In 2017 Gartner predicted that artificial intelligence (AI) would benefit procurement and sourcing technology. That moment has arrived, according to Mike Quindazzi, managing director at PriceWaterhouseCoopers and top financial-tech influencer. "We're now in the golden age of AI, where advancements come from voluminous sets of data, new algorithms being created, computing power and the ability to do this in the cloud at scale," said Quindazzi. Procurement data has exploded because procurement has evolved into something called "intelligent spend management," which oversees all corporate purchasing processes including direct and indirect purchases, travel and external labor. Quindazzi cautions that while AI has many use cases in procurement, such as rating vendors, "there will always be a human at the end of AI processes, so there needs to be a sense of accountability."

January 31, 2019Disaster relief, remote-healthcare diagnostics, tracking rhino poachers, upping student achievement--each of these disparate activities could get a big boost from artificial intelligence (AI). A recent discussion paper from the McKinsey Global Institute (MGI), "Notes from the AI frontier: Applying AI for social good," lays out just how AI can help tackle some of the world's most challenging social problems, analyzing 160 use cases. The good news: about one-third are already being used today. Still, there's much more that can be done, both to implement these solutions and to fully understand the breadth of what they can do for social-good organizations. We gathered questions about this topic from our social-media audience around the globe for Michael Chui, an MGI partner based in McKinsey's San Francisco office and one of the report's authors.

The Viral Marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a large cascade of propagation and adoptions. In this paper, we will introduce an evidential opinion-based influence maximization model for viral marketing. Besides, our approach tackles three opinions based scenarios for viral marketing in the real world. The first scenario concerns influencers who have a positive opinion about the product. The second scenario deals with influencers who have a positive opinion about the product and produce effects on users who also have a positive opinion. The third scenario involves influence users who have a positive opinion about the product and produce effects on the negative opinion of other users concerning the product in question. Next, we proposed six influence measures, two for each scenario. We also use an influence maximization model that the set of detected influencers for each scenario. Finally, we show the performance of the proposed model with each influence measure through some experiments conducted on a generated dataset and a real world dataset collected from Twitter.

One of the classical approaches to automated planning is the reduction to propositional satisfiability (SAT). Recently, it has been shown that incremental SAT solving can increase the capabilities of several modern encodings for SAT-based planning. In this paper, we present a further improvement to SAT-based planning by introducing a new algorithm named PASAR based on the principles of counterexample guided abstraction refinement (CEGAR). As an abstraction of the original problem, we use a simplified encoding where interference between actions is generally allowed. Abstract plans are converted into actual plans where possible or otherwise used as a counterexample to refine the abstraction. Using benchmark domains from recent International Planning Competitions, we compare our approach to different state-of-the-art planners and find that, in particular, combining PASAR with forward state-space search techniques leads to promising results.

When you think of canine-inspired robots, your brain probably conjures up images of Boston Dynamics' celebrated dog robot, Spot. Swiss robotics company Anybotics has also created its own audacious, quadruped robot. The size of a large dog and weighing a little under 80 pounds, Anymal aims to be the gold standard in dog-bots. It's capable of autonomously walking, running, and climbing, and can even get back on its feet if it falls over. Although Spot will go on sale for the first time later this year, this gleaming robotic beast is already on the market in Europe, the United States, and the Middle East.

With 340,000 trained volunteers in 189 countries and 140 languages, 7 Cups has an unparalleled reach in the behavioral health space.

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Earlier this month, an unexceptional thread appeared on Reddit announcing that there is a new way "to cook egg white[s] without a frying pan". As so often happens on this website, which calls itself "the front page of the internet", this seemingly banal comment inspired a slew of responses. "I've never heard of people frying eggs without a frying pan," one incredulous Redditor replied. "I'm gonna try this," added another. One particularly enthusiastic commenter even offered to look up the scientific literature on the history of cooking egg whites without a frying pan. Every day, millions of these unremarkable conversations unfold on Reddit, spanning from cooking techniques to geopolitics in the Western Sahara to birds with arms.

With the recent advances in complex networks theory, graph-based techniques for image segmentation has attracted great attention recently. In order to segment the image into meaningful connected components, this paper proposes an image segmentation general framework using complex networks based community detection algorithms. If we consider regions as communities, using community detection algorithms directly can lead to an over-segmented image. To address this problem, we start by splitting the image into small regions using an initial segmentation. The obtained regions are used for building the complex network. To produce meaningful connected components and detect homogeneous communities, some combinations of color and texture based features are employed in order to quantify the regions similarities. To sum up, the network of regions is constructed adaptively to avoid many small regions in the image, and then, community detection algorithms are applied on the resulting adaptive similarity matrix to obtain the final segmented image. Experiments are conducted on Berkeley Segmentation Dataset and four of the most influential community detection algorithms are tested. Experimental results have shown that the proposed general framework increases the segmentation performances compared to some existing methods.

Interval type-2 (IT2) fuzzy systems have become increasingly popular in the last 20 years. They have demonstrated superior performance in many applications. However, the operation of an IT2 fuzzy system is more complex than that of its type-1 counterpart. There are many questions to be answered in designing an IT2 fuzzy system: Should singleton or non-singleton fuzzifier be used? How many membership functions (MFs) should be used for each input? Should Gaussian or piecewise linear MFs be used? Should Mamdani or Takagi-Sugeno-Kang (TSK) inference be used? Should minimum or product $t$-norm be used? Should type-reduction be used or not? How to optimize the IT2 fuzzy system? These questions may look overwhelming and confusing to IT2 beginners. In this paper we recommend some representative starting choices for an IT2 fuzzy system design, which hopefully will make IT2 fuzzy systems more accessible to IT2 fuzzy system designers.