Growing usage of big data in healthcare industry and imbalance between health workforce and patients is expected to drive the growth of the AI in healthcare market The artificial intelligence (AI) in healthcare market was valued at USD 667.1 million in 2016 and is expected to reach USD 7,988.8 million by 2022, at a CAGR of 52.68% between 2017 and 2022. The growth of this market is driven by the growing usage of Big Data in healthcare industry, ability of AI to improve patient outcomes, imbalance between health workforce and patients, reducing the healthcare costs, growing importance on precision medicine, cross-industry partnerships, and significant increase in venture capital investments in AI in healthcare domain. However, reluctance among medical practitioners to adopt AI-based technologies and ambiguous regulatory guidelines for medical software are the major factors restraining the growth of the AI in healthcare market. Faster calculations and lesser power consumption are the factors driving the growth of the hardware market for AI in healthcare Hardware which includes GPUs, DSPs, FPGAs, and neuromorphic chips is expected to grow at the highest rate in the offering segment of AI in healthcare. The GPU, DSP, and FPGA are widely used to implement the deep learning algorithm.

It was not long ago that Artificial Intelligence (AI) was only in the realm of science fiction. Today, it has become a reality and is only growing more prominent in many different industries every day. This includes the internet as AI in search engine technology has been around for a few years. The algorithms used to rank pages have been affected considerably by AI already and that trend will continue into the foreseeable future. Currently, Google's RankBrain, an AI process used help set search engine rankings, is having a major impact which is only expected to expand.

Accountability, responsibility, privacy and security are all key when considering ethical dimensions of this emerging field. If a semi-autonomous robot did not have a reliable control or override mechanism, a person might be considered negligent if they used it to pick up a baby, but not for other less risky activities. The authors propose that any semi-autonomous system should include a form of veto control -- an emergency stop -- to help overcome some of the inherent weaknesses of direct brain-machine interaction. Professor John Donoghue, Director of the Wyss Center for Bio and Neuroengineering in Geneva, Switzerland said: "Although we still don't fully understand how the brain works, we are moving closer to being able to reliably decode certain brain signals. We shouldn't be complacent about what this could mean for society. We must carefully consider the consequences of living alongside semi-intelligent brain-controlled machines and we should be ready with mechanisms to ensure their safe and ethical use." "We don't want to overstate the risks nor build false hope for those who could benefit from neurotechnology. Our aim is to ensure that appropriate legislation keeps pace with this rapidly progressing field."

Machine-learning has predicted death risk in people with serious heart disease faster and more accurately than current methods. New software, developed by scientists at Imperial College London, has created virtual 3D hearts of each patient that replicate the way the organ contracts with each beat. Artificial intelligence is able to rapidly learn which features of cardiac function best predict heart failure and death. The system uses magnetic resonance imaging (MRI) of the heart together with information from blood tests and other observations. The technology has been tested on patients with pulmonary hypertension, a condition that leads to heart failure if not treated appropriately.

The 4th industrial revolution is here, and it is completely transforming the way we live and work. This new world is fuelled by data and internet connected devices that are capable of collecting and processing ever-growing amounts of information. Smartphones, digital cameras, sensors and social media now create more information than ever before. In fact, over the past 18 months we have created more data than in all of prior human history combined. More hours of YouTube videos are now uploaded every three minutes than all Hollywood studios produce in a whole year.

The upcoming Wimbledon championships mark the last before All England Lawn and Tennis Club (AELTC) takes responsibility from the BBC as the tournament's lead broadcaster, a shift that has seen it double-down on content production and experiment with new technologies, including artificial intelligence (AI). The AELTC has been flexing its digital muscles for some time now, working with IBM over the past two years to overhaul its data-capabilities, website and apps, and forge tech partnerships in the hopes of shedding its self-described "stuffy" image. As it stands, Wimbledon trebled its mobile audience last year, while its app was downloaded 1.5 million times in 2016. Now, as it faces a future with greater control of its content output it's testing the waters on how technology might help its still-limited team scale, particularly on video. With an average of three matches per court per day there is hundreds of hours of footage an editor might have to sift through for a highlights reel.

Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation. Two dual tasks have intrinsic connections with each other due to the probabilistic correlation between their models. This connection is, however, not effectively utilized today, since people usually train the models of two dual tasks separately and independently. In this work, we propose training the models of two dual tasks simultaneously, and explicitly exploiting the probabilistic correlation between them to regularize the training process. For ease of reference, we call the proposed approach \emph{dual supervised learning}. We demonstrate that dual supervised learning can improve the practical performances of both tasks, for various applications including machine translation, image processing, and sentiment analysis.

Automation and computer intelligence to support complex human decisions becomes essential to manage large and distributed systems in the Cloud and IoT era. Understanding the root cause of an observed symptom in a complex system has been a major problem for decades. As industry dives into the IoT world and the amount of data generated per year grows at an amazing speed, an important question is how to find appropriate mechanisms to determine root causes that can handle huge amounts of data or may provide valuable feedback in real-time. While many survey papers aim at summarizing the landscape of techniques for modelling system behavior and infering the root cause of a problem based in the resulting models, none of those focuses on analyzing how the different techniques in the literature fit growing requirements in terms of performance and scalability. In this survey, we provide a review of root-cause analysis, focusing on these particular aspects. We also provide guidance to choose the best root-cause analysis strategy depending on the requirements of a particular system and application.

Creativity is one of the great challenges for machine intelligence. There is no shortage of evidence showing how machines can match and even outperform humans in vast areas of endeavor, such as face and object recognition, doodling, image synthesis, language translation, a vast variety of games such as chess and Go, and so on. But when it comes to creativity, the machines lag well behind. For example, machines have learned to recognize artistic style, separate it from the content of an image, and then apply it to other images. That makes it possible to convert any photograph into the style of Van Gogh's Starry Night, for instance.

One of Unity Labs' missions is identifying and supporting cutting edge research. For 2017, we have identified a slate of research topics we are currently working on in the areas not limited to VR and AR authoring tools, Game AI, and Graphics. It's our vision to advance the next generation of 3D interactive entertainment content authoring. Over the past weeks, we've joined forces with Unity's AI & Machine Learning Group to identify and support graduate researchers specifically working on research challenges in Machine Learning for games. The current perception of Machine Learning in the games industry is surrounding the potential that learning has to offer the gaming world.