so much faster than traditional search." These apps work by taking a wealth of data on travelers into account and anticipating their choices.