We've noticed a lot of talk about Artificial Intelligence (AI) among online marketplaces, including eBay (eBay CEO Devin Wenig said AI technology will render the search box redundant), Alibaba (invested in Twiggle), and Amazon (CEO Jeff Bezos said we're at the beginning of a golden age of AI). According to Facebook's detailed post published on Wednesday, June 1, DeepText is a deep-learning artificial intelligence system that is able to understand the content of several thousand posts per second. The system will also be able to filter out malicious, hateful, or hurtful speech on the social network, along with photos that contravene Facebook's policies. Published By: Eunice Gettys on June 5, 2016 08:37 am EST Microsoft Corporation ( NASDAQ:MSFT) announced back in March that Windows 10 had exceeded 300 million active users, calculating from when the operating system was launched in mid-2015, making it the company's most successful operating system ever. Here are five things in technology that happened this past week and how they affect your business.

Driverless trucks will be safer and cheaper than their human-controlled counterparts, but that doesn't mean America's 3.5 million professional truck drivers are giving up to the machines without a fight. Across the US, truckers collectively haul more than 10bn tons of freight each year, but it's a tough job – the hours are long and lonely, the pay is low and the lifestyle is sedentary. "Picture the taxi drivers around the world acting in response to Uber," says Andy Stern, the former former president of the Service Employees International Union, referring to protests and violence that erupted in many cities as the 62.5bn Silicon Valley on-demand ride-hailing firm challenged conventional, regulated taxis. "Truck drivers will follow a similar pattern," says Stern. "There will be disruption in different places. You can imagine people ringing state capitals with their trucks."

Blizzard, the developer behind games like Hearthstone, World of Warcraft and Overwatch, has confirmed it is having issues with its Battle.net Reports of players not being able to log in to Battle.net Blizzard's customer service has since confirmed the problem. We're currently investigating an issue affecting our authentication servers, which may result in failed or slow login attempts. It is unclear if the problem arose because of a technical issue or if it was caused by a malicious attack.

NATIONAL HARBOR, MARYLAND – A new maker of self-driving vehicles burst onto the scene Thursday in partnership with IBM's supercomputer platform Watson, and it is ready to roll right now. Arizona-based startup Local Motors is offering Olli, an electric minibus capable of carrying 12 people. It says the vehicle can be produced to order by 3-D printing. Olli was designed as an on-demand transportation solution that passengers can summon with a mobile app, like Uber rides. And it can be "printed" to specification in "micro factories" in a matter of hours. Olli will be demonstrated in National Harbor, Maryland, over the next few months with additional trials expected in Las Vegas and Miami.

Olli will be exclusive to DC these next few months, but Miami and Las Vegas will get their own in late 2016. Local Motors is also in talks to test the bus in cities outside the US, including Berlin, Copenhagen and Canberra. It's unclear if anyone can get the chance to ride one, since these are merely trial runs, but you can ask local authorities if the EV makes its way to your city. If and when the time comes that driverless public vehicles can legally shuttle passengers, you'll be able to summon an Olli through an app, just like Uber. And if Local Motors' plans pan out, a lot of people around the globe will be using that app: Company co-founder John Rogers envisions building hundreds of micro-factories all over the world that can 3D print an Olli within 10 hours and assemble it one.

The importance of algorithms in our lives today cannot be overstated. They are used virtually everywhere, from financial institutions to dating sites. But some algorithms shape and control our world more than others -- and these ten are the most significant. Just a quick refresher before we get started. Though there's no formal definition, computer scientists describe algorithms as a set of rules that define a sequence of operations.

It has been noticed that some external CVIs exhibit a preferential bias towards a larger or smaller number of clusters which is monotonic (directly or inversely) in the number of clusters in candidate partitions. This type of bias is caused by the functional form of the CVI model. For example, the popular Rand index (RI) exhibits a monotone increasing (NCinc) bias, while the Jaccard Index (JI) index suffers from a monotone decreasing (NCdec) bias. This type of bias has been previously recognized in the literature. In this work, we identify a new type of bias arising from the distribution of the ground truth (reference) partition against which candidate partitions are compared. We call this new type of bias ground truth (GT) bias. This type of bias occurs if a change in the reference partition causes a change in the bias status (e.g., NCinc, NCdec) of a CVI. For example, NCinc bias in the RI can be changed to NCdec bias by skewing the distribution of clusters in the ground truth partition. It is important for users to be aware of this new type of biased behaviour, since it may affect the interpretations of CVI results. The objective of this article is to study the empirical and theoretical implications of GT bias. To the best of our knowledge, this is the first extensive study of such a property for external cluster validity indices.

Researchers recently used an artificial intelligence to run a complex experiment, which it learnt to perform from scratch in under an hour. "A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out," said co-lead researcher Paul Wigley from the Australian National University Research School of Physics and Engineering. This suggests that even physicists are on track to having their jobs augmented if not outright captured by artificial intelligence. The experiment involved the creation of a Bose-Einstein condensate, an extremely cold gas trapped in a laser beam. At a billionth of a degree Kelvin, it is even colder than outer space.

The increasing interest in complex networks research has been a consequence of several intrinsic features of this area, such as the generality of the approach to represent and model virtually any discrete system, and the incorporation of concepts and methods deriving from many areas, from statistical physics to sociology, which are often used in an independent way. Yet, for this same reason, it would be desirable to integrate these various aspects into a more coherent and organic framework, which would imply in several benefits normally allowed by the systematization in science, including the identification of new types of problems and the cross-fertilization between fields. More specifically, the identification of the main areas to which the concepts frequently used in complex networks can be applied paves the way to adopting and applying a larger set of concepts and methods deriving from those respective areas. Among the several areas that have been used in complex networks research, pattern recognition, optimization, linear algebra, and time series analysis seem to play a more basic and recurrent role. In the present manuscript, we propose a systematic way to integrate the concepts from these diverse areas regarding complex networks research. In order to do so, we start by grouping the multidisciplinary concepts into three main groups, namely features, similarity, and network connectivity. Then we show that several of the analysis and modeling approaches to complex networks can be thought as a composition of maps between these three groups, with emphasis on nine main types of mappings, which are presented and illustrated. Such a systematization of principles and approaches also provides an opportunity to review some of the most closely related works in the literature, which is also developed in this article.

Predicting ambulance demand accurately at a fine resolution in time and space (e.g., every hour and 1 km$^2$) is critical for staff / fleet management and dynamic deployment. There are several challenges: though the dataset is typically large-scale, demand per time period and locality is almost always zero. The demand arises from complex urban geography and exhibits complex spatio-temporal patterns, both of which need to captured and exploited. To address these challenges, we propose three methods based on Gaussian mixture models, kernel density estimation, and kernel warping. These methods provide spatio-temporal predictions for Toronto and Melbourne that are significantly more accurate than the current industry practice.