In the future, artificial intelligence will diagnose you and pick out the right medication. To provide a human connection. That's according to Kai-Fu Lee, CEO of Sinovation Ventures (创新工场), former president of Google Greater China, and author of the book "AI Superpowers: China, Silicon Valley, and the New World Order." Lee predicts the complicated, technical parts of medicine will be handled by AI. But the role of doctors will change dramatically. Once physicians aren't bound by the scientific requirements of medicine, they will rely more on developing emotional intelligence and good communication when providing care to patients. "Doctors no longer need to have an [encyclopedic] knowledge of all kind of diseases and treatments, which is where mistakes come from," Lee said, in the video interview.

Personal transportation in a world void of human drivers will presumably be much safer, right? That's great, but it also means that a lot of people are going to be without jobs. Insurance companies won't need nearly as many claims adjusters, the DMV won't need to be nearly as large as it is today, police forces could be greatly reduced and as morbid as it sounds, hospitals won't need as many doctors (in 2012, motor vehicle collisions sent nearly 7,000 Americans to the ER each day). With fewer people dying in auto accidents, there won't be nearly as many organ donations, meaning that some sick people who might have survived thanks to a transplant won't live as long. It's a seemingly endless chain reaction of cause and effect.

On giant screens in the European parliament building in Brussels last week, the face of Mark Zuckerberg looked down on the world's data protection and privacy commissioners assembled there for their annual conference. What he said was cautious and rather bland, but the imagery was potent: a young Big Brother issuing a tailored message to those who administer the laws of many lands. Zuckerberg did not take questions – a Facebook executive in the chamber did, after Zuckerberg faded from the screens into the green and sunny background of his distant locale. An actual dialogue with the controller of Facebook might have been illuminating. For example, does Facebook anticipate, as others speculate, that the internet will split into two, or three – the US internet, the China internet and the EU internet?

In 2017, United Nations Secretary-General António Guterres noted the difficult challenge that policymakers, particularly those in the Global South face with respect to AI. He said that "The implications for development are enormous. Developing countries can gain from the benefits of AI, but they also face the highest risk of being left behind." For example, in Nigeria doctors are using AI to help reduce the incidence of birth asphyxia, a leading cause of under-five death in Africa, and yet at the same time there are real concerns about AI's impact on rising unemployment and the influence that Google, China, and others are exerting across the Global South. AI technologies are raising complex social, political, technological, economic, and ethical questions.

Vladimir Putin spoke with students about science in an open lesson on September 1, the start of the school year in Russia. He told them that "the future belongs to artificial intelligence," and whoever masters it first will rule the world. "Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world," Russian President Vladimir Putin said.

ABSTRACT End-to-end speech synthesis is a promising approach that directly converts raw text to speech. Although it was shown that Tacotron2 outperforms classical pipeline systems with regards to naturalness in English, its applicability to other languages is still unknown. Japanese could be one of the most difficult languages for which to achieve end-to-end speech synthesis, largely due to its character diversity and pitch accents. Therefore, state-of-theart systems are still based on a traditional pipeline framework that requires a separate text analyzer and duration model. Towards endto-end Japanese speech synthesis, we extend Tacotron to systems with self-attention to capture long-term dependencies related to pitch accents and compare their audio quality with classical pipeline systems under various conditions to show their pros and cons. In a large-scale listening test, we investigated the impacts of the presence of accentual-type labels, the use of force or predicted alignments, and acoustic features used as local condition parameters of the Wavenet vocoder. Our results reveal that although the proposed systems still do not match the quality of a top-line pipeline system for Japanese, we show important stepping stones towards end-to-end Japanese speech synthesis. Index Terms-- speech synthesis, deep learning, Tacotron 1. INTRODUCTION Tacotron [1] opened a novel path to end-to-end speech synthesis.

With the advent of modern data collection and storage technologies, data-driven approaches have been developed for discovering the governing partial differential equations (PDE) of physical problems. However, in the extant works the model parameters in the equations are either assumed to be known or have a linear dependency. Therefore, most of the realistic physical processes cannot be identified with the current data-driven PDE discovery approaches. In this study, an innovative framework is developed that combines data-driven and data-assimilation methods for simultaneously identifying physical processes and inferring model parameters. Spatiotemporal measurement data are first divided into a training data set and a testing data set. Using the training data set, a data-driven method is developed to learn the governing equation of the considered physical problem by identifying the occurred (or dominated) processes and selecting the proper empirical model. Through introducing a prediction error of the learned governing equation for the testing data set, a data-assimilation method is devised to estimate the uncertain model parameters of the selected empirical model. For the contaminant transport problem investigated, the results demonstrate that the proposed method can adequately identify the considered physical processes via concurrently discovering the corresponding governing equations and inferring uncertain parameters of nonlinear models, even in the presence of measurement errors. This work helps to broaden the applicable area of the research of data driven discovery of governing equations of physical problems.

Using piezoelectric impedance/admittance sensing for structural health monitoring is promising, owing to the simplicity in circuitry design as well as the high-frequency interrogation capability. The actual identification of fault location and severity using impedance/admittance measurements, nevertheless, remains to be an extremely challenging task. A first-principle based structural model using finite element discretization requires high dimensionality to characterize the high-frequency response. As such, direct inversion using the sensitivity matrix usually yields an under-determined problem. Alternatively, the identification problem may be cast into an optimization framework in which fault parameters are identified through repeated forward finite element analysis which however is oftentimes computationally prohibitive. This paper presents an efficient data-assisted optimization approach for fault identification without using finite element model iteratively. We formulate a many-objective optimization problem to identify fault parameters, where response surfaces of impedance measurements are constructed through Gaussian process-based calibration. To balance between solution diversity and convergence, an -dominance enabled many-objective simulated annealing algorithm is established. As multiple solutions are expected, a voting score calculation procedure is developed to further identify those solutions that yield better implications regarding structural health condition. The effectiveness of the proposed approach is demonstrated by systematic numerical and experimental case studies.

This letter presents a physical human-robot interaction scenario in which a robot guides and performs the role of a teacher within a defined dance training framework. A combined cognitive and physical feedback of performance is proposed for assisting the skill learning process. Direct contact cooperation has been designed through an adaptive impedance-based controller that adjusts according to the partner's performance in the task. In measuring performance, a scoring system has been designed using the concept of progressive teaching (PT). The system adjusts the difficulty based on the user's number of practices and performance history. Using the proposed method and a baseline constant controller, comparative experiments have shown that the PT presents better performance in the initial stage of skill learning. An analysis of the subjects' perception of comfort, peace of mind, and robot performance have shown a significant difference at the p < .01 level, favoring the PT algorithm.

Text classification is a challenging problem which aims to identify the category of texts. Recently, Capsule Networks (CapsNets) are proposed for image classification. It has been shown that CapsNets have several advantages over Convolutional Neural Networks (CNNs), while, their validity in the domain of text has less been explored. An effective method named deep compositional code learning has been proposed lately. This method can save many parameters about word embeddings without any significant sacrifices in performance. In this paper, we introduce the Compositional Coding (CC) mechanism between capsules, and we propose a new routing algorithm, which is based on k-means clustering theory. Experiments conducted on eight challenging text classification datasets show the proposed method achieves competitive accuracy compared to the state-of-the-art approach with significantly fewer parameters.