SQL Server 2017 Machine Learning Services is an add-on to a database engine instance, used for executing R and Python code on SQL Server. The code runs in an extensibility framework, isolated from core engine processes, but fully available to relational data as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL. If you previously used SQL Server 2016 R Services, Machine Learning Services in SQL Server 2017 is the next generation of R support, with updated versions of base R, RevoScaleR, MicrosoftML, and other libraries introduced in 2016. The key value proposition of Machine Learning Services is the power of its enterprise R and Python packages to deliver advanced analytics at scale, and the ability to bring calculations and processing to where the data resides, eliminating the need to pull data across the network. SQL Server 2017 supports R and Python.

Tom was discussing the film star Tang Wei with a chatbot named XiaoIce, and the bot was excited: "A goddess! She stole my heart … and then went off and married!" Married who? "Haven't you heard?" XiaoIce replied. "Tang Wei is engaged to famous Korean director Kim Tae-yong." XiaoIce is a massive hit on social networks in Asia. Introduced in 2014 by Microsoft Research and Bing in Beijing, it can answer simple questions, like a stripped-down version of Cortana.

Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a given system such that the system makes recommendations as the attacker desires. However, these poisoning attacks are either agnostic to recommendation algorithms or optimized to recommender systems that are not graph-based. Like association-rule-based and matrix-factorization-based recommender systems, graph-based recommender system is also deployed in practice, e.g., eBay, Huawei App Store. However, how to design optimized poisoning attacks for graph-based recommender systems is still an open problem. In this work, we perform a systematic study on poisoning attacks to graph-based recommender systems. Due to limited resources and to avoid detection, we assume the number of fake users that can be injected into the system is bounded. The key challenge is how to assign rating scores to the fake users such that the target item is recommended to as many normal users as possible. To address the challenge, we formulate the poisoning attacks as an optimization problem, solving which determines the rating scores for the fake users. We also propose techniques to solve the optimization problem. We evaluate our attacks and compare them with existing attacks under white-box (recommendation algorithm and its parameters are known), gray-box (recommendation algorithm is known but its parameters are unknown), and black-box (recommendation algorithm is unknown) settings using two real-world datasets. Our results show that our attack is effective and outperforms existing attacks for graph-based recommender systems. For instance, when 1% fake users are injected, our attack can make a target item recommended to 580 times more normal users in certain scenarios.

"AI is one of the most important things humanity is working on. It is more profound than electricity or fire…We have learned to harness fire for the benefits of humanity but we had to overcome its downsides too.….AI is really important, but we have to be concerned about it." While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google's search algorithms to IBM's Watson, to autonomous weapons. Everyone is excited about AI, and everyone has a view on AI. AI is no longer the preserve of an Alex Garland screenplay, it's a reality and being used in multiple ways.

Sometime in the late 1980s, I participated in a meeting organized by the Honda Foundation in which a Japanese professor--I can't remember his name--made the case that the Japanese had more success integrating robots into society because of their country's indigenous Shinto religion, which remains the official national religion of Japan. Followers of Shinto, unlike Judeo-Christian monotheists and the Greeks before them, do not believe that humans are particularly "special." Instead, there are spirits in everything, rather like the Force in Star Wars. Nature doesn't belong to us, we belong to Nature, and spirits live in everything, including rocks, tools, homes, and even empty spaces. The West, the professor contended, has a problem with the idea of things having spirits and feels that anthropomorphism, the attribution of human-like attributes to things or animals, is childish, primitive, or even bad.

The trio of phones will boast other features, such as facial-recognition technology, but their display size stands out--their average screen area, without accounting for a facial-recognition system that juts into the top of the display, is 23% larger than last year's three new phones and 28% bigger than the two models unveiled in 2016. Note: Screens on iPhone X and newer have facial-recognition systems that cut into the display. At a time when people are buying fewer new phones, bigger size brings two advantages. It helps Apple buoy prices and profit margins because it can sell larger phones at a greater markup than it pays suppliers for the larger screens. And it encourages people to use their phones more, helping momentum of Apple's services business, which includes app-store sales and subscriptions to video services like Netflix and HBO.

Big Data has been a focus for DG Competition for the last few years. In particular, the Commission has been interested in mergers involving the acquisition of a company holding valuable data, even if it has low turnover (see here). Apple's $400 million acquisition of Shazam, approved by the Commission on 6 September 2018, falls squarely within this category. Shazam is a music recognition app. Consumers can use Shazam to record a clip of an unknown song playing in a bar or other public place – Shazam turns this into an audio fingerprint and matches this against its database containing the audio fingerprints for millions of songs in order to identify the song playing.

In the documentary Swiped, filmmaker Nancy Jo Sales investigates how dating apps have created unintended consequences in actual relationships. In the documentary Swiped, filmmaker Nancy Jo Sales investigates how dating apps have created unintended consequences in actual relationships. For some of the 40 million or so Americans who currently use online dating apps like Tinder, Bumble and Hinge, the findings of the new HBO documentary Swiped might be intuitively obvious. But for others, there may still be revelations aplenty in the film, which is subtitled Hooking Up in the Digital Age. It's about how these apps may change how we think about relationships -- and it doesn't paint a positive picture.

Yuval Noah Harari, author of the best-selling books Sapiens and Homo Deus, is a big fan of science fiction, and includes an entire chapter about it in his new book 21 Lessons for the 21st Century. "Today science fiction is the most important artistic genre," Harari says in Episode 325 of the Geek's Guide to the Galaxy podcast. "It shapes the understanding of the public on things like artificial intelligence and biotechnology, which are likely to change our lives and society more than anything else in the coming decades." Because science fiction plays such a key role in shaping public opinion, he would like to see more science fiction that grapples with realistic issues like AI creating a permanent'useless class' of workers. "If you want to raise public awareness of such issues, a good science fiction movie could be worth not one, but a hundred articles in Science or Nature, or even a hundred articles in the New York Times," he says.

It may come as little surprise to learn that musicians perceive sounds differently than the average person. But according to a new study, there are striking differences among the artists themselves, too. MRI scans on the brains of guitarists and beatboxers have revealed that their responses to hearing unfamiliar songs differ based on their craft; while the'hand area' of a guitarist's brain lights up, it's the'mouth area' that activates for beatboxers. For beatboxers, the music spurred activity in the region that controls mouth movements. For guitarists, hearing a guitar track led to activity in an area responsible for hand movement.