They call Amazon the everything store--and Tuesday, the world learned about one of its lesser-known but provocative products. Police departments pay the company to use facial-recognition technology Amazon says can "identify persons of interest against a collection of millions of faces in real-time." More than two dozen nonprofits wrote to Amazon CEO Jeff Bezos to ask that he stop selling the technology to police, after the ACLU of Northern California revealed documents to shine light on the sales. The letter argues that the technology will inevitably be misused, accusing the company of providing "a powerful surveillance system readily available to violate rights and target communities of color." The revelation highlights a key question: What laws or regulations govern police use of the facial-recognition technology?

In late 2017, a government employee in Livingston County, Mich., plugged a personal laptop into the workplace server -- inadvertently exposing the network to malware. "We had 9,000 attacks within a few minutes from this computer," says Rich Malewicz, CIO and security officer for Livingston County. The county detected the attack and stopped it quickly using a program called Darktrace, which uses artificial intelligence (AI) and machine learning to provide real-time alerts about abnormal activity on the network. "No device on the network detected [the attack] except for Darktrace," he says. More local and state governments are eyeing AI and machine learning as tools to help combat cyberattacks, in part because hackers themselves have adopted the technology.

Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning. We study a family of sequential codes parameterized by recurrent neural network (RNN) architectures. We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms). We show strong generalizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a `good' explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.

An image from the product page of Amazon's Rekognition service, which provides image and video facial and item recognition and analysis. SAN FRANCISCO – Two years ago, Amazon built a facial and image recognition product that allows customers to cheaply and quickly search a database of images and look for matches. One of the groups it targeted as potential users of this service was law enforcement. At least two signed on: the Washington County Sheriff's Office outside of Portland, Ore., and the Orlando Police Department in Florida. Now the ACLU and civil rights groups are demanding that Amazon stop selling the software tool, called Rekognition, to police and other government entities because they fear it could be used to unfairly target protesters, immigrants and any person just going about their daily business.

Korean tech giant Samsung has announced a major investment in artificial intelligence research in the UK. The company is to open an AI research lab in Cambridge, in a move that has been welcomed by the prime minister. The lab will join other Samsung centres dedicated to the topic, based in Moscow and Toronto. The technology is now seen as key to competing in many industries. The UK has been a hotspot for AI research.

It was the first day of school in Russia, a much-beloved unofficial holiday, and President Vladimir Putin was on stage in a national TV broadcast, chatting with jeans-clad teenagers about the future. "Artificial intelligence is the future," he told them, "not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world." Then, this March, in the final moments of Putin's re-election campaign, came a stern message to lawmakers at his annual address to parliament: "The speed of technological progress is accelerating sharply... Those who manage to ride this technological wave will surge far ahead. Those who fail to do this will be submerged and drown."

Fixing Britain's stuttering Brexit programme or promising that artificial intelligence (AI) will help make a huge breakthrough in tackling cancer? Such is the malaise that afflicts Britain's pledge to leave the European Union that Prime Minister Theresa May appears to have chosen the latter option. This could, however, become a reality much sooner than many people think. "My ambition is that within 15 years we'll diagnose cancer much earlier in at least 50,000 more people a year," Mrs May said on Monday, citing AI as the reason. She said early diagnosis of people at an early stage of prostate, ovarian, lung and bowel cancer would lead to around 22,000 fewer people in the UK dying each year by 2033.

India will enlist the help of artificial intelligence to develop weapons, defense, and surveillance systems, government officials announced today. "The world is moving towards an artificial intelligence-driven ecosystem," Dr. Ajay Kumar, secretary at the defense ministry, said in a statement. "India is also taking necessary steps to prepare our defense forces for the war of the future." A 17-person task force is working on an AI roadmap for India's armed forces, the Times of India reports. Within the next two years, the task force will recommend ways machine learning can be incorporated into the country's aviation, naval, land, cybersecurity, nuclear, and biological resources, specifically as it relates to the areas of autonomous weapons systems and unmanned surveillance.

If you want to understand what's happening with artificial intelligence (AI) and cybersecurity, look no further than this week's news. On Monday, Palo Alto Networks introduced Magnifier, a behavioral analytics solution that uses structured and unstructured machine learning to model network behavior and improve threat detection. Additionally, Google's parent company, Alphabet, announced Chronicle, a cybersecurity intelligence platform that throws massive amounts of storage, processing power, and advanced analytics at cybersecurity data to accelerate the search and discovery of needles in a rapidly growing haystack. So, cybersecurity suppliers are innovating to bring AI-based cybersecurity products to market in a big way. OK, but is there demand for these types of advanced analytics products and services?