Anastasia Gnezditskaia is a writer / analyst covering France, Benelux and Morocco. Based in Antwerp, Belgium, she has a background working for trade publications covering markets and their regulation in Washington, D.C., where she lived for 10 years. Following this she managed international development projects in Africa at the World Bank, and worked as a journalist covering Congress, federal government agencies and financial markets, including energy futures.

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.

We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict open types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets. Our data and model can be downloaded from: http://nlp.cs.washington.edu/entity_type

This article was written for The Data Incubator by Jay Kaiser, a Fellow of our 2018 Winter cohort in Washington, DC who landed a job with our hiring partner, ZeniMax Online Studios, as a Big Data Engineer. The 2016 Presidential Election was, in a single word, weird. So much happened during the months leading up to November that it became difficult to keep track with what who said when and why. However, the finale of the election that culminated with Republican candidate Donald J. Trump winning the majority of the Electoral College and hence becoming the 45th President of the United States was an outcome which at the time I had thought impossible, if solely due to the aforementioned eccentric series of events that had circulated around Trump for a majority of his candidacy. Following the election, the prominent question that could not leave my mind was a simple one: how?

China purchased 141,000 industrial robots in 2017, up 58.1 per cent year-on-year, but foreign brands accounted for nearly three-quarters of that, showing that the gap is still widening between Chinese robot makers and their foreign peers. The China International Robot Industry Summit, held in Shanghai, said the sales and growth rate of industrial robots hit records in 2017. Among industrial robots, 37,825 were domestically manufactured, up 29.8 per cent year-on-year. "As robotics is expanding into nearly every industry, Chinese robot makers should realise the gap between them and foreign brands, take advantage of China's robotics development boom and learn from foreign experience to help China grow from the world's largest robot market into a robot manufacturing power," said Qu Daokui, president of China Robot Industry Alliance and chief executive of the Shenyang-based Siasun Robot and Automation company. According to Mr Qu, foreign robot makers sold 103,191 robots to China in 2017, up 71.9 per cent from a year earlier.

The government has published its long-awaited Brexit White Paper. The document is 104 pages long and follows last week's Chequers agreement which set out the sort of relationship the UK wants with the EU after Brexit. The White Paper is split into four chapters: economic partnership, security, cooperation and institutional arrangements. So here are the key excerpts from the chapter on "economic partnership" and what they mean. This is a line that emerged in the Chequers statement last Friday, and it is one of the most important in this White Paper. It is the UK government's answer to the concerns expressed by businesses that rely on "just-in-time" manufacturing supply chains (such as car manufacturers), and to the need to avoid the reimposition of a hard border in Ireland.

Unleashing a legal and ethical debate worldwide, AI (artificial intelligence) is progressing with leaps and bounds as it portends to change human society forever. For example, if a driverless car meets with an accident involving fatalities, it is the algorithm operator who faces "product liability" rules. In the case of AI used in conventional war, machines killing humans is an ethically chilling concept. Carrying major implications, it is feared that the proto-AI technologies of today are going to evolve into true AI super-intelligence very rapidly without giving enough time for research into the pros and cons. As apprehensions of a "hyper-war scenario" build up, the main challenge remains: how to place the human factor in AI and prevent a drastic downgrade in military security as combat involving the technology changes the dimensions of warfare.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Starting in 2012, the Supreme Court's approach to digital privacy has undergone a seismic shift. In a series of recent cases on location tracking and cellular phone searches, the court has recognized that, when it comes to big data, old rules about our expectations of privacy may not apply. Because information can now be gathered, stored, and analyzed cheaply, the Supreme Court has recently found that Fourth Amendment protections must be carefully recalibrated to prevent unchecked police power. Supreme Court nominee Brett Kavanaugh, however, has exhibited a contrasting and outdated understanding of privacy. As important questions come before the court in the future--on police drone surveillance, on the use of facial recognition software, on government access to the vast troves of different kinds of digital data companies hold on us--it's crucial to understand where Kavanaugh stands.

Computer programs can, in some cases, predict chemical toxicity as well as tests done on rats and other animals.Credit: Coneyl Jay/SPL Machine-learning software trained on masses of chemical-safety data is so good at predicting some kinds of toxicity that it now rivals -- and sometimes outperforms -- expensive animal studies, researchers report. Computer models could replace some standard safety studies conducted on millions of animals each year, such as dropping compounds into rabbits' eyes to check if they are irritants, or feeding chemicals to rats to work out lethal doses, says Thomas Hartung, a toxicologist at Johns Hopkins University in Baltimore, Maryland. "The power of big data means we can produce a tool more predictive than many animal tests." In a paper published in Toxicological Sciences1 on 11 July, Hartung's team reports that its algorithm can accurately predict toxicity for tens of thousands of chemicals -- a range much broader than other published models achieve -- across nine kinds of test, from inhalation damage to harm to aquatic ecosystems. The paper "draws attention to the new possibilities of big data", says Bennard van Ravenzwaay, a toxicologist at the chemicals firm BASF in Ludwigshafen, Germany.

With a new generation quickly entering the modern workforce, it is never been more important for public-sector agencies to take a thoughtful, strategic approach to human capital management. It is hard to imagine a better time to start down that road, with state-of-the-art IT systems taking the guesswork out of human resources decisions and delivering a level of management insight that would have been unimaginable even a decade ago. That technology is certainly the cornerstone of government's efforts to stay ahead of a rapidly shifting workforce. Yet it is worth remembering that this is not the first massive wave of transformation the U.S. public service has faced. We have been here before, and it is useful to draw insight and inspiration from another memorable moment when America had to surmount an overwhelming HR challenge to achieve a fundamentally important objective.