A country's dexterity with artificial intelligence technology might be the next strong source of national pride and international power. Knowing it would lay the foundation for the future of medicine, IBM captured the world's imagination in 2011 with Watson, a supercomputer that not only won Jeopardy!, but beat trivia superstar Ken Jennings in the process. The novel cognitive computing technology was quickly adapted to "read" the thousands of medical research papers published weekly in order to diagnose cancer patients more accurately than human doctors seemingly could. It's a banner technology for IBM, a company that remains no slouch in its 105 years of operation Now five years after Watson's debut, Japanese researchers at Kyoto University and Fujitsu are collaborating to build their own computing technology that's fairly characterized as a response to Watson. Skipping the game shows and going straight to medical applications, the Japanese system aims to close the gap in understanding how our genes determine our health by accounting for a patient's genetic code in its computer-generated diagnoses.

Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems. We propose a method for multi-domain dialogue policy learning---termed NDQN, and apply it to an information-seeking spoken dialogue system in the domains of restaurants and hotels. Experimental results comparing DQN (baseline) versus NDQN (proposed) using simulations report that our proposed method exhibits better scalability and is promising for optimising the behaviour of multi-domain dialogue systems.

Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging modalities that allow in-vivo analysis of a patient's brain network (known as a connectome). Use of these technologies has enabled faster and better diagnoses and treatments of neurological disorders and a deeper understanding of the human brain. Recently, researchers have been exploring the application of machine learning models to connectome data in order to predict clinical outcomes and analyze the importance of subnetworks in the brain. Connectome data has unique properties, which present both special challenges and opportunities when used for machine learning. The purpose of this work is to review the literature on the topic of applying machine learning models to MRI-based connectome data. This field is growing rapidly and now encompasses a large body of research. To summarize the research done to date, we provide a comparative, structured summary of 77 relevant works, tabulated according to different criteria, that represent the majority of the literature on this topic. (We also published a living version of this table online at http://connectomelearning.cs.sfu.ca that the community can continue to contribute to.) After giving an overview of how connectomes are constructed from dMRI and fMRI data, we discuss the variety of machine learning tasks that have been explored with connectome data. We then compare the advantages and drawbacks of different machine learning approaches that have been employed, discussing different feature selection and feature extraction schemes, as well as the learning models and regularization penalties themselves. Throughout this discussion, we focus particularly on how the methods are adapted to the unique nature of graphical connectome data. Finally, we conclude by summarizing the current state of the art and by outlining what we believe are strategic directions for future research.

Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e., pivot features. For simplicity, however, it assumes that the word translation oracle maps each pivot feature in source language to exactly only one word in target language. This one-to-one mapping between words in different languages is too strict. Also the context is not considered at all. In this paper, we propose a cross-lingual SCL based on distributed representation of words; it can learn meaningful one-to-many mappings for pivot words using large amounts of monolingual data and a small dictionary. We conduct experiments on NLP\&CC 2013 cross-lingual sentiment analysis dataset, employing English as source language, and Chinese as target language. Our method does not rely on the parallel corpora and the experimental results show that our approach is more competitive than the state-of-the-art methods in cross-lingual sentiment classification.

Fei-Fei Li is a big deal in the world of AI. As the director of the Artificial Intelligence and Vision labs at Stanford University, she oversaw the creation of ImageNet, a vast database of images designed to accelerate the development of AI that can "see." And, well, it worked, helping to drive the creation of deep learning systems that can recognize objects, animals, people, and even entire scenes in photos--technology that has become commonplace on the world's biggest photo-sharing sites. Now, Fei-Fei will help run a brand new AI group inside Google, a move that reflects just how aggressively the world's biggest tech companies are remaking themselves around this breed of artificial intelligence. Intel Looks to a New Chip to Power the Coming Age of AI Giant Corporations Are Hoarding the World's AI Talent OpenAI Joins Microsoft on the Cloud's Next Big Front: Chips Facebook Manages to Squeeze an AI Into Its Mobile App Giant Corporations Are Hoarding the World's AI Talent Giant Corporations Are Hoarding the World's AI Talent Alongside a former Stanford researcher--Jia Li, who more recently ran research for the social networking service Snapchat--the China-born Fei-Fei will lead a team inside Google's cloud computing operation, building online services that any coder or company can use to build their own AI. This new Cloud Machine Learning Group is the latest example of AI not only re-shaping the technology that Google uses, but also changing how the company organizes and operates its business.

Japan has announced plans to build the world's fastest supercomputer, doing so to help some industries in the nation perform research vital to growth and expansion. According to the nation's Ministry of Economy, Trade and Industry, this supercomputer will cost the equivalent of about $173 million USD; sources go on to claim that it'll be able to perform 130 quadrillion calculations every second with the project set to start as early as 2017. Assuming engineers succeed in hitting the 130 petaflops goal, Japan will outpace China's Sunway Taihulight and officially be home to the world's fastest supercomputer. Once completed, the computer will serve as a research platform for various industries, including medical, robotics, and self-driving automotive industries. The supercomputer would ultimately help the nation regain footing it has lost in an increasingly competitive market.

What is AI? AI is concerned with replicating mechanisms of human intelligence using computers and software. One popular technique involves replicating the brain's neural network (a modelling technique known as'artificial neural networks') to analyse information, extract layers of detail from within it and ultimately attempt to interpret the results. This makes the technology perfect for performing tasks such as analysing language and identifying objects within images. The basic principles have been around since the '60s and were refined in the '90s to allow systems to'learn' based on previous results. In medicine such methods were used to perform tasks such as analysing pap smears.

Internet of @ThingsExpo, taking place June 6-8, 2017at Javits Center, New York City, is co-located with the 20th International @CloudExpo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The Internet of Things (IoT) is the most profound change in personal and enterprise IT since the creation of the Worldwide Web more than 20 years ago. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades. Help plant your flag in the fast-expanding business opportunity that is the Internet of Things: submit your speaking proposal today!

Imagine if your fear of spiders, heights or confined spaces vanished, leaving you with neutral feelings instead of a sweat-soaked panic. A team of neuroscientists said they found a way to recondition the human brain to overcome specific fears. Their approach, if proven in further studies, could lead to new ways of treating patients with phobias or post-traumatic stress disorder (PTSD). The international team published their findings Monday in the journal Nature Human Behaviour. About 19 million U.S. adults, or 8.7 percent of the adult population, suffer prominent and persistent fears at the sight of specific objects or in specific situations, according to the National Institute of Mental Health.

The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas. Google has been ramping up their Cloud Platform quite aggressively in recent months. Just a few weeks ago, the Google Cloud Platform opened its newest zone in Tokyo, increasing the total number of regions they are present in to six - three in the US and one each in Belgium and Taiwan and Tokyo. Not long ago, the company announced its acquisition of Orbitera, a cloud commerce company. The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas.