In December, Microsoft President Brad Smith urged lawmakers to set rules on facial-recognition technology to prevent a privacy-threatening "race to the bottom." Now the company has joined a legislative fight in its home state against rules it says would be too restrictive. Microsoft is pushing back on a bill sponsored by a bipartisan group of Washington state lawmakers that would ban local and state government from using facial recognition until certain conditions are met, including a report by the state attorney general certifying that systems in use are equally accurate for people of differing races, skin tones, ethnicities, genders, or age. Microsoft has endorsed a different bipartisan privacy bill, modeled on European data laws. It contains less restrictive facial recognition rules, which closely mirror Smith's proposals from December.

[The drone pilot] knew the blind spots for it, where it could not be'hit',

Biologists the world over routinely pay companies to synthesize snippets of DNA for use in the laboratory or clinic. But intelligence experts and scientists alike have worried for years that bioterrorists could hijack such services to build dangerous viruses and toxins--perhaps by making small changes in a genetic sequence to evade security screening without changing the DNA's function. Now, the US government is backing efforts that use machine learning to detect whether a DNA sequence encodes part of a dangerous pathogen. Researchers are beginning to make progress towards designing artificial-intelligence-based screening tools, and several groups are presenting early results at the American Society for Microbiology (ASM) Biothreats meeting in Arlington, Virginia, on 31 January. Their findings could lead to a better understanding of how pathogens harm the body, as well as new ways for scientists to link DNA sequences to specific biological functions.

Internet of Business's comprehensive guide to where Industry 4.0 will lead manufacturers in the year ahead. Most manufacturers believe they are leading their markets in Industry 4.0 technologies, despite evidence to the contrary. There is a huge gap between the many companies that are exploring digital manufacturing strategies – via technologies such as automation, robotics, AI, and the Internet of Things – and those that are implementing them successfully. With Brexit looming, many manufacturers and solutions providers fear what this will mean for the wider European industrial community, which depends on the free movement of people and confident investment. The UK Budget recently sought to soften this blow by reinforcing the UK's commitment to a strong environment for international scientific collaboration. As part of this investment in R&D, the government will increase the Industrial Strategy Challenge Fund by £1.1 billion, supporting technologies of the future. This includes up to £121 million for the Made Smarter initiative to support the transformation of manufacturing through digitally enabled technologies, such as the Internet of Things and virtual reality.

If you've been following the work of Boston Dynamics (currently owned by Softbank) you've probably seen some of their four legged and wheeled robots which are able to navigate all sorts of obstacles and remain standing after being kicked, shoved, and pushed. While some of these robots, such as their BigDog, WildCat, and Spot appear to have an amazing ability to mimic an animal's gait. However, last year they introduced a two-legged anthropomorphic robot called Atlas, which was based on a more primitive biped called Petman. When I first saw Atlas I was impressed by its (his?) ability to perform some basic human-like tasks, such as picking up objects and resisting a human's attempts to knock it over. Still, it most often looked as though it would have a tough time passing a field sobriety test when it attempted to traverse even moderately rough terrain.

The central government plans to ease language requirements for foreign technical interns in the nursing care sector as part of its efforts to bring in more laborers from abroad, government sources have said. Japan opened up its nursing care sector to foreign nationals willing to work as trainees from November 2017. But the number of such trainees has seen sluggish growth apparently due to Japanese-language proficiency requirements, which have been set higher than those for interns in other sectors. Currently, care workers must have either reached the N4 level on the Japanese-Language Proficiency Test before entering the country or pass N3 a year after they arrive. Those who fail the N3 test have to return to their home country.

You sit down to watch a movie and ask Netflix for help. Zoolander 2?") The Netflix recommendation algorithm predicts what movie you'd like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don't believe it's possible to forecast which families will wind up on the streets. You'd love to move your city's use of predictive analytics into the 21st century, or at least into the 20th century. You just hired a pair of 24-year-old computer programmers to run your data science team. But should they be the ones to decide which problems are amenable to these tools? Or to decide what success looks like? You're also not reassured by the vendors the city interacts with.

On February 12, 2019 the Department of Defense released a summary and supplementary fact sheet of its artificial intelligence strategy ("AI Strategy"). The AI Strategy has been a couple of years in the making as the Trump administration has scrutinized the relative investments and advancements in artificial intelligence by the United States, its allies and partners, and potential strategic competitors such as China and Russia. The animating concern was articulated in the Trump administration's National Defense Strategy ("NDS"): strategic competitors such as China and Russia has made investments in technological modernization, including artificial intelligence, and conventional military capability that is eroding U.S. military advantage and changing how we think about conventional deterrence. As the NDS states, "[t]he reemergence of long-term strategic competition, rapid dispersion of technologies" such as "advanced computing, "big data" analytics, artificial intelligence" and others will be necessary to "ensure we will be able to fight and win the wars of the future." The AI Strategy offers that "[t]he United States, together with its allies and partners, must adopt AI to maintain its strategic position, prevail on future battlefields, and safeguard [a free and open international] order. We will also seek to develop and use AI technologies in ways that advance security, peace, and stability in the long run. We will lead in the responsible use and development of AI by articulating our vision and guiding principles for using AI in a lawful and ethical manner."

Sight Diagnostics, an Israeli medical devices startup that's using AI technology to speed up blood testing, has closed a $27.8 million Series C funding round. The company has built a desktop machine, called OLO, that analyzes cartridges manually loaded with drops of the patient's blood -- performing blood counts in situ. The new funding is led by VC firm Longliv Ventures, also based in Israel, and a member of the multinational conglomerate CK Hutchison Group. Sight Diagnostics said it was after strategic investment for the Series C -- specifically investors that could contribute to its technological and commercial expansion. And on that front CK Hutchison Group's portfolio includes more than 14,500 health and beauty stores across Europe and Asia, providing a clear go-to-market route for the company's OLO blood testing device.

This article reviews the applications of Bayesian Networks to Intelligent Autonomous Vehicles (IAV) from the decision making point of view, which represents the final step for fully Autonomous Vehicles (currently under discussion). Until now, when it comes making high level decisions for Autonomous Vehicles (AVs), humans have the last word. Based on the works cited in this article and analysis done here, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian Networks as a promising computer model for decision making. Further research should go into the direction of testing Bayesian Network models in real situations. In addition to the applications, Bayesian Network fundamentals are introduced as elements to consider when developing IAVs with the potential of making high level judgement calls.