We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model's ability to selectively focus on the relevant parts of an input sequence.

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Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence--we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. At the same time, the hype around machine intelligence methods continues to grow: the words "deep learning" now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like "big data" (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one.

What are tech titans Google, Amazon and Uber agitating for to further the march of machine learning technology and ultimately inject more fuel in the engines of their own dominant platforms? Specifically, they're pushing for free and liberal access to publicly funded data -- urging that this type of data continue to be "open by default," and structured in a way that supports "wider use of research data." After all, why pay to acquire data when there are vast troves of publicly funded information ripe to be squeezed for fresh economic gain? Other items on this machine learning advancement wish-list include new open standards for data (including metadata); research study design that has the "broadest consents that are ethically possible," and a stated desire to rethink the notion of "consent" as a core plank of good data governance -- to grease the pipe in favor of data access and make data holdings "fit for purpose" in the AI age. These suggestions come in a 125-page report published today by the Royal Society, aka the U.K.'s national academy of science, ostensibly aimed at fostering an environment where machine learning technology can flourish in order to unlock mooted productivity gains and economic benefits -- albeit the question of who, ultimately, benefits as more and more data gets squeezed to give up its precious insights is the overarching theme and unanswered question here.

IMAGE: (a) Posteroanterior chest radiograph shows upper lobe opacities with pathologic analysis-proven active TB. OAK BROOK, Ill. - Researchers are training artificial intelligence models to identify tuberculosis (TB) on chest X-rays, which may help screening and evaluation efforts in TB-prevalent areas with limited access to radiologists, according to a new study appearing online in the journal Radiology. According to the World Health Organization, TB is one of the top 10 causes of death worldwide. In 2016, approximately 10.4 million people fell ill from TB, resulting in 1.8 million deaths. TB can be identified on chest imaging, however TB-prevalent areas typically lack the radiology interpretation expertise needed to screen and diagnose the disease. "There is a tremendous interest in artificial intelligence, both inside and outside the field of medicine," said study co-author Paras Lakhani, M.D., from Thomas Jefferson University Hospital (TJUH) in Philadelphia.

Samsung has now admitted that its Galaxy S8's facial recognition technology is not fit for carrying out mobile payments. The feature is said to be not as secure as fingerprint and iris recognition, and it would take time before it could be used in authenticating Samsung Pay transactions. On Tuesday, someone from Samsung told The Investor that the facial scanning technology of the Galaxy S8 and S8 Plus is not secure enough for mobile payments. The same source stated that the South Korea giant needs more than four years before the feature could be ready to handle mobile payment transactions. "In order for facial recognition to be solely used for financial transactions, it would take more than four years considering the current camera and deep learning technology levels," the source was quoted as saying.

Mike Abbott, Partner of Kleiner Perkins Caufield & Byers, sits with Dr. Fei Fei Li, Associate Professor in the Computer Science Department at Stanford, and the Director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab. Dr Li's main research areas are in machine learning, deep learning, computer vision and cognitive and computational neuroscience. She has published more than 150 scientific articles in top-tier journals and conferences and invented ImageNet and the ImageNet Challenge, a critical large-scale dataset and benchmarking effort that has contributed to the latest developments in deep learning and AI. As of January 2017, Fei-Fei Li is spending her sabbatical at Google Cloud as Chief Scientist AI/ML.

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There is tremendous interest in artificial intelligence this year, which is ironic since the technology is more than 60 years old. In fact, artificial intelligence actually encompasses a few different technologies, each of which can help an organization better understand and engage with customers. Forrester Research Senior Analyst Brandon Purcell has authored two reports on the current strong adoption of artificial intelligence. In part one of Information Management's interviews with Purcell, we discuss "The Top Emerging Technologies in Artificial Intelligence." Part two will discuss the report "Artificial Intelligence Technologies and Solutions, Q1 2017."

Investigation of artificial intelligence is as ancient as computers themselves. Much of the present eagerness concerns a subfield of it termed "deep learning", a contemporary modification of "machine learning" in which processors teach themselves errands by crunching huge sets of data. When it comes to paddling through complex mathematical calculations, the simplest processor can run rings around the brightest individual. At the same time, the most prevailing processors have, in the past, writhed with things that individuals find insignificant, such as identifying faces, deciphering speech and classifying objects in pictures. One way of comprehending this is that for individuals to do things they find problematic, such as resolving differential calculations, they have to compose a set of prescribed rules.