"... the research area that studies the operation and design of systems that recognize patterns in data." It includes statistical methods like discriminant analysis, feature extraction, error estimation, cluster analysis.
– Pattern Recognition Laboratory at Delft University of Technology
On Tuesday, a group of 90 advocacy groups penned a letter to Amazon, Google, and Microsoft, requesting that the companies pledge not to sell facial recognition technology to the government. The American Civil Liberties Union (ACLU), the Refugee and Immigrant Center for Education and Legal Services (RAICES), and the Electronic Frontier Foundation (EFF) were among the groups that pressed these companies. The letter marks mounting pressure on some of Silicon Valley's most influential companies and their ramping efforts to build facial recognition systems. "We are at a crossroads with face surveillance, and the choices made by these companies now will determine whether the next generation will have to fear being tracked by the government for attending a protest, going to their place of worship, or simply living their lives," Nicole Ozer, technology and civil liberties director for the ACLU of California, said. Recently, Google and Microsoft have acknowledged the risks involving facial recognition services and their potential for misuse and surveillance by bad actors.
Lost amongst the hype and hyperbole surrounding machine learning today, especially deep learning, is the critical distinction between correlation and causation. Developers and data scientists increasingly treat their creations as silicon lifeforms "learning" concrete facts about the world, rather than what they truly are: piles of numbers detached from what they represent, mere statistical patterns encoded into software. We must recognize that those patterns are merely correlations amongst vast reams of data, rather than causative truths or natural laws governing our world. As machine learning has expanded beyond its roots in the worlds of computer science and statistics into nearly every conceivable field, the data scientists and programmers building those models are increasingly detached from an understanding of how and why the models they are creating work. To them, machine learning is akin to a black box in which you blindly feed different mixes of training data in one side, twirl some knobs and dials and repeat until you get results that seem to work well enough to throw into production.
A facial recognition scan could become part of a standard medical checkup in the not-too-distant future. Researchers have shown how algorithms can help identify facial characteristics linked to genetic disorders, potentially speeding up clinical diagnoses. In a study published this month in the journal Nature Medicine, US company FDNA published new tests of their software, DeepGestalt. Just like regular facial recognition software, the company trained their algorithms by analyzing a dataset of faces. FDNA collected more than 17,000 images covering 200 different syndromes using a smartphone app it developed named Face2Gene.
A judge in California ruled Thursday that U.S. authorities cannot force people to unlock technology with fingerprint or facial recognition, even with a search warrant. A judge in California ruled Thursday that U.S. authorities cannot force people to unlock technology via fingerprint or facial recognition, even with a search warrant. Magistrate Judge Kandis Westmore, of the U.S. District Court for the Northern District of California, made the ruling as investigators tried to access someone's property in Oakland. Two people allegedly used Facebook messenger to threaten a victim with the release of an "embarrassing video" if they didn't hand over money. Authorities investigating the case requested a search and seizure warrant "to seize various items" believed to be at a home connected to the suspects.
A team of Portuguese researchers have developed a way to identify and track individual animals with artificial intelligence but without facial recognition, which could eventually be applied to public surveillance of humans, Defense One reports. The researchers used a convolutional neural network (CNN) to create idtracker.ai, CNNs are commonly used in facial biometrics, and NIST recently singled them out as the advance most responsible for the dramatic improvement of the technology's accuracy over the past five years. According to the researchers, idtracker.ai is "species agnostic," so will work with people or any other kind of animal. Microsoft called for government regulation of facial recognition in July of last year, saying it raises issues about privacy and other fundamental human rights.
I have written many times before about how AI is changing the landscape of marketing. It gives marketers the opportunity to reach more people while delivering personalized, relevant and timely content to them. Particularly interesting, is the use of AI in the retail industry. Many people fear that e-commerce giants threaten the existence of local retailers, but, brick-and-mortar stores aren't dying, they're simply evolving. The use of technology is enhancing customer connectivity and experience at every touchpoint, both online and in-store.
Media and privacy advocates have devoted a lot of attention to facial recognition as a means to identify, and then surveil, specific individuals in public. But facial recognition works far better on mugshots than in crowded public spaces where lighting, camera angles, and obscuring objects can't be controlled. And yet the debate might soon be moot, thanks to Portuguese researchers who say artificial intelligence can detect and identify individuals without facial recognition. They know, because they tested it on zebrafish and flies. The brain's cortex divides the visual field the way a map is divided into grids.
Facial recognition is making inroads into more aspects of our daily lives – and a majority of Americans seem to be okay with that. As a survey by the Center for Data Innovation shows, most Americans agree with the use of the technology in a security context. Less people would like to see facial recognition used in stores. Still, 49.1 percent agreed to its use if it was proving beneficial to catching shoplifters. Only 44.9 percent of respondents said they were okay with a deregulated use of the technology if it wasn't tied to a specific security scenario.
Americans are warming to the idea of facial recognition technology in the interest of public safety, according to a national survey. A poll of 3,151 adults in December found that Americans are more likely to relinquish their privacy if it benefits law enforcement. Facial recognition could help reduce shoplifting and other petty thefts, as well as speeding up airport security lines. Fifty five per cent of Americans do not believe that the government should impose strict regulations on the technology. Only eighteen per cent, or fewer than one in five, believed that there should be strict limitations, however.
Intel's new AI facial recognition kit could revolutionize mobility for wheelchair users. At CES, the firm demonstrated its incredible technology in the Hoobox Robotics' Wheelie 7 kit, which can be retrofitted to existing motorized chairs to give the rider control using only their facial expressions. This means wheelchair-users with impaired motor control in their arms and hands could drive themselves around without assistance, using uniquely programmed gestures such as an outstretched tongue to direct the chair's motion. Many wheelchair users, including quadriplegics and people with neurological diseases such as amyotrophic lateral sclerosis (ALS), are unable to press directional buttons or push the joystick required to drive a motorized chair. But, the new technology from Intel and Hoobox aims to change that.