Donald Trump is speaking Mandarin. This is happening in the city of Tianjin, about an hour's drive south of Beijing, within a gleaming office building that belongs to iFlytek, one of China's rapidly rising artificial-intelligence companies. Beyond guarded gates, inside a glitzy showroom, the US president is on a large TV screen heaping praise on the Chinese company. It's Trump's voice and face, but the recording is, of course, fake--a cheeky demonstration of the cutting-edge AI technology iFlytek is developing. Jiang Tao chuckles and leads the way to some other examples of iFlytek's technology.

BEIJING – China's policy of "reform and opening up" has brought monumental changes to the world's most populous country since its launch 40 years ago under leader Deng Xiaoping. Next week, China will mark the anniversary of the shift, agreed to at a Communist Party gathering on Dec. 18, 1978. Ou Banlan, 52, is a retired garment factory worker in Shenzhen, a former fishing village that was the testing ground for the reforms and morphed into a major manufacturing and high-tech hub. "My life is much better than that of my parents' generation," said the diminutive woman with short black hair, standing in front of the factory where she once toiled. She was born and raised in a village outside Shenzhen.

The surprise arrest of Huawei Technologies Co.'s Chief Financial Officer Meng Wanzhou is about to impact one of the Chinese company's suppliers in Japan. Yaskawa Electric Corp., which supplies industrial robots for Huawei's smartphone and telecom gear factories, saw all orders for its machines put on hold after the arrest, President Hiroshi Ogasawara said in an interview Wednesday. Of Yaskawa's ¥448.5 billion revenue for the fiscal year that ended in February, 23 percent came from China. "My people on the ground in China say that Huawei is turned upside down internally," Ogasawara said. "All kinds of capex (capital expenditure) deals are temporarily on hold as they figure things out."

Several contemporary artists tackling the social implications of technology have been banned by censors from China's upcoming Guangzhou Triennial. One of them was Heather Dewey-Hagborg, whose works often critique biotechnology, notably including portraits derived from the DNA of Chelsea Manning. She woke up last on December 8th to an email from one of the show's three curators, Angelique Spaninks, explaining that her piece T3511 was being pulled last-minute. The triennial, titled "As We May Think, Feedforward," explores the links between humanity and technology and opens on December 21st. Spaninks told Dewey-Hagborg that her work was censored by the government, and while she was given no official justification, speculated that authorities were sensitive to bioethics issues.

Yes--but it's far from enough to satisfy China hawks like U.S. Trade Representative Robert Lighthizer. Markets clearly recognize this: The S&P 500 ended up only 0.5% Wednesday after the news broke. Moreover, the China 2025 plan itself--despite all the attention it has received--may be less menacing than it seems. What's really needed to take negotiations to the next level, and assuage market concerns, is for China to enact a few big-bang reforms to convince foreigners that Xi Jinping's administration is committed to level dealing. Doing away entirely with most joint-venture requirements--instead of endless foot-dragging and qualifications--is one possibility.

As reinforcement learning algorithms are being applied to increasingly complicated and realistic tasks, it is becoming increasingly difficult to solve such problems within a practical time frame. Hence, we focus on a \textit{satisficing} strategy that looks for an action whose value is above the aspiration level (analogous to the break-even point), rather than the optimal action. In this paper, we introduce a simple mathematical model called risk-sensitive satisficing ($RS$) that implements a satisficing strategy by integrating risk-averse and risk-prone attitudes under the greedy policy. We apply the proposed model to the $K$-armed bandit problems, which constitute the most basic class of reinforcement learning tasks, and prove two propositions. The first is that $RS$ is guaranteed to find an action whose value is above the aspiration level. The second is that the regret (expected loss) of $RS$ is upper bounded by a finite value, given that the aspiration level is set to an "optimal level" so that satisficing implies optimizing. We confirm the results through numerical simulations and compare the performance of $RS$ with that of other representative algorithms for the $K$-armed bandit problems.

The 2018 AAAI Conference on Artificial Intelligence, the 2018 Innovative Applications of Artificial Intelligence, and the 2018 Symposium on Educational Advances in Artificial Intelligence were held February 2–7, 2018 at the Hilton New Orleans Riverside, New Orleans, Louisiana, USA.  This report, based on the prefaces contained in the AAAI-18 proceedings and program, summarizes the events of the conference.

Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical to help explain ASD and predict or monitor treatment outcomes. Toward this end, deep learning classifiers have recently been used for identifying ASD from functional magnetic resonance imaging (fMRI) with higher accuracy than traditional learning strategies. However, a key challenge with deep learning models is understanding just what image features the network is using, which can in turn be used to define the biomarkers. Current methods extract biomarkers, i.e., important features, by looking at how the prediction changes if "ignoring" one feature at a time. In this work, we go beyond looking at only individual features by using Shapley value explanation (SVE) from cooperative game theory. Cooperative game theory is advantageous here because it directly considers the interaction between features and can be applied to any machine learning method, making it a novel, more accurate way of determining instance-wise biomarker importance from deep learning models. A barrier to using SVE is its computational complexity: $2^N$ given $N$ features. We explicitly reduce the complexity of SVE computation by two approaches based on the underlying graph structure of the input data: 1) only consider the centralized coalition of each feature; 2) a hierarchical pipeline which first clusters features into small communities, then applies SVE in each community. Monte Carlo approximation can be used for large permutation sets. We first validate our methods on the MNIST dataset and compare to human perception. Next, to insure plausibility of our biomarker results, we train a Random Forest (RF) to classify ASD/control subjects from fMRI and compare SVE results to standard RF-based feature importance. Finally, we show initial results on ranked fMRI biomarkers using SVE on a deep learning classifier for the ASD/control dataset.

Sensor drift is a well-known issue in the field of sensors and measurement and has plagued the sensor community for many years. In this paper, we propose a sensor drift correction method to deal with the sensor drift problem. Specifically, we propose a discriminative subspace projection approach for sensor drift reduction in electronic noses. The proposed method inherits the merits of the subspace projection method called domain regularized component analysis. Moreover, the proposed method takes the source data label information into consideration, which minimizes the within-class variance of the projected source samples and at the same time maximizes the between-class variance. The label information is exploited to avoid overlapping of samples with different labels in the subspace. Experiments on two sensor drift datasets have shown the effectiveness of the proposed approach. Keywords: Sensor drift; Electronic nose; Subspace projection method; Domain adaptation; Transfer learning.

The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018. This report summarizes the The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018.  IEA/AIE 2018 continued the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas including engineering, science, industry, automation a robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace, and human-machine interactions.