We propose a method for constructing artificial intelligence (AI) of mahjong, which is a multiplayer imperfect information game. Since the size of the game tree is huge, constructing an expert-level AI player of mahjong is challenging. We define multiple Markov decision processes (MDPs) as abstractions of mahjong to construct effective search trees. We also introduce two methods of inferring state values of the original mahjong using these MDPs. We evaluated the effectiveness of our method using gameplays vis-\`{a}-vis the current strongest AI player.

With the rise of knowledge management and knowledge economy, the knowledge elements that directly link and embody the knowledge system have become the research focus and hotspot in certain areas. The existing knowledge element representation methods are limited in functions to deal with the formality, logic and reasoning. Based on description logic ALC and the common knowledge element model, in order to describe the knowledge element, the description logic ALC is expanded. The concept is extended to two different ones (that is, the object knowledge element concept and the attribute knowledge element concept). The relationship is extended to three (that is, relationship between object knowledge element concept and attribute knowledge element concept, relationship among object knowledge element concepts, relationship among attribute knowledge element concepts), and the inverse relationship constructor is added to propose a description logic KEDL system. By demonstrating, the relevant properties, such as completeness, reliability, of the described logic system KEDL are obtained. Finally, it is verified by the example that the description logic KEDL system has strong knowledge element description ability. Introduction With the rise of knowledge management and knowledge economy, knowledge has attracted people's attention as an important strategic resource. The direct control and management of knowledge itself has become the focus of attention in various disciplines.

Deep learning has been widely used for hyperspectral pixel classification due to its ability of generating deep feature representation. However, how to construct an efficient and powerful network suitable for hyperspectral data is still under exploration. In this paper, a novel neural network model is designed for taking full advantage of the spectral-spatial structure of hyperspectral data. Firstly, we extract pixel-based intrinsic features from rich yet redundant spectral bands by a subnetwork with supervised pre-training scheme. Secondly, in order to utilize the local spatial correlation among pixels, we share the previous subnetwork as a spectral feature extractor for each pixel in a patch of image, after which the spectral features of all pixels in a patch are combined and feeded into the subsequent classification subnetwork. Finally, the whole network is further fine-tuned to improve its classification performance. Specially, the spectral-spatial factorization scheme is applied in our model architecture, making the network size and the number of parameters great less than the existing spectral-spatial deep networks for hyperspectral image classification. Experiments on the hyperspectral data sets show that, compared with some state-of-art deep learning methods, our method achieves better classification results while having smaller network size and less parameters.

A counterfactual query is typically of the form 'For situation X, why was the outcome Y and not Z?'. A counterfactual explanation (or response to such a query) is of the form "If X was X*, then the outcome would have been Z rather than Y." In this work, we develop a technique to produce counterfactual visual explanations. Given a 'query' image $I$ for which a vision system predicts class $c$, a counterfactual visual explanation identifies how $I$ could change such that the system would output a different specified class $c'$. To do this, we select a 'distractor' image $I'$ that the system predicts as class $c'$ and identify spatial regions in $I$ and $I'$ such that replacing the identified region in $I$ with the identified region in $I'$ would push the system towards classifying $I$ as $c'$. We apply our approach to multiple image classification datasets generating qualitative results showcasing the interpretability and discriminativeness of our counterfactual explanations. To explore the effectiveness of our explanations in teaching humans, we present machine teaching experiments for the task of fine-grained bird classification. We find that users trained to distinguish bird species fare better when given access to counterfactual explanations in addition to training examples.

Glas discusses how ERICA was designed, the uncanny valley, the software architecture of ERICA, and some of the research studies that ERICA has been involved in. Dylan Glas is a Senior Robotics Software Architect at Futurewei Technologies, a research division of Huawei in Silicon Valley. He was previously a senior researcher in social robotics at Hiroshi Ishiguro Laboratories at ATR and a Guest Associate Professor at the Intelligent Robotics Laboratory at Osaka University. He was the chief architect for the ERICA android in the ERATO Ishiguro Symbiotic Human-Robot Interaction Project. His research interests include social human-machine interaction, ubiquitous sensing, network robot systems, teleoperation for social robots, and machine learning.

Society is shaped by humans' unique capacity to take on another person's perspective - which might be the reason why modern societies have such extraordinary levels of cooperation. Writing for the Conversation, Arunas L. Radzvilavicius, a Postdoctoral Researcher of Evolutionary Biology at the University of Pennsylvania explains that social norms can shed light on why people display altruistic behaviour. Humans are far more likely to be kind to individuals they see as'good', than they are to people of'bad' reputation. But, he says, if everyone agrees that being altruistic toward other cooperators earns you a good reputation, cooperation will persist. A person who appears'good' to someone might seem like a bad individual from another person's perspective.

Instagram and Facebook likes and Snapchat streaks could be banned for young people, under new rules that attempt to protect children's safety on the internet. A new report from the British Information Commissioner's Office suggests that those features and other techniques that "nudge" users into engaging with the site should be switched off. The features encourage them to stay online longer and so could damage their wellbeing, the major report suggests. The report also suggests that such features should be banned entirely on websites that can't be sure whether or not their users are under-18. The suggested rules on likes and streaks are just one of a whole range of wide-ranging changes to the way social media sites could work.

Hackers had access to Hotmail, MSN and Outlook emails from a large number of accounts for two months, Microsoft has revealed. The technology giant confirmed that email accounts of non-corporate users were breached, with the contents of around 6 per cent of emails exposed by cyber criminals exploiting a customer support portal. According to an email sent to the majority of affected users and then posted online, the firm said a Microsoft support agent's credentials were compromised, potentially allowing unauthorised access to some account information. We'll tell you what's true. You can form your own view.

Last Wednesday, US lawmakers introduced a new bill that represents one of the country's first major efforts to regulate AI. There are likely to be more to come. It hints at a dramatic shift in Washington's stance toward one of this century's most powerful technologies. Only a few years ago, policymakers had little inclination to regulate AI. Now, as the consequences of not doing so grow increasingly tangible, a small contingent in Congress is advancing a broader strategy to rein the technology in.

The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. In the 1970s, Dr. Geoffrey Franglen of St. George's Hospital Medical School in London began writing an algorithm to screen student applications for admission.