Classical machine learning algorithms are currently used for performing complex computational tasks, such as pattern recognition or classification in large amounts of data, and constitute a crucial part of many modern technologies. The aim of quantum learning algorithms is to bring these features into scenarios where information is in a fully quantum form. The scientists, Alex Monràs at the Autonomous University of Barcelona, Spain; Gael Sentís at the University of the Basque Country, Spain, and the University of Siegen, Germany; and Peter Wittek at ICFO-The Institute of Photonic Science, Spain, and the University of Borås, Sweden, have published a paper on their results in a recent issue of Physical Review Letters. "Our work unveils the structure of a general class of quantum learning algorithms at a very fundamental level," Sentís told Phys.org. "It shows that the potentially very complex operations involved in an optimal quantum setup can be dropped in favor of a much simpler operational scheme, which is analogous to the one used in classical algorithms, and no performance is lost in the process. This finding helps in establishing the ultimate capabilities of quantum learning algorithms, and opens the door to applying key results in statistical learning to quantum scenarios."

Artificial intelligence is a topic that's been discussed for decades, but it's an industry still very much in its infancy - we're only seeing the beginning of its capabilities. There are areas where AI has become heavily relied upon - such as algorithmic trading - but, in general, the broad adoption of the technology is still marginal. As an industry, it's a toddler you could say, but we're at a point in time where we can expect to see it grow up - and fast. There have been notable achievements and breakthroughs throughout the years which we can look at to get a better understanding of where AI is at today. First came expert systems that were adopted in the 70s and 80s for use in our cars, PCs, and other forms of manufacturing, but which failed dramatically when applied to fields such as healthcare, so hit a barrier in terms of their exponential adoption.

While they might currently be seen as rivals, Amazon and Apple's smart assistants could soon work together, according to a senior executive from Amazon. He claims that future versions of the Amazon Echo device will support rival software. This could mean that users could choose whether to use Amazon's Alexa assistant on the device, or other assistants, like Siri. Amazon's senior vice president of devices at David Limp (pictured) told audiences at the Wired Business Conference held yesterday in New York that future versions of the Echo smart speaker could support rival software like Apple's Siri Apple unveiled its new $349 (£270) smart HomePod home speaker at its 2017 Worldwide Developers Conference on Monday. Amazon and Google's existing speakers have also proved popular with consumers since the Echo launched in the UK in 2016 and Home earlier this year.

Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems (e.g. inpainting of missing pixels or zooming). Secondly it can deal with signal dependent noise models, particularly suited to digital cameras. As such, the scheme is especially adapted to computational photography. In order to illustrate this point, we provide an application to high dynamic range imaging from a single image taken with a modified sensor, which shows the effectiveness of the proposed scheme.

Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning. As a step in this direction, we propose a new architecture, called neural equivalence networks, for the problem of learning continuous semantic representations of algebraic and logical expressions. These networks are trained to represent semantic equivalence, even of expressions that are syntactically very different. The challenge is that semantic representations must be computed in a syntax-directed manner, because semantics is compositional, but at the same time, small changes in syntax can lead to very large changes in semantics, which can be difficult for continuous neural architectures. We perform an exhaustive evaluation on the task of checking equivalence on a highly diverse class of symbolic algebraic and boolean expression types, showing that our model significantly outperforms existing architectures.

The world's most pampered rhino is having his mudpack applied by hand. Sudan the veteran northern white rhinoceros stands placidly and allows his personal assistant to rub dollops of wet clay into his hide, to moisturise his skin and keep insects away. 'He loves that,' says Zach, his 24-hour-a-day PA. Zach lives next door to the rhino enclosure, permanently on call in case His Lordship should require anything. And Sudan is very high maintenance – he used to have a man whose job was rubbing oil into his hooves.

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. We've written about extra robot arms for humans in the past, but these are more complicated and perhaps capable than most: I'm not completely sold on the control system here, since it essentially means you're trading the use of your legs for the use of some extra arms. If this isn't enough reason to get a RoboThespian, I don't know what is: ROBOTIS was running this demo at ICRA as well; the first TurtleBot is using its laser for person-detection and following, while the other TurtleBots are wirelessly following the first.

As every scrap of James Comey's testimony before the Senate Intelligence Committee is pored over and picked apart, one word gleams brighter than any other. It is a common word, employable as both a noun and a verb, and it boasts an extraordinary breadth. We may say, "I hope to catch the 6:42 A.M.," or "I hope the kids don't catch a cold," and, at the other end of the spectrum, Christians are exhorted to pray "for all who have died in the hope of the Resurrection." So where do the hopes that Comey cited yesterday, in his own utterances and in his reports of others' speech, belong? First, we have his homely dictum, which is already destined to wind up on a thousand T-shirts, or in the chorus of a country ballad: "Lordy, I hope there are tapes."

But staying relevant is not just a matter of innovating or achieving operational excellence. It's also about transforming the business and the corporate models it is based on in order to become a real workplace of the future. I believe that Human Resources (HR) professionals have a big role to play in that transformation process. HR will be the pioneering force in creating the workplace of the future. To accomplish this, it will essential to leverage new technologies, data management, and other innovations.

AI (Artificial Intelligence) has been a hot topic in 2017, especially when looking at the future of Artificial Intelligence in Social Media. Investment has been growing in AI, and this is expected to grow by around 300% throughout the rest of this year. A third of the world's population is using Social Media, and AI is playing a huge part in how businesses are communicating with potential prospects online. This is very exciting for me, as I have seen the evolution of Social Media all the way from the Myspace/Bebo days, all the way to Facebook, Twitter, Instagram, Pinterest, Google and now SnapChat. AI is not going anywhere and in fact, it is going to become more widely used by businesses, which is why I wanted to write this article.