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.

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.

Hollywood actor Morgan Freeman has provided the voice for an AI assistant created by Facebook's Mark Zuckerberg. Mr Zuckerberg said he asked the actor, who was chosen by the public, after an awards ceremony earlier this month. The Facebook co-founder coded the AI assistant - called Jarvis, after the butler in Iron Man - for his home. If he decides to release it to the public, people would relate differently to a famous voice than more robotic sounding assistants, tech experts said. Mr Zuckerberg asked his Facebook followers to pick the voice after building artificial intelligence to help him around the house.

Most tasks in Machine Learning can be reduced to classification tasks. For example, we have a medical dataset and we want to classify who has diabetes (positive class) and who doesn't (negative class). We have a dataset from the financial world and want to know which customers will default on their credit (positive class) and which customers will not (negative class). To do this, we can train a Classifier with a'training dataset' and after such a Classifier is trained (we have determined its model parameters) and can accurately classify the training set, we can use it to classify new data (test set). If the training is done properly, the Classifier should predict the class probabilities of the new data with a similar accuracy.

Terrible user interface and complex designs have plagued enterprise tools for decades. These tools are not only boring and bulky, but most of them require hours of training, onboarding, and whatnot before you can actually start using them. You end up losing crucial time just figuring out the basic workflow. This is 2016, and there ought to be a better way for enterprise businesses to get work done quickly and efficiently. The good news is it looks like bots might be the answer.

In March of 2016, a twenty-seven-year-old Syrian refugee named Rakan Ghebar began discussing his mental health with a counsellor. Ghebar, who has lived in Beirut since 2014, lost a number of family members to the civil war in Syria and struggles with persistent nervous anxiety. Before he fled his native country, he studied English literature at Damascus University; now, in Lebanon, he works as the vice-principal at a school for displaced Syrian children, many of whom suffer from the same difficulties as he does. When Ghebar asked the counsellor for advice, he was told to try to focus intently on the present. By devoting all of his energy to whatever he was doing, the counsellor said, no matter how trivial, he could learn to direct his attention away from his fears and worries.