Multi-Context Systems (MCS) are a powerful framework for interlinking possibly heterogeneous, autonomous knowledge bases, where information can be exchanged among knowledge bases by designated bridge rules with negation as failure. An acknowledged issue with MCS is inconsistency that arises due to the information exchange. To remedy this problem, inconsistency removal has been proposed in terms of repairs, which modify bridge rules based on suitable notions for diagnosis of inconsistency. In general, multiple diagnoses and repairs do exist; this leaves the user, who arguably may oversee the inconsistency removal, with the task of selecting some repair among all possible ones. To aid in this regard, we extend the MCS framework with preference information for diagnoses, such that undesired diagnoses are filtered out and diagnoses that are most preferred according to a preference ordering are selected. We consider preference information at a generic level and develop meta-reasoning techniques on diagnoses in MCS that can be exploited to reduce preference-based selection of diagnoses to computing ordinary subset-minimal diagnoses in an extended MCS. We describe two meta-reasoning encodings for preference orders: the first is conceptually simple but may incur an exponential blowup. The second is increasing only linearly in size and based on duplicating the original MCS. The latter requires nondeterministic guessing if a subset-minimal among all most preferred diagnoses should be computed. However, a complexity analysis of diagnoses shows that this is worst-case optimal, and that in general, preferred diagnoses have the same complexity as subset-minimal ordinary diagnoses. Furthermore, (subset-minimal) filtered diagnoses and (subset-minimal) ordinary diagnoses also have the same complexity.

The tech titan has moved away from relying on country-specific domains to serve up localized results on mobile web, the Google app for iOS, as well as Search and Maps for desktop. Now, your location dictates the kind of results you'll get -- you could go to google.com.au, for instance, but if you're in New Zealand, you'll still get search results tailored for your current whereabouts. You'll know the location Google recognizes by looking at the lower left-hand corner of the page, as you can see above. Google will automatically detect if you go to another country and serve you results for your new location. So, you'll get results tailored for Japan if you go there, but Google will seamlessly transition back to United States when you fly back home.

We hear a lot these days about how AI is changing the marketing technology landscape, helping us sell products and services in the 21st century. But during my two-week journey through Israel's startup scene, it was a medtech company that struck a nerve with me -- a deep learning imaging analytics startup called Zebra Medical Vision. Using AI to recommend related products, build a million split tests, or determine the optimal time to push a discount voucher may be effective from a marketing standpoint, but these applications are hardly life-changing. And they wouldn't have helped save my dad's life. He passed away from lung cancer in February this year, and there is a good chance that an early and accurate diagnosis could have given him a better chance at beating "the big C." Zebra's technology is making it possible to catch misdiagnosed diseases, early-stage cancers, and other life-threatening ailments, and the company today announced a significant change in its business model that makes its AI-powered medical scan recognition for hospitals more affordable.

What is needed to drive mass adoption of smart homes is integrated technology, supported by machine learning tailored to individual behaviour, that provides solutions for real consumer issues. One of the biggest barriers to smart home adoption is the unnatural environment connected devices create. AI removes this barrier by adding an extra layer of intelligence to the home. Connected devices do have a role to play in truly smart homes though. The embedded sensors on each device collect thousands upon thousands of data points ranging from when it was turned on, how long it was used for and its electricity usage for that period.

The writing has been on the wall for years, at least since Microsoft de-bundled the motion-tracking system from the Xbox One in 2014, knocking $100 off the price tag and making the system more competitive with the PlayStation 4. The Kinect debuted in 2010 with the Xbox 360, and it had a good run, overall: Microsoft sold roughly 35 million devices in total. However, across its iterations and upgrades, the Kinect never quite found its market -- the one application that would turn the hardware into an essential piece of home technology. It wasn't a conversational, connected, voice-activated system like Google Home or Amazon Alexa, and game developers lost interest in the device as virtual and mixed reality rose to the fore. The Kinect was a product out of time. That's not to say it didn't contribute to some truly wild experiences over the years. Developers quickly applied Kinect to surgery, physical therapy and a range of other medical uses.

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. A new RoboBee from Harvard can swim underwater, and then launch itself into the air with a microrocket and fly away. At the millimeter scale, the water's surface might as well be a brick wall.

Humans are always creating machines to do our work. Machines replace work we find tedious or backbreaking. They allow us to do things faster, to make a thousand parts an hour, or travel a hundred miles an hour. For thousands of years, the tasks that machines replaced were primarily physical. With the advent of computers, tasks requiring mental effort became more tractable.

A survey of 500 CIOs from around the world by ServiceNow finds that machine learning has arrived in the enterprise, making material contributions to everyday work. To realize its full value, technology leaders must find skilled talent to work side-by-side with machines, in addition to redesigning their organizations and processes. Go from Pro to Superhero! Our Amazing, Incredible, Invincible PR Strategy Checklist is jam-packed with wisdom and resources to keep you saving the day! For The Global CIO Point of View, ServiceNow surveyed CIOs in 11 countries across 25 industries to uncover the competitive benefits of adopting machine learning and hear how those leaders are driving results.

According to a survey from SLI Systems, 54 percent of mid-size retailers are using or plan to use artificial intelligence (AI) as an online tool in the next 12 months. The most popular applications are expected to be personalized product recommendations, customer service requests and chatbots. The online survey of 234 e-commerce professionals primarily in the U.S., Europe, Australia and New Zealand showed that 56 percent are either using or planning to use AI for personalized product recommendations. That was followed by customer service requests, 41 percent; chatbots, 35 percent; and visual search, 32 percent. VR/AR, voice-activated apps and virtual buying assistants scored lower.In a note from late September, according to Barron's, R.W. Baird's Colin Sebastian indicated that the "overriding theme" at the Shop.org Noting that AI chatter had risen significantly at e-commerce conferences over the last two or three years, Mr. Sebastion said the message being relayed was that "machines will learn about and communicate with individual customers."

The first instances to include NVIDIA Tesla V100 GPUs, P3 instances are the most powerful GPU instances available in the cloud. P3 instances allow customers to build and deploy advanced applications with up to 14 times better performance than previous-generation Amazon EC2 GPU compute instances, and reduce training of machine learning applications from days to hours. With up to eight NVIDIA Tesla V100 GPUs, P3 instances provide up to one petaflop of mixed-precision, 125 teraflops of single-precision, and 62 teraflops of double-precision floating point performance, as well as a 300 GB/s second-generation NVIDIA NVLink interconnect that enables high-speed, low-latency GPU-to-GPU communication. P3 instances also feature up to 64 vCPUs based on custom Intel Xeon E5 (Broadwell) processors, 488 GB of DRAM, and 25 Gbps of dedicated aggregate network bandwidth using the Elastic Network Adapter (ENA). "When we launched our P2 instances last year, we couldn't believe how quickly people adopted them," said Matt Garman, Vice President of Amazon EC2.