Chinese companies are "aggressively investing" in artificial intelligence (AI) applications and show more thirst for talent, a joint study by Massachusetts Institute of Technology (MIT) and Boston Consulting Group (BCG) shows, at a time when the race for AI superiority is in the spotlight around the world. The conclusion – based on a survey of over 3,000 participants in 126 countries and 300 executives from China – shines a light on China's ambitions in AI, which is seen as a major driver of the new economy, and the perceived competitive threat the country poses to other big economies. "China's rapid rise in AI has been a wake-up call for nations, industries and corporate executives globally," says the report, which was released on Tuesday and titled Artificial Intelligence in Business Gets Real. "Indeed, many recent national programmes to advance the development of AI refer to China as a competitive threat." Betting big on the core technology behind an array of cutting-edge applications from autonomous driving to facial recognition, China's State Council last July laid out a three-step road map to AI supremacy.

Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication technologies for which a rational design is no longer feasible. Also, numerical resources are available to enable the computational photonic material design and to identify structures that meet predefined optical properties for specific applications. However, the optimization objective function is in general non-convex and its computation remains resource demanding such that the right choice for the optimization method is crucial to obtain excellent results. Here, we benchmark five global optimization methods for three typical nano-optical optimization problems from the field of shape optimization and parameter reconstruction: downhill simplex optimization, the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, particle swarm optimization, differential evolution, and Bayesian optimization. In these examples, Bayesian optimization, mainly known from machine learning applications, obtains significantly better results in a fraction of the run times of the other optimization methods.

Semantic frame parsing is a crucial component in spoken language understanding (SLU) to build spoken dialog systems. It has two main tasks: intent detection and slot filling. Although state-of-the-art approaches showed good results, they require large annotated training data and long training time. In this paper, we aim to alleviate these drawbacks for semantic frame parsing by utilizing the ubiquitous user information. We design a novel coarse-to-fine deep neural network model to incorporate prior knowledge of user information intermediately to better and quickly train a semantic frame parser. Due to the lack of benchmark dataset with real user information, we synthesize the simplest type of user information (location and time) on ATIS benchmark data. The results show that our approach leverages such simple user information to outperform state-of-the-art approaches by 0.25% for intent detection and 0.31% for slot filling using standard training data. When using smaller training data, the performance improvement on intent detection and slot filling reaches up to 1.35% and 1.20% respectively. We also show that our approach can achieve similar performance as state-of-the-art approaches by using less than 80% annotated training data. Moreover, the training time to achieve the similar performance is also reduced by over 60%.

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the "single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes. We propose a new approach for capturing this decision-making process through counterfactual reasoning in pairwise comparisons. Our approach is model-free and does not require iterating through the state space. We demonstrate that this approach accurately learns multifaceted heuristics on a synthetic and real world data sets. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of schedule optimization. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates optimal solutions up to 9.5 times faster than a state-of-the-art optimization algorithm.

The terms artificial intelligence (AI), machine learning and big data have all become buzzwords of late, and you may be wondering whether you might be able to utilize these innovative technologies for your own benefit. Figuring out which problems in your business would be suitable for AI is a good place to start. Furthermore, determining whether those problems are automation problems or learning problems is equally important. Automation without machine learning capabilities is appropriate for problems that are relatively straightforward in nature (i.e. These are the tasks and workflows that have a predefined sequence of steps currently being carried out by a human worker, but that could feasibly be transferred over to a software robot.

We review common methods of solving for multi-class from binary and generalize them to a common framework. Since conditional probabilties are useful both for quantifying the accuracy of an estimate and for calibration purposes, these are a required part of the solution. There is some indication that the best solution for multi-class classification is dependent on the particular dataset. As such, we are particularly interested in data-driven solution design, whether based on a priori considerations or empirical examination of the data. Numerical results indicate that while a one-size-fits-all solution consisting of one-versus-one is appropriate for most datasets, a minority will benefit from a more customized approach. The techniques discussed in this paper allow for a large variety of multi-class configurations and solution methods to be explored so as to optimize classification accuracy, accuracy of conditional probabilities and speed.

Abstract--With the advent of the era of artificial intelligence (AI), deep neural networks (DNNs) have shown huge superiority over human in image recognition, speech processing, autonomous vehicles and medical diagnosis. However, recent studies indicate that DNNs are vulnerable to adversarial examples (AEs) which are designed by attackers to fool deep learning models. Different from real examples, AEs can hardly be distinguished from human eyes, but mislead the model to predict incorrect outputs and therefore threaten security critical deep-learning applications. In recent years, the generation and defense of AEs have become a research hotspot in the field of AI security. This article reviews the latest research progress of AEs. First, we introduce the concept, cause, characteristic and evaluation metrics of AEs, then give a survey on the state-of-the-art AE generation methods with the discussion of advantages and disadvantages. After that we review the existing defenses and discuss their limitations. Finally, the future research opportunities and challenges of AEs are prospected. In the era of AI, DNNs have shown great advantages in autonomous vehicles, robotics, network security, image/speech recognition and natural language processing (NLP). For example, in 2017, an intelligent robot with the superior face recognition ability, named XiaoDu developed by Baidu, defeated a representative from the team of humans strongest brain with the score of 3:2 [1]. On October 19th, 2017, the DeepMind team of Google released the AlphaGo Zero, which shocked the world. Compared with the previous AlphaGo, AlphaGo Zero relies on reinforcement learning without any priori knowledge to grow chess skills and finally beats every human competitor [2]. For AI research, the United States received huge support from the government, such as the Federal Research Fund. In October 2016, the United States issued the project of Preparing for the Future of Artificial Intelligence and the National Artificial Intelligence Research and Development Strategic Plan, which raised AI to the national strategic level and formulated ambitious blueprints [3], [4]. Manuscript received xxx; revised xx; accepted xxx. This work is supported by the National Natural Science Foundation of China (Grant NOs. J. Zhang and X. Jiang are with the College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China (email: zhangjiliang@hnu.edu.cn). In the same year, AI was written into the nineteenth National Congress report, which pushed the development of AI industries to a new height and filled the gap in the top-level strategy of AI development [5].

We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi-supervised learning benchmark experiments, and outperforms the neural networks in active learning experiments where labels are scarce. Furthermore, the model does not require a validation data set for early stopping to control over-fitting. Our model can be viewed as an instance of empirical distribution regression weighted locally by network connectivity. We further motivate the intuitive construction of the model with a Bayesian linear model interpretation where the node features are filtered by an operator related to the graph Laplacian. The method can be easily implemented by adapting off-the-shelf scalable variational inference algorithms for Gaussian processes.

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state of the art performance for document classification and speech recognition. In this article, we study the current state of the art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

On May 16, via a video link, US President Donald Trump "addressed" a conference in Tianjin from Washington and floored the audience with his almost flawless Chinese. Trump highlighted the big leaps made by artificial intelligence or AI, but what impressed the audience more was the US president's tone - his Chinese intonations, inflections and pitch were near perfect. Well, as it transpired, the voice was not really Trump's, after all, but that of an AI-enabled voice technology developed by iFlytek Co Ltd. And, for the record, unlike his granddaughter, Trump hardly knows any Chinese. The iFlytek technology demonstrated its speech synthesis capability - it can produce an unbelievably human-like voice.