Yamato Transport and Rakuten are among a group of companies set to partner with the Japanese government to test unmanned delivery robots on public roads. The government will set up a council of officials from the public and private sectors next week to identify potential issues, including liability in the event of accidents and how to maintain safety. The group will also examine operating rules that could eventually be added to the Road Traffic Act. It is hoped that the robots will alleviate the labor shortage in Japan's logistics sector, as well as create new business opportunities. The robots are equipped with cameras and GPS to deliver goods without human intervention.

A group of experts on Tuesday warned a House panel that artificial intelligence is not capable of sweeping up the full breadth of online extremist content -- in particular posts from white supremacists. At a House Homeland Security subcommittee hearing, lawmakers cast doubt on claims from top tech companies that artificial intelligence, or AI, will one day be able to detect and take down terrorist and extremist content without any human moderation. Rep. Max RoseMax RoseCongress needs to continue fighting the opioid epidemic Hillicon Valley: Investigation finds federal agencies failed to address cyber vulnerabilities Officials crack down on illegal robocallers Warren offers plan to secure elections Senators grill Google exec on'persuasive technology' Artificial intelligence can't solve online extremism issue, experts tell House panel MORE (D-N.Y.), the chairman of counterterrorism subcommittee holding the hearing, said he is fed up with responses from companies like Google, Twitter and Facebook about their failure to take down extremist posts and profiles, calling it "wanton disregard for national security obligations." "We are hearing the same thing from social media companies, and that is, 'AI's got this, it's only gonna get better,' " Rose said during his opening remarks. "Nonetheless ... we have seen egregious problems."

Maintaining a competitive edge over global competition in artificial intelligence (AI) has risen swiftly over the past several years as a bipartisan national security priority. That high-level strategic guidance is now transitioning to implementation. Two important steps were taken recently. These documents made clear that delivering results for the nation on AI is the responsibility of individual federal departments and agencies doing their part. These documents also rightly advance what my CSIS colleagues and I presented in a previous report on AI and National Security termed an "AI ecosystem."

The opening up of data is the necessary step to promote Artificial Intelligence (AI) adoption across all UK economy sectors. According to Margot James, minister of state for the Department for Digital, Culture, Media and Sport "The full benefits to society and the economy that can come from Artificial Intelligence (AI) can only be realised if it is widely used,". Additionally in terms of planning, "That means government working together with industry to seize the prize of a reported additional £232bn on GDP by 2030." "Data is a critical part of our national digital infrastructure and fundamental, of course, to AI," said James. "Without access to good quality data from a range of sources, AI technologies cannot deliver on that promise of better, more efficient and seamless services. Government is really committed to opening up more data in a way that makes it reusable and easily accessible."

It's true, the recent advancements of narrow AI are mind-blowing: algorithms are beating humans in applications ranging from gaming to healthcare. But however'magical' these accomplishments may seem – especially when we retrospectively look back at what we thought AI would be able to do just a couple of years ago – this is far from the reality of the everyday work we do at Ericsson. For us, the'magic' of AI is making it work for us to make our everyday lives better and more efficient as a result. Now is the moment when AI goes from hype to reality. Already in our own industry we can see that AI is being embraced by service providers around the world.

American businesses are telling the Trump administration that an escalating trade war with China will hurt the U.S. economy. This comes as public hearings are being held to consider extending the 25% tariffs to practically all Chinese imports. Tariffs on imports from China could price one-fourth of U.S. families out of the market to buy a video game system this holiday season, game console makers say. Microsoft, Nintendo and Sony, in a letter to the U.S. Trade Representative's Office dated June 17, wrote that tariffs could price an Xbox, Switch or PlayStation "out of reach for many American families." Those shoppers who do buy a new game system will collectively pay $840 million more this holiday season than they would have had tariffs not been imposed, the companies say, citing a study from the independent economic group Trade Partnership for the Consumer Technology Association.

Fairness has emerged as a key issue in machine learning as it is increasingly used in areas like hiring [Dastin, 2018], healthcare[Gupta and Mohammad, 2017], and criminal justice [Equivant, 2019]. In particular, models' predictions should not lead to decisions that discriminate on the basis of a legally protected attribute, such as race or gender. Among the proposals to address this issue, a growing body of work focuses on learning et al., 2017, del Barrio et al., 2018, Feldmanfair representations of data for downstream modeling [Calmon 2015, Johndrow and Lum, 2019, Kamiran and Calders, 2012]. Most of these approaches are modelet al., agnostic, which provides flexibility when working with the learned representations, but comes at the cost of potentially suboptimal results in terms of both fairness and accuracy. In this work, we present a new approach for fair representation learning that takes into account the target hypothesis class of models that will be learned from the representation. Specifically, we show how to leverage information about the reproducing kernel Hilbert space (RKHS) to learn a fair representation for kernel-based models with provable fairness and accuracy guarantees. Our approach builds on the classic Sufficient Dimension Reduction (SDR) framework [Li, 1991, Cook 1991, Cook, 1998, Fukumizu et al., 2004, 2009, Wu et al., 2009, Cook and Forzani, 2009]and Weisberg, which is used to compute a low-dimensional projection of the feature vector X that captures all information related to the response Y. Our key insight is that we can instead perform SDR with respect to the protected attributes S, and then take the orthogonal complement of the resulting projection to obtain a fair subspace of the RKHS that captures information in X unrelated to S. We show that functions in the fair subspace 2.2), and we leverage this fact to prove that our approachwill be independent of S under mild conditions (§

A team of MIT researchers is making it easier for novices to get their feet wet with artificial intelligence, while also helping experts advance the field. In a paper presented at the Programming Language Design and Implementation conference this week, the researchers describe a novel probabilistic-programming system named "Gen." Users write models and algorithms from multiple fields where AI techniques are applied -- such as computer vision, robotics, and statistics -- without having to deal with equations or manually write high-performance code. Gen also lets expert researchers write sophisticated models and inference algorithms -- used for prediction tasks -- that were previously infeasible. In their paper, for instance, the researchers demonstrate that a short Gen program can infer 3-D body poses, a difficult computer-vision inference task that has applications in autonomous systems, human-machine interactions, and augmented reality.

IT IS ONE of the most widely quoted statistics of recent years. No report or conference presentation on the future of work is complete without it. Think-tanks, consultancies, government agencies and news outlets have pointed to it as evidence of an imminent jobs apocalypse. The finding--that 47% of American jobs are at high risk of automation by the mid-2030s--comes from a paper published in 2013 by two Oxford academics, Carl Benedikt Frey and Michael Osborne. It has since been cited in more than 4,000 other academic articles.

Eight years ago, Visa, Mastercard and PayPal, which together make up more than 97% of the global market for payment services, cut off funding to WikiLeaks (you could still donate to the Ku Klux Klan, the English Defence League or Americans for Truth about Homosexuality). The blockade, backed by Republican senators, was political: WikiLeaks had published Chelsea Manning's material documenting US military drone strikes and civilian killings in Iraq; stopping inflowing cash silenced Julian Assange's outfit, albeit temporarily. Perhaps it was time for a cryptocurrency to stride from the proverbial phone booth, underpants over its tights, and save the day? After all, bitcoin's philosophy was that it would cut out the middleman, whether state functionary or corporate lackey, and realise a radical future in which, for instance, Afghan women, prohibited from opening bank accounts, might work and get paid in bitcoin. Certainly, in December 2010 there was much enthusiasm from cypherpunks for WikiLeaks to link to bitcoin on their website for donations.