Would you like your oven to send you a text? Your extractor fan to have a life of its own? Your fridge to become something like a robot, able to talk and catalogue what it contains, sending that information to you while you're on your way home? That, at least, is the vision the companies who made your kitchen are pursuing. The smart home – long the preserve of early adopters who want to control their lightbulbs – is gradually growing up.

Artificial intelligence is assuming a greater role in many walks of life, with research suggesting it may even help doctors diagnose disease. One new study suggests artificial intelligence (AI) might some day detect breast cancer that has spread to the lymph nodes. Researchers found that several computer algorithms outperformed a group of pathologists in analyzing lymph tissue from breast cancer patients. The technology was specifically better at catching small clusters of tumor cells known as micrometastases. "Micrometastases can easily be missed during the routine examination by pathologists," said lead researcher Babak Ehteshami Bejnordi of Radboud University Medical Center in the Netherlands.

This paper presents a framework for exact discovery of the top-k sequential patterns under Leverage. It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact discovery of top-k sequential patterns under a given measure of interest. Our interestingness measure employs the partition approach. A pattern is interesting to the extent that it is more frequent than can be explained by assuming independence between any of the pairs of patterns from which it can be composed. The larger the support compared to the expectation under independence, the more interesting is the pattern. We build on these two elements to exactly extract the k sequential patterns with highest leverage, consistent with our definition of expected support. We conduct experiments on both synthetic data with known patterns and real-world datasets; both experiments confirm the consistency and relevance of our approach with regard to the state of the art. This article was published in Data Mining and Knowledge Discovery and is accessible at http://dx.doi.org/10.1007/s10618-016-0467-9.

For more than 30 years, Geoffrey Hinton hovered at the edges of artificial intelligence research, an outsider clinging to a simple proposition: that computers could think like humans do--using intuition rather than rules. The idea had taken root in Hinton as a teenager when a friend described how a hologram works: innumerable beams of light bouncing off an object are recorded, and then those many representations are scattered over a huge database. Hinton, who comes from a somewhat eccentric, generations-deep family of overachieving scientists, immediately understood that the human brain worked like that, too--information in our brains is spread across a vast network of cells, linked by an endless map of neurons, firing and connecting and transmitting along a billion paths. He wondered: could a computer behave the same way? The answer, according to the academic mainstream, was a deafening no. Computers learned best by rules and logic, they said. And besides, Hinton's notion, called neural networks--which later became the groundwork for "deep learning" or "machine learning"--had already been disproven. In the late '50s, a Cornell scientist named Frank Rosenblatt had proposed the world's first neural network machine. It was called the Perceptron, and it had a simple objective--to recognize images. The goal was to show it a picture of an apple, and it would, at least in theory, spit out "apple." The Perceptron ran on an IBM mainframe, and it was ugly.

The ability to register the emotional response of customers or potential customers from their facial expressions or words they speak or write, is a growing business and another instance of how AI is bringing new capability to the table. Emotion AI is the market seen by Affectiva, while emotion recognition is the market described by EMRAYS. Both are using powerful software incorporating AI to measure emotions people register when they view an ad or write a response. Affectiva spun out of MIT's Media Lab in 2009, co-founded by Dr. Rosalind Picard and Dr. Rana el Kaliouby, now the CEO. Dr. Picard, an engineer, had published the book Affective Computing in 1997.

"The Davos Man," a phrase that dates back to 2004, was first used by political scientist Samuel Huntington to describe the participants of the World Economic Forum held every January in Davos, Switzerland. This year, the rise of the "The Davos Woman" was a clear win for the historical forum that was co-chaired by seven women an all-time high female participation, albeit still miserably low at just "over 21%." At the WEF, held last week in Davos, we sat down with some of the most powerful world leaders in travel, artificial intelligence, healthcare and consulting, and asked them how they approach the mission-critical tasks of building up company culture, embracing lifelong-learning principles, managing the challenges in their field today and fostering the next generation of leaders.

Among those presenting at last week's World Economic Forum in Davos, Switzerland, was Mary "Missy" Cummings, a Duke professor in the Department of Mechanical Engineering and Materials Science whose areas of expertise include artificial intelligence. Duke Today asked Cummings, who has presented before at the forum, about this year's event. Q: How, if at all, was the mood at Davos different this year than in previous years in which you have attended? CUMMINGS: Interestingly I did not see any real difference in the overall mood over last year, but (President) Trump's presence certainly created a buzz that was not as palpable as last year. Q: What were the 3 major points that you made during your presentation this year?

UK-based legal AI company, Luminance, is to open an office in Singapore to meet increasing demand in the Asia-Pacific for doc review automation. The fast-growing legal AI venture said that this move follows winning several clients in Singapore and Australia. Luminance has also released Version 3.0 of its contract review technology and bagged UK law firm, Bird & Bird, as a new client. Bird & Bird would appear to be the second UK law firm to publicly announce it is using Luminance. The other is Slaughter and May, which owns a financial stake in the company. Although, it is understood several other UK firms are currently piloting the AI platform.

We consider an extension of the contextual bandit setting, motivated by several practical applications, where an unlabeled history of contexts can become available for pre-training before the online decision-making begins. We propose an approach for improving the performance of contextual bandit in such setting, via adaptive, dynamic representation learning, which combines offline pre-training on unlabeled history of contexts with online selection and modification of embedding functions. Our experiments on a variety of datasets and in different nonstationary environments demonstrate clear advantages of our approach over the standard contextual bandit.

Community detection in hypergraphs is explored. Under a generative hypergraph model called "d-wise hypergraph stochastic block model" (d-hSBM) which naturally extends the Stochastic Block Model from graphs to d-uniform hypergraphs, the asymptotic minimax mismatch ratio is characterized. For proving the achievability, we propose a two-step polynomial time algorithm that achieves the fundamental limit. The first step of the algorithm is a hypergraph spectral clustering method which achieves partial recovery to a certain precision level. The second step is a local refinement method which leverages the underlying probabilistic model along with parameter estimation from the outcome of the first step. To characterize the asymptotic performance of the proposed algorithm, we first derive a sufficient condition for attaining weak consistency in the hypergraph spectral clustering step. Then, under the guarantee of weak consistency in the first step, we upper bound the worst-case risk attained in the local refinement step by an exponentially decaying function of the size of the hypergraph and characterize the decaying rate. For proving the converse, the lower bound of the minimax mismatch ratio is set by finding a smaller parameter space which contains the most dominant error events, inspired by the analysis in the achievability part. It turns out that the minimax mismatch ratio decays exponentially fast to zero as the number of nodes tends to infinity, and the rate function is a weighted combination of several divergence terms, each of which is the Renyi divergence of order 1/2 between two Bernoulli's. The Bernoulli's involved in the characterization of the rate function are those governing the random instantiation of hyperedges in d-hSBM. Experimental results on synthetic data validate our theoretical finding that the refinement step is critical in achieving the optimal statistical limit.