The latest PoCs covered: analysis of large-scale supervisory data sets; executing high-value payments across currencies and borders; identifying and applying cross-cutting legal themes from regulatory enforcement actions; and measuring performance on the Bank's internal projects portfolio. The Bank of England has moved closer to the latest achievements in the financial technology arena, as shown by the results of the third round of Proofs of Concept (POCs) completed by its FinTech Accelerator. In an announcement on Monday, the Bank said the latest PoCs covered four key areas of its work: analysis of large-scale supervisory data sets; executing high-value payments across currencies and borders; identifying and applying cross-cutting legal themes from regulatory enforcement actions; and measuring performance on the Bank's internal projects portfolio. An important step was taken towards the use of artificial intelligence (AI) solutions, as the Bank has collaborated with Mindbridge Ai, a machine learning and AI firm, to explore the analytical value of using AI tools to detect anomalies in supervisory data sets. Via the use of a sample set of anonymised reporting data, it was found that Mindbridge's user interface is intuitive, allowing the user to explore a time series of each variable, while comparing the results to industry averages.

Senator Maria Cantwell, D-Wash., just drafted forward-looking legislation that aims to establish a select committee of experts to advise agencies across the government on the economic impact of federal artificial intelligence. The move is an early step toward formalizing the exploration of AI in a government context. But it could ultimately contribute to jump-starting AI-focused programs that help stimulate the United States economy, benefit citizens, uphold data security and privacy, and eventually ensure America is successful during the initial introduction of this important technology to U.S. consumers. The presence of legislation could also lend legitimacy to the prospect of near-term government investment in AI innovation -- something that may even sway Treasury Secretary Steve Mnuchin and others away from their belief that the impact of AI won't be felt for years to come. Indeed, other than a few economic impact and policy reports conducted by the Obama Administration -- led by former U.S. Chief Data Scientist DJ Patil and other tech-minded government leaders -- this is the first policy effort toward moving the U.S. public sector past acknowledging its significance, and toward fully embracing AI technology.

As their case gained attention, Trump intervened by asking National Security Council officials to find a way for them to travel, officials said. Ultimately the State Department, which adjudicates visa applications, asked the Homeland Security Department to let them in on "parole," a temporary status used only in exceptional circumstances to let in someone who is otherwise ineligible to enter the country. The U.S. granted parole after determining that it constituted a "significant public benefit."

Jaw-dropping tech demo shows off the amazing (and, frankly, worrying) power of artificial neural networks. Regardless of which side of the political aisle you sit on, chances are you've got some strong opinions on "fake news." Whether it's comments taken out of context, or quotes being outright fabricated, fake news is a frustrating byproduct of today's twenty-first century news cycle. Well, we're sorry to tell you that things are about to get much, much worse! At least, that's based on a frankly crazy demonstration of artificial intelligence carried out by computer scientists at the University of Washington.

Automation, digital platforms, and other innovations are changing the fundamental nature of work. Understanding these shifts can help policy makers, business leaders, and workers move forward. The world of work is in a state of flux, which is causing considerable anxiety--and with good reason. There is growing polarization of labor-market opportunities between high- and low-skill jobs, unemployment and underemployment especially among young people, stagnating incomes for a large proportion of households, and income inequality. Migration and its effects on jobs has become a sensitive political issue in many advanced economies. And from Mumbai to Manchester, public debate rages about the future of work and whether there will be enough jobs to gainfully employ everyone.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. We could watch these water drones swim and dive all day. They were developed by APIUM Swarm Robotics, which took them for a swim off of Catalina Island in California.

Nowadays, with the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using forecasted weather data. In this study, the performance of several commonly used forecasting methods in the presence of weather predictors with uncertainty is assessed and compared. Accordingly, both observed and forecasted weather data are collected, then the influential predictors for solar PV generation forecasting model are selected using several measures. Using observed and forecasted weather data, an analysis on the uncertainty of weather variables is represented by MAE and bootstrapping. The energy forecasting model is trained using observed weather data, and finally, the performance of several commonly used forecasting methods in solar energy forecasting is simulated and compared for a real case study.

Tech Review (TR 2017-07-15)--Domain Mondo's weekly review of tech news: Features • 1) How Google Cloud Plans to Overtake Amazon's AWS: TensorFlow, 2) FCC Net Neutrality Nomination Hearing, 3) Mars Rover 2020 and Europa Clipper, 4) Q2 2017 earnings T-Mobile $TMUS & Qualcomm $QCOM, 5) BlackHat and DefCon, 6) Rural Broadband Internet, 7) ICYMI Tech News. 1) How Google Cloud Plans to Overcome Amazon's AWS Lead in Cloud Computing: TensorFlow TensorFlow: Machine Learning for Everyone "TensorFlow [domain: tensorflow.org] is becoming the clear leader among programmers building new things with machine learning. "We have significant usage today, and it's accelerating," says Jeff Dean, who led TensorFlow's design and heads Google's core artificial- intelligence research group. Once you've built something with TensorFlow, you can run it anywhere--but it's especially easy to transfer it to Google's cloud platform." Other Tech News: 2) FCC Net Neutrality Nomination Hearing FCC - U.S. Senate Committee On Commerce, Science, & Transportation: "Ajit Varadaraj Pai, of Kansas, to be a Member of the Federal Communications Commission (Reappointment) - Jessica Rosenworcel, of Connecticut, to be a Member of the Federal Communications Commission (Reappointment) - Brendan Carr, of Virginia, to be a Member of the Federal Communications Commission," Wednesday, July 19, 2017 10:00 a.m. See also Poll of Republican voters shows that a majority of President Trump's supporters are in favor of net neutrality rules and also oppose the pending AT&T-Time Warner merger TheHill.com.

Inspite of all the current hype, AI is not a new field of study, but it has its ground in the fifties. If we exclude the pure philosophical reasoning path that goes from the Ancient Greek to Hobbes, Leibniz, and Pascal, AI as we know it has been officially started in 1956 at Dartmouth College, where the most eminent experts gathered to brainstorm on intelligence simulation. This happened only a few years after Asimov set his own three laws of robotics, but more relevantly after the famous paper published by Turing (1950), where he proposes for the first time the idea of a thinking machine and the more popular Turing test to assess whether such machine shows, in fact, any intelligence. As soon as the research group at Dartmouth publicly released the contents and ideas arisen from that summer meeting, a flow of government funding was reserved for the study of creating a nonbiological intelligence. Atthat time, AI seemed to be easily reachable, but it turned out that was not the case.

The following essay is reprinted with permission from The Conversation, an online publication covering the latest research. As an artificial intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb?