DAVOS - Singapore has released a framework on how artificial intelligence (AI) can be ethically and responsibly used, which businesses in the Republic and elsewhere can adopt as they grapple with issues that have emerged with new technology. This model framework for AI governance is a "living document" intended to evolve along with the fast-paced changes in a digital economy. It takes in feedback from the industry, and will be tweaked when it gets more views. It was released by Mr S. Iswaran, Minister for Communications and Information, at the World Economic Forum (WEF) meeting which he is attending. The framework is the first in Asia to provide detailed and readily implementable guidance to private sector organisations using AI, said the Infocomm Media Development Authority (IMDA).

"In this environment, it's more important than ever that we have as many methods as possible to reach people where they are for health care and education," said Dr. Leana Wen, president of Planned Parenthood Federation of America. "The chatbot is one more critical tool we're piloting to provide that information to people." For years, conservative lawmakers have sought to defund Planned Parenthood, which works to offer low-cost or free health services to women through grants and government-supported health plans like Medicare and Medicaid. Roo, as the chatbot is called, is meant to extend the organization's reach by providing information to young people who don't have access to sex education or only have access to programs that teach abstinence, said Dr. Wen. Planned Parenthood funded the initiative through a private grant, as well as a sizable investment from Work & Co., a design and technology company that has worked with the organization for more than 14 months to plan and develop the product.

For the first time this year, the United States has sent two warships through the strategic Taiwan Strait, according to the Taiwanese government. The move risks further heightening tensions with China, which considers Taiwan a breakaway province and has not ruled out the use of force to bring the self-ruled island under its control. It is also likely to be viewed in Taiwan as a sign of support from US President Donald Trump's administration amid growing friction between Taipei and Beijing. Taiwan's defence ministry said in a statement late on Thursday the US ships were moving in a northerly direction and that their voyage was in accordance with regulations. It added that Taiwan closely monitored the operation to "ensure the security of the seas and regional stability".

The first time Azim Shariff met Iyad Rahwan--the first real time, after communicating with him by phone and e-mail--was in a driverless car. It was November, 2012, and Rahwan, a thirty-four-year-old professor of computing and information science, was researching artificial intelligence at the Masdar Institute of Science and Technology, a university in Abu Dhabi. He was eager to explore how concepts within psychology--including social networks and collective reasoning--might inform machine learning, but there were few psychologists working in the U.A.E. Shariff, a thirty-one-year-old with wild hair and expressive eyebrows, was teaching psychology at New York University's campus in Abu Dhabi; he guesses that he was one of four research psychologists in the region at the time, an estimate that Rahwan told me "doesn't sound like an exaggeration." Rahwan cold-e-mailed Shariff and invited him to visit his research group.

On paper alone you would never guess that I grew up poor and hungry. My most recent annual salary was over $700,000. I am a Truman National Security Fellow and a term member at the Council on Foreign Relations. My publisher has just released my latest book series on quantitative finance in worldwide distribution. None of it feels like enough. I feel as though I am wired for a permanent state of fight or flight, waiting for the other shoe to drop, or the metaphorical week when I don't eat. I've chosen not to have children, partly because--despite any success--I still don't feel I have a safety net. I have a huge minimum checking account balance in mind before I would ever consider having children. If you knew me personally, you might get glimpses of stress, self-doubt, anxiety, and depression.

US President Donald Trump was greeted with laughter in September when he told the UN that he had accomplished more than "almost any administration" in his country's history. Mr Trump admitted that he "didn't expect" that reaction - but that it was "just fine". It was one example of the many reasons people laugh - and most of them are not because somebody is being particularly funny. Laughter is primarily a form of bonding; we are 30 times more likely to laugh if we are with others than if we are alone. It is an ancient, universal reaction that is not even limited to humans; it has been documented in many animal species, including apes and even rats.

Since its founding, the Defense Advanced Research Projects Agency (DARPA) has been a hub of innovation. While created as the research arm of the Department of Defense, DARPA has played an important role in some of the technologies that have become (or will become) fundamental to modern human societies. In the 1960s and 1970s, DARPA (then known as ARPA), created ARPANET, the computer network that became the precursor to the internet. In 2003, DARP launched CALO, a project that ushered in the era of Siri and other voice-enabled assistants. In 2004, DARPA launched the Grand Challenge, a competition that set the stage for current developments and advances in self-driving cars. In 2013, DARPA launched the Brain Initiative, an ambitious project that brings together universities, tech companies and neuroscientists to discover how the brain works and develop technologies that enable the human brain to interact with the digital world. Among DARPA's many exciting projects is Explainable Artificial Intelligence (XAI), an initiative launched in 2016 aimed at solving one of the principal challenges of deep learning and neural networks, the subset of AI that is becoming increasing prominent in many different sectors.

THE GIST: Although media headlines imply we are already living in a future where AI has infiltrated every aspect of society, this actually sets unrealistic expectations about what AI can really do for humanity. Governments around the world are racing to pledge support to AI initiatives, but they tend to understate the complexity around deploying advanced machine learning systems in the real world. This article reflects on the risks of "AI solutionism": the increasingly popular belief that, given enough data, machine learning algorithms can solve all of humanity's problems. There is no AI solution for everything. All solutions come at a cost and not everything that can be automated should be.

This article was originally published as a TechRepublic cover story. Marcus Hall was nine years old when he first drove a tractor on his family's sprawling Iowa farm, eschewing Tonka trucks and Matchbox cars for long rides on heavy machinery. Growing up on a multigenerational family farm is common in an agricultural state like Iowa, where nearly 27 million acres are devoted to cropland--out of the 35 million acres that make up the state. Hall grew up with all the trappings of a future farmer, but a penchant for technology led him down a more experimental path--to the test farm of ag equipment giant John Deere. As manager of the test farm, Hall gets to run field trials of John Deere's high-tech farm equipment before it goes to market. "I just enjoy being out on the tractor," says Hall. "Plus, it's fun being part of this type of technology and the leading edge of what's out there." Download this article as a PDF (free registration required). It's a warm, breezy day in late May 2018, when we meet up with Hall at John Deere's test facility in Bondurant, IA. The farm sits on an unassuming patch of land framed by two-lane roads.

The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018. The GDPR is designed to give users more control over their personal data, which motivates us to explore machine learning frameworks with data sharing without violating user privacy. To meet this goal, in this paper, we propose a novel lossless privacy-preserving tree-boosting system known as SecureBoost in the setting of federated learning. This federated-learning system allows a learning process to be jointly conducted over multiple parties with partially common user samples but different feature sets, which corresponds to a vertically partitioned virtual data set. An advantage of SecureBoost is that it provides the same level of accuracy as the non-privacy-preserving approach while at the same time, reveal no information of each private data provider. We theoretically prove that the SecureBoost framework is as accurate as other non-federated gradient tree-boosting algorithms that bring the data into one place. In addition, along with a proof of security, we discuss what would be required to make the protocols completely secure.