This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by FCT (INESC-ID multiannual funding) through the PIDDAC Program funds and under project PEst-OE/EEI/LA0021/2011 and the FP7 Cooperation Work Programme: Food, Agriculture and Fisheries, and Biotechnologies, KBBE-227258 (BIOHYPO project). Quotient Bioresearch received part-funding from the European Union in the scope of BIOHYPO project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: During the elaboration of this manuscript, Ian Morrissey and Daniel Knight were employed by Quotient Bioresearch and belonged to the BIOHYPO European project.

What if your security camera could not only see who's at your door, but also identify whether it's a guest you're expecting, alert you when they arrive, and let them in? Or how about a speaker system that automatically plays music as your child wakes up? That's the type of functionality Facebook CEO Mark Zuckerberg (fb) is trying to build into his virtual butler, Jarvis, which he's been developing throughout the year as part of his New Year's resolution. With 2016 coming to a close, Zuckerberg published a lengthy blog post detailing the types of tasks Jarvis can accomplish. He also wrote about the biggest challenges that he faced when developing his artificial intelligence (AI) software, and where he believes AI is heading.

The same can be said about the world of science, which witnessed some of the biggest breakthroughs in decades, even as it provided several grim reminders about the impact of climate change on planet Earth. One hundred years ago, Albert Einstein predicted that the collision of massive objects such as black holes and neutron stars can create "ripples" in the curvature of space-time. Earlier this year, scientists associated with the Laser Interferometer Gravitational-Wave Observatory (LIGO) discovered these distortions. "The achievement fulfilled a 100-year-old prediction, opened up a potential new branch of astronomy, and was a stunning technological accomplishment," the journal Science, which was one of the many publications that termed the discovery of gravitational waves "Breakthrough of the Year," said in a recent statement. Currently, all we know about the cosmos is what we have gathered from electromagnetic radiation such as radio waves, visible light, infrared light, X-rays and gamma rays.

This market is expected to be worth USD 1,7 million by 2022, growing at a CAGR of 65.3 percent between 2016 and 2022, according to a new reportnew report from Research and Markets. Deep learning is a branch of machine learning that attempts to model high level abstractions in data that is used in artificial intelligence programs. The major driver for the growth is the growing usage of deep learning technology across various industrial vertical such as advertisement, finance, automotive, medical and among other. Other important drivers for this market is the robust R&D for the development of better processing hardware for deep learning. Of all the major end-user industries, the medical industry has highest growth opportunity.

Deep learning has continued to drive the computing industry's agenda in 2016. But come 2017, experts say the Artificial Intelligence community will intensify its demand for higher performance and more power efficient "inference" engines for deep neural networks. The current deep learning system leverages advances in large computation power to define network, big data sets for training, and access to the large computing system to accomplish its goal. Unfortunately, the efficient execution of this learning is not so easy on embedded systems (i.e. This problem leaves wide open the possibility for innovation of technologies that can put deep neural network power into end devices. "Deploying Artificial Intelligence at the edge [of the network] is becoming a massive trend," Movidius CEO, Remi El-Ouazzane, told us a few months ago.

Or carmakers could utilise the technology to make autonomous cars safer and more reactive to their immediate environment. Cremers' trailblazing research into mathematical image pro-cessing and pattern recognition earned him the 2016 Gottfried Wilhelm Leibniz Prize – Germany's most esteemed award in the sciences. His question: How can we use a camera to capture and "recover" the 3D world and reconstruct it in real time? It might lie in something called "Direct Image Alignment," which is a core component of his current research into realising the 3D world in images – faster, with greater accuracy and with more robustness.

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a is our selection for the picture of the week. How to build a search engine - Part 2: Configuring elasticsearch Generative Adversarial Networks Explained in Layman Terms Curriculum Guidelines for Undergraduate Programs in Data Science The Perceptron Algorithm explained with Python code Great list of resources: data science, visualization, machine learn... Great list of resources: data science, visualization, machine learn... ALDI – New Paradigm for Integrating Marketing Analytics with Data S... Want to know how to choose Machine Learning algorithm? Quantifying Probabilities for Gambling System Strategies An Intro to Predictive Analytics: Can I predict the future?

Traditional broadcast TV services have stagnated over the past couple of years, while over-the-top services such as Amazon Video, Netflix and the BBC's iPlayer led the way. Sky's Q dragged broadcast TV kicking and screaming into the 21st century with a modern interface, fast box and service that put time and place shifting at the heart of it. It records, it downloads, it supports 4K and can spit video around your house via Q Mini boxes or the Q app on smartphones and tablets using your home network. Sky Q was pretty expensive at launch, but now is available from £20 per month. Bluetooth headphones are almost mainstream.

Type 1 diabetes (T1D) is a metabolic disease characterised by uncontrolled blood glucose levels, due to the absence or malfunction of insulin. The Artificial Pancreas (AP) system aims to simulate the function of the physiological pancreas and serve as an external automatic glucose regulation system. AP combines a continuous glucose monitor (CGM), a continuous subcutaneous insulin infusion (CSII) pump and a control algorithm which closes the loop between the two devices and optimises the insulin infusion rate. An important challenge in the design of efficient control algorithms for AP is the use of the subcutaneous route both for glucose measurement and insulin infusion (sc-sc route); this introduces delays of up to 30 minutes for sc glucose measurement and up to 20 minutes for insulin absorption. Thus, a total delay of almost one hour restricts both monitoring and intervention in real time. Moreover, glucose is affected by multiple factors, which may be genetic, lifestyle and environmental. With the improvement in sensor technology, more information can be provided to the control algorithm (e.g. more accurate glucose readings and physical activity levels); however, the level of uncertainty remains very high. Last but not least, one of the most important challenges emerges from the high inter- and intra-patient variability, which dictate personalised insulin treatment. Along with hardware improvements, the challenges of the AP are gradually being addressed with the development of advanced algorithmic strategies; the strategies most investigated clinically are the Proportional Integral Derivative (PID) [1], the Model Predictive Controller (MPC) [2]-[7] and fuzzy logic (e.g.

IDC predicts that 35 percent of leading organizations in logistics, health, utilities, and resources will explore the use of robots to automate operations by 2019. What does the future hold for robotics? It's hard to say, given the rapid pace of change in the field as well as in associated areas such as machine learning and artificial intelligence. But one thing seems certain: Robots will play an increasingly important role in business and life in general. Research firm International Data Corp's (IDC) Manufacturing Insights Worldwide Commercial Robotics program recently unveiled its top 10 predictions for worldwide robotics for 2017 and beyond.