Donald Trump wants Russian President Vladimir Putin to visit the US this autumn. The two men met at the beginning of this week in Finland, and despite some significant fall-out, planning is under way for a second get-together. Moscow hasn't reacted publicly yet to the invitation. "OK... that's going to be special," laughed US Director of National Intelligence Dan Coats when told about it. Well, the US intelligence services were at the centre of the aforementioned fall-out, after the president seemed to reject their view on Russian meddling in the 2016 US election and side with Moscow instead.

Who's doing this? 75% of the Fortune 500 companies have some kind of smart data or semantics program underway, most under the banner of 360 initiatives, comprehensive enterprise data systems, or machine learning/data science projects. Amazon has recently added linked data capabilities to their AWS infrastructure with the Neptune project, and social media giants have built their entire data infrastructure around smart ontological data. Moreover, China, Japan, England, the OECD, and the United States have all moved critical data resources into semantic form, and semantics has become one of the hottest areas for investment banks such as Wells Fargo, Morgan Stanley, Citigroup, Goldman Sachs and others. It even ties into such cutting edge technologies as Blockchain and the Internet of Things.

The race to become the global leader in artificial intelligence (AI) has officially begun. In the past fifteen months, Canada, China, Denmark, the EU Commission, Finland, France, India, Italy, Japan, Mexico, the Nordic-Baltic region, Singapore, South Korea, Sweden, Taiwan, the UAE, and the UK have all released strategies to promote the use and development of AI. No two strategies are alike, with each focusing on different aspects of AI policy: scientific research, talent development, skills and education, public and private sector adoption, ethics and inclusion, standards and regulations, and data and digital infrastructure. This article summarizes the key policies and goals of each strategy, as well as related policies and initiatives that have announced since the release of the initial strategies. It also includes countries that have announced their intention to develop a strategy or have related AI policies in place. I plan to continuously update this article as new strategies and initiatives are announced. If a country or policy is missing (or if something in the summary is incorrect), please leave a comment and I will update the article as soon as possible. I also plan to write an article for each country that provides an in-depth look at AI policy. Once these articles are written, I will include a link to the bottom of each country's summary. June 28: Publication of original article, included Australia, Canada, China, Denmark, EU Commission, Finland, France, Germany, India, Japan, Singapore, South Korea, UAE, US, and UK.

For each hotel we receive dozens of images and face the challenge of choosing the most "attractive" image for each offer on our offer comparison pages, as photos can be just as important for bookings as reviews. Given that we have millions of hotel offers, we end up with more than 100 million images for which we need an "attractiveness" assessment. We addressed the need to automatically assess image quality by implementing an aesthetic and technical image quality classifier based on Google's research paper "NIMA: Neural Image Assessment". NIMA consists of two Convolutional Neural Networks (CNN) that aim to predict the aesthetic and technical quality of images, respectively. The models are trained via transfer learning, where ImageNet pre-trained CNNs are fine-tuned for each quality classification tasks.

HM Revenue & Customs' decision to utilise more artificial intelligence in its quest to gather evidence for tax evasion investigations has led to a decline in the number of raids at business premises across the UK. The number of raids fell overall by 30% within 12 months. HMRC officers raided 471 commercial properties in the 12 months to April 2018, compared with 669 in the previous year, according to figures obtained in a recent Freedom of Information request. In March, HMRC's Acting Digital Transformation Director, Brigid McBride, confirmed that the tax authority was keen to use AI to improve departmental efficiencies and ease the complexity of tax investigations. HMRC has set its department a goal of automating ten million processes by the end of 2018.

Global telecoms tech firm Cisco has announced it will be investing $100m (£76.8m) Backed by Prime Minister Theresa May and digital secretary Jeremy Wright to run in tandem to the government's industrial strategy, Cisco will partner with University College London (UCL) to open one of the world's largest research centres for AI. The centre will become a hub for addressing ways to integrate AI into British industry by housing more than 200 UCL academics and researchers, as well as developing up and coming AI talent. Announced this morning at Downing Street, May called the investment a "vote of confidence" in the government's industrial strategy. "We believe that the UK's expertise in AI and its commitment to making sure future innovators have the right digital skills will help ensure the nation's citizens are well-positioned to capture the opportunity ahead," said Chuck Robbins, Cisco's chairman and chief executive.

Can't get your Amazon Echo or Google Home smart speaker to understand you? It might be your accent. Two teams of researchers recruited by The Washington Post found that Amazon's Alexa assistant and the Google Assistant exhibit a pattern of poor performance with certain dialects. They tested thousands of voice commands dictated by more than 100 people across 20 cities, and the results were conclusive: Google's and Amazon's speakers were 30 percent less likely to understand non-American accents than those of native-born users, and the overall accuracy rate for Chinese, Indian, and Spanish accents was about 80 percent. It points to clear evidence of bias in the data used to train the two voice recognition systems, Rachael Tatman, a Kaggle data scientist with expertise in speech recognition, told The Washington Post.

A recent survey by Deloitte of "aggressive adopters" of cognitive technologies found that 76% believe that they will "substantially transform" their companies within the next three years. There probably hasn't been this much excitement about a new technology since the dotcom boom years in the late 1990s. The possibilities would seem to justify the hype. In the future, we will see new computing architectures, like quantum computing and neuromorphic chips, propel capabilities even further. Still, there remains a large gap between aspiration and reality.

Just because something is practical doesn't mean it has to be unfashionable. See how seven disabled people have "pimped up" the equipment they use every day. Viktorija Radvila's custom-made prosthetic leg cover is adorned with sculpted dragons and crystal beads. She describes it as a "Sunday best" item. "I put this on instead of a necklace or rings if I'm going out and I want to look smart," the 34-year-old Lithuanian, who now lives near London, says.

The VC-dimension of a set system is a way to capture its complexity and has been a key parameter studied extensively in machine learning and geometry communities. In this paper, we resolve two longstanding open problems on bounding the VC-dimension of two fundamental set systems: $k$-fold unions/intersections of half-spaces, and the simplices set system. Among other implications, it settles an open question in machine learning that was first studied in the 1989 foundational paper of Blumer, Ehrenfeucht, Haussler and Warmuth as well as by Eisenstat and Angluin and Johnson.