We all know very well that China has a surveillance network with more than 170 million cameras that can identify a person in minutes. By the way, the network is operating at full speed: with the help of Facial Recognition AI (artificial intelligence) system, Chinese police identified a fugitive and arrested in the midst of 60,000 people accompanying a show. China has a surveillance network with more than 170 million cameras that can identify a person in minutes. By the way, the network is operating at full speed: with the help of an artificial intelligence system, Chinese police identified a fugitive and arrested in the midst of 60,000 people accompanying a show. This is a 31-year-old man identified only as Ao.

A day after President Trump promised to slash the red tape involved in weapons sales, the administration announced on Thursday a new policy that could vastly expand sales of armed drones, a contentious emblem of the shift toward remotely controlled warfare. That change, in addition to a newly released update to the policy governing which nations are allowed to buy sophisticated American-made weapons, is intended to accelerate arms sales, a key priority of Mr. Trump. The president seemed to foreshadow the new policies on Wednesday night, when he said at a news conference with Prime Minister Shinzo Abe of Japan that after allies order weapons from the United States, "we will get it taken care of, and they will get their equipment rapidly." "It would be, in some cases, years before orders would take place because of bureaucracy with Department of Defense, State Department," Mr. Trump said. It's now going to be a matter of days.

The proposed changes include permitting IPOs that restrict shareholders' voting rights, secondary listings by Chinese and international companies already listed elsewhere and primary listings by unprofitable biotech firms. The reforms are set to become effective April 30. The exchange will begin taking listing applications in early May, Mr. Li said. "This probably is the largest reform we've ever had in the last 25 years," he said, adding that it's "only a matter of time" before the likes of Alibaba and Xiaomi list in Hong Kong. Mr. Li is one of several speakers who are discussing some of the most compelling ideas emerging globally.

For those who ask if artificial intelligence (AI) will change the world – it already has. Advances in AI have accelerated our ability to consume and act on data. We've created machines that can learn on their own using complex calculations involving the largest of data sets. At SAS, we believe that when AI augments human efforts, we can improve not only the businesses that rely on data, but nearly every facet of our lives. Join us about O'Reilly Artificial Intelligence Conference April 30-May 2 or on Twitter April 20 to discuss these and other issues surrounding AI.

Senator Maria Cantwell, D-Wash., just drafted forward-looking legislation that aims to establish a select committee of experts to advise agencies across the government on the economic impact of federal artificial intelligence. The move is an early step toward formalizing the exploration of AI in a government context. But it could ultimately contribute to jump-starting AI-focused programs that help stimulate the United States economy, benefit citizens, uphold data security and privacy, and eventually ensure America is successful during the initial introduction of this important technology to U.S. consumers. The presence of legislation could also lend legitimacy to the prospect of near-term government investment in AI innovation -- something that may even sway Treasury Secretary Steve Mnuchin and others away from their belief that the impact of AI won't be felt for years to come. Indeed, other than a few economic impact and policy reports conducted by the Obama Administration -- led by former U.S. Chief Data Scientist DJ Patil and other tech-minded government leaders -- this is the first policy effort toward moving the U.S. public sector past acknowledging its significance, and toward fully embracing AI technology.

To understand today's headlines about China's approach to issues such as trade, foreign policy or internet censorship, turn to its past. The country is perhaps more aware of its own history than any other major society on earth. That remembering is certainly partial - events like Mao's Cultural Revolution are still very difficult to discuss within China itself. But it is striking how many echoes of the past can be found in its present. China remembers a time when it was forced to trade against its will.

COMPUTERS have already proved better than people at playing chess and diagnosing diseases. But now a group of artificial-intelligence researchers in Singapore have managed to teach industrial robots to assemble an IKEA chair--for the first time uniting the worlds of Allen keys and Alan Turing. Now that machines have mastered one of the most baffling ways of spending a Saturday afternoon, can it be long before AIs rise up and enslave human beings in the silicon mines? The research also holds a serious message. It highlights a deep truth about the limitations of automation.

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages -- from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.

In this work, we focus on the task of generating natural language descriptions from a structured table of facts containing fields (such as nationality, occupation, etc) and values (such as Indian, actor, director, etc). One simple choice is to treat the table as a sequence of fields and values and then use a standard seq2seq model for this task. However, such a model is too generic and does not exploit task-specific characteristics. For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level). Further, a human would continue attending to a field for a few timesteps till all the information from that field has been rendered and then never return back to this field (because there is nothing left to say about it). To capture this behavior we use (i) a fused bifocal attention mechanism which exploits and combines this micro and macro level information and (ii) a gated orthogonalization mechanism which tries to ensure that a field is remembered for a few time steps and then forgotten. We experiment with a recently released dataset which contains fact tables about people and their corresponding one line biographical descriptions in English. In addition, we also introduce two similar datasets for French and German. Our experiments show that the proposed model gives 21% relative improvement over a recently proposed state of the art method and 10% relative improvement over basic seq2seq models. The code and the datasets developed as a part of this work are publicly available.

Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel. The approach jointly optimizes the input distribution (constellation shaping) and the auxiliary channel distribution to compute AIRs without explicit channel knowledge in an end-to-end fashion. Proposed Autoencoder Structure In machine learning, an AE is a neural network (NN) which consists of two parts: an encoder maps an input s (e.g., an image) to a lowerdimensional representation or code and a decoder attempts to reconstruct the input from the code. The AE structure used in this paper is shown in Figure 1 and will be described in the following. The goal is to transmit a message s chosen from a set of M possible messages {1, 2,..., M} M. Following 9 In this paper, we develop an AE for a simplified memoryless fiber channel model.