We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the pediatric intensive care unit (PICU) at Children's Hospital Los Angeles, our data consists of multivariate time series of observations. The measurements are irregularly spaced, leading to missingness patterns in temporally discretized sequences. While these artifacts are typically handled by imputation, we achieve superior predictive performance by treating the artifacts as features. Unlike linear models, recurrent neural networks can realize this improvement using only simple binary indicators of missingness. For linear models, we show an alternative strategy to capture this signal. Training models on missingness patterns only, we show that for some diseases, what tests are run can be as predictive as the results themselves.

Graphics chipmaker Nvidia blew past its third quarter earnings targets Thursday after the bell. The company posted record-revenue for the quarter, thanks to strong sales of Nvidia's new Pascal GPUs. Nvidia co-founder and CEO Jen-Hsun Huang said the GPUs are fully ramped and rolling out in gaming, VR, self-driving cars and datacenter AI computing applications. "We have invested years of work and billions of dollars to advance deep learning. Our GPU deep learning platform runs every AI framework, and is available in cloud services from Amazon, IBM, Microsoft and Alibaba, and in servers from every OEM. GPU deep learning has sparked a wave of innovations that will usher in the next era of computing," he said.

Google announced a new machine learning platform for developers. The company is also open-sourcing tools such as Tensorflow to allow the community to take its internal tools, adapt them for their own uses, and improve them.

His contention made sense: If marketers are turning out exponentially-increasing amounts of content, yet are competing for the attention of prospects who each have a limited amount of bandwidth, those marketers are forced to invest more resources and money in creating competitive content.

By determining how accurate a machine is relative to human reference standards, we can work on more sophisticated machine-learning methods (i.e., deep-learning convolutional neural networks) to create a narrow-field artificial intelligence system that can extract text features (e.g., acute findings) and tabulate the findings as part of a larger database that could be used for quality studies, outcomes analysis, resource utilization studies, and predictive analytics and modeling,

Deep learning delivers extraordinary cognitive powers in the never-ending battle to distill sense from data at ever larger scales. But high performance doesn't come cheap. Deep learning relies on the application of multilevel neural-network algorithms to high-dimensional data objects. As such, it requires that fast-matrix manipulations in highly parallel architectures in order to identify complex, elusive patterns--such as objects, faces, voices, threats, etc.–amid big data's "3 V" noise. As evidence for the technology's increasingly superhuman cognitive abilities, check out research projects such as this that use it to put the Turing test to shame.

Yoshua Bengio, one of the leading figures behind the rise of deep learning, is launching a Silicon Valley-style startup incubator dedicated to this enormously influential form of artificial intelligence. The incubator, Element AI, will help build companies from AI research that emerges from the University of Montreal, where Bengio is a professor, and nearby McGill University, and he says this is just part of his efforts to develop an "AI ecosystem" in Montreal. Bengio says the Canadian city offers "the biggest concentration in the world" of academic researchers exploring deep learning, the breed of AI that now plays such an important role inside the likes of Google, Facebook, and Microsoft. "Element AI will help entrepreneurs get started in that high-growth area, with a team of experts--and my help--to steer those companies in the right direction," he says. According to Bengio, about 100 researchers are exploring deep learning at the University of Montreal and about 50 others are doing similar work at McGill.

But that is not to say it is perfect by any stretch of the imagination. "Deep learning has led to some big advances in computer vision, natural language processing, and other areas," Tommi Jaakkola, a Massachusetts Institute of Technology professor of electrical engineering and computer science, told Digital Trends. "It's tremendously flexible in terms of learning input/output mappings, but the flexibility and power comes at a cost. That is it that it's very difficult to work out why it is performing a certain prediction in a particular context." This black-boxed lack of transparency would be one thing if deep learning systems were still confined to being lab experiments, but they are not.

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Few industries are as competitive as ecommerce. Not only are online retailers competing with other online stores and brick-and-mortar locations, but also the overall noise that is the Internet. We live in a world where consumer attention span is getting shorter and shorter: 40 percent of people abandon a website that takes more than three seconds to load, and the average shopping cart is abandoned more than 68 percent of the time. I'm hard pressed to find an ecommerce site that is not constantly scrambling to engage more and drive more sales. Technology is finally helping with those efforts in a big way.