Facial-recognition software developed by Amazon and marketed to local and federal law enforcement as a powerful crime-fighting tool struggles to pass basic tests of accuracy, such as correctly identifying a person's gender, new research released Thursday says. Researchers with M.I.T. Media Lab also said Amazon's Rekognition system performed more accurately when assessing lighter-skinned faces, raising concerns about how biased results could tarnish the artificial-intelligence technology's use by police and in public venues, including airports and schools. Amazon's system performed flawlessly in predicting the gender of lighter-skinned men, the researchers said, but misidentified the gender of darker-skinned women in roughly 30 percent of their tests. Rival facial-recognition systems from Microsoft and other companies performed better but were also error-prone, they said. The problem, AI researchers and engineers say, is that the vast sets of images the systems have been trained on skew heavily toward white men.

As concerns over security risks for connected vehicles continue to build, automotive cybersecurity company SafeRide Technologies believes unsupervised machine learning will help keep threat actors out of the driver's seats. Earlier this month, SafeRide launched its vXRay technology for connected vehicles' security operations center (SOC), which uses unsupervised machine learning technology to provide behavioral profiling and anomaly detection to improve connected vehicle security. Gil Reiter, vice president of product management and marketing at SafeRide, based in Tel Aviv, Israel, said vXRay is available for OEMs and fleet managers to integrate in their vehicles' SOC. "The vXRay technology establishes the normal behavior of the vehicle without any dependencies or without any knowledge of the specific electronic control unit properties," Reiter said. "Once the behavioral baseline of the vehicle is established, the technology can accurately detect and then flag any abnormal behavior of the vehicle system and report the abnormal behavior to the connected vehicle's SOC for further analysis."

A Florida state appellate court ruled last week that Willie Allen Lynch, who was convicted in 2016 for selling crack cocaine, had no right to view photos of other suspects identified by the facial recognition search that led to his arrest. In 2015, undercover agents working with the Jacksonville Sheriff's Office photographed a man selling $50 of cocaine. Detectives were unable to identify him, so they decided to turn to the Face Analysis Comparison Examination System, known as FACES, which draws from a database consisting of more than 33 million driver's license and law enforcement photos. The software, which is designed to return multiple potential matches for a given image, named Lynch and four other suspects. Upon further investigation, detectives arrested Lynch for the crime.

Has machine learning and artificial intelligence (AI) reached to the point where they can power businesses to make better decisions and improve operational efficiencies? This is one of the common questions concerning business owners today. Machine learning systems are developing at a faster pace as they can now be trained using vast pools of available data. One powerful example is Google's demonstration of its AI-powered Google Assistant making restaurant reservations through a phone call. Marketers in the US have already started incorporating AI and seeing benefits such as improved sales and ROI (return on investment) in advertising and innovation, according to a survey conducted by LoopMe.

On a clinical level, American healthcare is arguably the most advanced in the world, powered by science and technology and guided by skilled and dedicated medical professionals. But the healthcare system itself is inefficient, byzantine, and often wildly expensive, due in large part to systemic inefficiencies that result in vast waste. "The National Academy of Sciences/Institute of Medicine (IOM) issued a report a few years ago that said about 30% of our healthcare spending does nothing directly to help patients--and, in fact, can often harm them, which isn't sustainable," says Gregory Moore, Vice President of Healthcare for Google Cloud. "If we took half of that out of the system, we'd solve much of the healthcare spend issue for the U.S." Artificial intelligence (AI) is being hyped as the next big thing for, well, nearly everything, and healthcare is no exception. It has already shown promise as a clinical tool, and experts believe it is one of the keys to containing runaway costs in the healthcare system.

To some, artificial intelligence and machine learning may still seem like far-off concepts. But the reality is tht enterprises are already adopting these technologies, and many experts believe that 2019 may be the year AI and ML move to the mainstream. Not surprisingly, security looks to be one of the leading drivers of that adoption. In a recent survey of enterprise IT by 451 Research, "Voice of the Enterprise: AI & Machine Learning--Adoption, Drivers and Stakeholders," almost 50% of respondents indicated that they have deployed or plan to deploy machine learning in their organizations in the next 12 months. The same study revealed that security is the second most important reason for applying machine learning in enterprises.

The AI poker-playing bot, which initially became popular after it hit a hot streak against top poker players in tournaments in 2017, has been reportedly "hired" by a Pentagon agency -- the Defense Innovation Unit, according to the report. After the computational game theory-based Libratus won more than $1.8 million in a poker championship, defeating four poker professionals, Tuomas Sandholm -- the head of the project under which the AI mechanism was created -- reportedly founded a startup called Strategy Robot, which is primarily aimed at adapting the poker AI for military use in such areas as simulations and planning. In June 2018 the US Department of Defense created the Joint Artificial Intelligence Centre (JAIC), designed to accelerate "the delivery of AI-enabled capabilities, scaling the Department-wide impact of AI and synchronizing DoD AI activities to expand Joint Force advantages." The centre will oversee around 600 AI projects and has a five-year, $1.7 billion budget, according to reports.

George Soros, billionaire and founder of Soros Fund Management LLC, speaks at an event on day three of the World Economic Forum (WEF) in Davos, Switzerland, on Thursday, Jan. 24, 2019. George Soros, the billionaire philanthropist, issued a warning on Thursday that advancements in the field of artificial intelligence pose a "mortal threat" to humanity when they're made by repressive regimes, The Financial Times reports. The 88-year-old, who made his money in the hedge fund industry, reportedly made his comments during a "provocative" speech at the World Economic Forum in Davos. While China is not the only authoritarian state in the world, it is the most powerful and technologically advanced, Soros said. "This makes [Chinese president] Xi Jinping the most dangerous opponent of open societies," he said.

The Hungarian-born Holocaust survivor also reportedly criticized China's deep investments in technology development, specifically artificial intelligence and machine learning. Soros said such technologies pose a "mortal danger" when used by authoritarian states, and he encouraged other elements of Chinese society to offset Chinese President Xi Jinping.

Artificial intelligence (AI) was by far the hottest trend discussed in sessions and across the expo floor at the world's largest radiology conference, the 2018 Radiological Society Of North America (RSNA). At the meeting in late November, there was an explosion of AI and deep learning algorithms across the expo floor. How machine learning will impact medical imaging was the key takeaway from the opening session, where examples of how AI will alter medical imaging in the near future were highlighted. Here is an overview of the types of AI software being developed and a few examples from RSNA that are specific to cardiovascular imaging. Artificial intelligence has been a growing topic in past years at RSNA, but this year several companies showed products that recently gained U.S. Food and Drug Administration (FDA) market clearance.