In warehouses, hospitals and retail stores, and on city streets, industrial parks and the footpaths of college campuses, the first representatives of this new invading force are starting to become apparent. "The robots are among us," says Steve Jurvetson, a Silicon Valley investor and a director at Elon Musk's Tesla and SpaceX companies, which have relied heavily on robotics. A multitude of machines will follow, he says: "A lot of people are going to come in contact with robots in the next two to five years." The arrival of the robots -- and their potentially devastating effect on human employment -- has been widely predicted. Now, the machines are starting to roll or walk out of the labs.

Using humanoid robots to study human behavior.

In the new digital era, a business needs a store of data to help inform its decisions and interactions, but data is useless unless someone acts upon it. Unfortunately, it's very easy to collect more data than any human team could possibly sort through, let alone put into practice. That's where machine learning comes in. These systems learn from information stores to locate patterns and create rules with computer speed. Machine learning is becoming a valuable, perhaps even necessary, part of business infrastructure.

China and Russia said Monday that the South China Sea dispute should not be internationalized and called for its settlement based on negotiation and consultation, Beijing's official Xinhua News reported. The comments come at time when the United States has beefed up its military presence in the contested region in a bid to help the Philippines and other Southeast Asian countries tackle China's assertiveness. Chinese Foreign Minister Wang Yi and his Russian counterpart Sergey Lavrov made the comments during a meeting on Monday. Wang insisted that China was protecting its rights and interests in South China Sea, and was free to choose how to resolve tensions in the area, Xinhua reported. The world's second largest economy's refusal of the Philippines' proposed international arbitration case over the matter was meant to uphold the dignity and authority of the law, Wang said, adding that China and Russia should be cautious against abuses of mandatory arbitration. Meanwhile, Hugo Swire, the British minister of state responsible for East Asia, said earlier in the day that a ruling -- expected in May or early June -- in the Philippines' international arbitration case against China's South China Sea claims must be binding.

In this demo we present a semantic framework for innovation and patent analytics powered by Mined Semantic Analysis (MSA). Our framework provides cognitive assistance to its users through a Web-based visual and interactive interface. First, we describe building a conceptual knowledge graph by mining user-generated encyclopedic textual corpus for semantic associations. Then, we demonstrate applying the acquired knowledge to support many cognition and knowledge based use cases for innovation analysis including technology exploration and landscaping, competitive analysis, literature and prior art search and others.

The concept of "affordance" represents the relationship between human perceivers and their environment. Affordance perception, representation, and inference are central to commonsense reasoning, tool-use and creative problem-solving in artificial agents. Existing approaches fail to provide flexibility with which to reason about affordances in the open world, where they are influenced by changing context, social norms, historical precedence, and uncertainty. We develop a formal rules-based logical representational format coupled with an uncertainty-processing framework to reason about cognitive affordances in a more general manner than shown in the existing literature. Our framework allows agents to make deductive and abductive inferences about functional and social affordances, collectively and dynamically, thereby allowing the agent to adapt to changing conditions. We demonstrate our approach with an example, and show that an agent can successfully reason through situations that involve a tight interplay between various social and functional norms.

The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime. The pro-active caching of certain subqueries further increases efficiency. Our empirical evaluation shows that this approach can often match or even surpass the performance of state-of-the-art systems, especially under restricted resources.

Privacy preserving in association mining is an important research topic in the database security field. This paper has proposed a blocking-based method to solve the association rule hiding problem for data sharing. It aims at reducing undesirable side effects and increasing desirable side effects, while ensuring to conceal all sensitive rules. The candidate transactions are selected for sanitization based on their relations with border rules. Comparative experiments on real datasets demonstrate that the proposed method can achieve its goals.

Aspect extraction is a key task of fine-grained opinion mining. Although it has been studied by many researchers, it remains to be highly challenging. This paper proposes a novel unsupervised approach to make a major improvement. The approach is based on the framework of lifelong learning and is implemented with two forms of recommendations that are based on semantic similarity and aspect associations respectively. Experimental results using eight review datasets show the effectiveness of the proposed approach.

We study a novel task of numerical relation extraction with the goal of extracting relations where one of the arguments is a number or a quantity ( e.g., atomic_number(Aluminium, 13), inflation_rate(India, 10.9%)). This task presents peculiar challenges not found in standard IE, such as the difficulty of matching numbers in distant supervision and the importance of units. We design two extraction systems that require minimal human supervision per relation: (1) NumberRule, a rule based extractor, and (2) NumberTron, a probabilistic graphical model. We find that both systems dramatically outperform MultiR, a state-of-the-art non-numerical IE model, obtaining up to 25 points F-score improvement.