The government must intervene to stop automation driving up wage inequality, a think tank has warned. The Institute for Public Policy Research said robots would not necessarily be bad for the economy. However, it warned lower-skilled jobs were much more likely to be phased out in the coming decades, and only higher-skilled workers would be able to command better wages. The government said it was committed to making automation work for everyone. According to the Institute for Public Policy Research (IPPR) - a centre-left think tank - automation could raise UK productivity growth by between 0.8 to 1.4% annually, and boost GDP by 10% by 2030.

Predictions for 2018 are here! The "artificial" in artificial intelligence might freak some people out, but thinking about it in a different way could assuage their fears. Airbnb plans to make AR and VR a part of the booking process so you can book with confidence. And in drone news: Add restoring a really old cathedral in Germany to the list of things UAVs can do. Consider this: 450,000 tweets are posted every minute.

Nearly half of British jobs are at risk of being taken over by robots, a report warned last night. Technological advances mean 44 per cent of current roles could be automated, the Institute for Public Policy Research (IPPR) think-tank found. Employees working in catering, retail and agriculture are most under threat by the march of machines. Nearly half of British jobs are at risk of being taken over by robots, a report warned last night. The report raised the example of how Amazon has used sensors in its warehouses to monitor productivity.

It sounds like a dream: Poor villagers are handed money regularly every month, for several years, with no conditions attached. An American organization is currently testing the model in Kenya. When the village elder came to her in September to invite her to a meeting under the acacia trees, Norah Odhiambo was skeptical. Storm clouds were gathering above nearby Lake Victoria, the 34-year-old relates, and she set aside the machete she uses to clear brush from her neighbor's field for a few shillings a day. A new aid organization called GiveDirectly, the village elder said, would like to introduce itself.

We have been hearing a lot lately on the topic of American greatness, where it went, and how to reclaim it. But greatness is complicated, and our ideas of what is great and what is not have changed over time. In this column, I ask what the history of one mighty corporation--IBM--can tell us about the rise and fall of a particular kind of American greatness. Decade after decade, IBM has been one of the world's largest, most profitable, and most admired companies. Of all American businesses, only General Electric, Apple, Microsoft, and Exxon-Mobile have generated more wealth.a Despite recent troubles, it has been ranked in the 2010s as the number one company for leaders (Fortune), the greenest company (Newsweek), the second most valuable global brand (Interbrand), the second most respected company (Barron's) and the fifth most admired (Fortune). IBM technical contributions to computing are second to none. Its researchers won six Turing awards and, more startling, four Nobel prizes. Its engineers produced the first hard disk drive, the first floppy disk drive, the first architecture implemented over a range of diverse but compatible machines, the first widely used high-level programming language, the relational database, the first scientific supercomputer, the first RISC designs, and the first DRAM chip. As recently as 2014, IBM ranked ahead of its old adversary Microsoft on Fortune's lists of the largest U.S. companies (20th place) and of the firms most admired by managers (16th place). In ways good and bad, IBM has been at the forefront of changes in American business and in America's relationships with the world.

The development of high-performance software has always suffered from a tension between achieving high performance on the one hand and portability and simplicity on the other hand. By specializing an algorithm for optimal performance, considering the memory hierarchy and other architectural particulars, we introduce architecture-specific detail. This obscures algorithmic structure and conflates the general with the specific, compromising simplicity and clarity. It also hurts portability to all but very similar architectures--simple changes, such as different cache sizes, can have substantial performance implications. Moreover, distinctly different architectures, such as CPUs versus GPUs versus DSPs, often require fundamentally different optimization strategies.

In 2015, when considering whether to apply for the position of executive director for the Computer Science Teachers Association (CSTA), I read the book Stuck in the Shallow End by Jane Margolis (http://bit.ly/2zcEZeP). Computer science (CS) remains one of the least diverse of the STEM disciplines, and Margolis' book opened with a compelling comparison between the racial divide in swimming and the divide we see today in CS. Understanding that teachers are critical to access, and how we teach can influence what field students might pursue, I saw that within CSTA there was opportunity to make a positive difference in the world. Fast-forward to 2017, and thanks to Stephen Ibaraki, I had the opportunity to participate in the AI for Good Summit held in Geneva, Switzerland (http://bit.ly/2h494Xi). The Summit crystalized for me the scope, magnitude, and importance of an organization like CSTA to change the world.

The rise of the machine economy risks social disruption by widening the gap between rich and poor in Britain, as automation threatens jobs generating £290bn in wages. Jobs accounting for a third of annual pay in the UK risk being automated, according to the study by the IPPR thinktank. Warning that low-paid roles are in the greatest danger, it urged ministers to head off the prospect of rising inequality by helping people retrain and share in the benefits from advances in technology. The study for the IPPR's commission on economic justice, which features senior business and public figures including the archbishop of Canterbury, called on the government to take a greater role in managing the adoption of robotics, artificial intelligence and other methods of job automation in the workforce. Mathew Lawrence, a senior research fellow at the IPPR, said: "Managed badly, the benefits of automation could be narrowly concentrated, benefiting those who own capital and highly skilled workers. The IPPR estimates that 44% of jobs in the UK economy could feasibly be automated, equating to more than 13.7 million people who together earn about £290bn. Although it doesn't give a forecast for how long this would take, it cited US research which estimates the changes could occur over the next 10 or 20 years. From the collective pay pool worth £290bn, middle-income jobs such as call-centre workers, secretaries and factory workers are likely to be hollowed out. Low-skilled workers could also lose their jobs or face fewer hours from greater levels of automation. At the same time the highest earners and workers able to retrain will gain higher pay thanks to rising productivity – which means more output being generated per hour worked. The research follows similar studies warning of the risks arising from the current rapid advances in technology, which have enabled machines to take on work that was once the preserve of humans. The Bank of England has said as many as 15m jobs in Britain are under threat. Measures called for in the IPPR report include a UK skills system to help retrain those affected by the introduction of machines into the workforce, as well as an ethics watchdog to oversee the use of automating technologies modelled on the Human Fertilisation and Embryology Authority, which regulates embryo research. Ministers are also being urged to consider new models of company ownership in the face of increasing returns to asset owners, because rising automation could result in higher profits for those who own companies - at the expense of workers' salaries. Carys Roberts, a research fellow at the IPPR, said: "Some people will get a pay rise while others are trapped in low-pay, low-productivity sectors.

This paper considers inference over distributed linear Gaussian models using factor graphs and Gaussian belief propagation (BP). The distributed inference algorithm involves only local computation of the information matrix and of the mean vector, and message passing between neighbors. Under broad conditions, it is shown that the message information matrix converges to a unique positive definite limit matrix for arbitrary positive semidefinite initialization, and it approaches an arbitrarily small neighborhood of this limit matrix at a doubly exponential rate. A necessary and sufficient convergence condition for the belief mean vector to converge to the optimal centralized estimator is provided under the assumption that the message information matrix is initialized as a positive semidefinite matrix. Further, it is shown that Gaussian BP always converges when the underlying factor graph is given by the union of a forest and a single loop. The proposed convergence condition in the setup of distributed linear Gaussian models is shown to be strictly weaker than other existing convergence conditions and requirements, including the Gaussian Markov random field based walk-summability condition, and applicable to a large class of scenarios.

This is an area where statistics-and MLbased that cognitive system developers currently address systems can be symbiotic with cognitive systems: the and methodological preferences that they, by and former can provide advanced computation frameworks large, share. For some of the issues, the consensus is while the latter can provide content-related not entirely universal, which is to be expected for a insights into the choice of the inventory of features to group of active developers. Still, the general points of consensus should help to characterize the overall be used in making decisions.