South Korea will invest 2.2 trillion won (US$2 billion) in developing core artificial intelligence (AI) technology over five years with the aim of becoming a global powerhouse in the sector by 2022, the ICT ministry said Tuesday. Under the plan announced by the Ministry of Science and ICT, the South Korean government will spearhead a national program on AI, including research and development (R&D), to join the list of world powers. "The government believes obtaining AI core technologies by joining hands with private corporations will not only achieve global standard but eventually nurture talented people and quality jobs," Chang Byung-gyu, the head of a presidential committee to spearhead the fourth industrial revolution, told reporters in Seoul. "We aim to reach the global top four by 2022." The government said it aims to indigenously develop technologies on par with counterparts, led by the United States and China, and eventually reduce dependence on them.

The supervisory control of probabilistic discrete event systems (PDESs) is investigated under the assumptions that the supervisory controller (supervisor) is probabilistic and has a partial observation. The probabilistic P-supervisor is defined, which specifies a probability distribution on the control patterns for each observation. The notions of the probabilistic controllability and observability are proposed and demonstrated to be a necessary and sufficient conditions for the existence of the probabilistic P-supervisors. Moreover, the polynomial verification algorithms for the probabilistic controllability and observability are put forward. In addition, the infimal probabilistic controllable and observable superlanguage is introduced and computed as the solution of the optimal control problem of PDESs. Several examples are presented to illustrate the results obtained.

The analysis of complex systems such as inventory control, financial markets, waste-to-energy plants and computer networks is difficult because of the inherent uncertainties in these systems. Risk-aware optimization offers a possible remedy by giving stronger reliability guarantees than the risk-neutral case. Furthermore, it allows expression of the risk attitude of the decision maker. Risk awareness is especially important in sequential decision making because of the dynamic nature of the uncertainty. Markov decision processes (MDPs) introduced by Bellman in [10] provide a mathematical framework for modeling sequential decision making in situations where outcomes are partly random and partly under the control the decision maker. However, in many cases the exact model of the underlying Markov decision process is not known and one can only observe the trajectory of states, actions, and rewards/costs.

We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition. We retain the basic design of a fundamental algorithmic substrate called an "AI kernel" for problem solving and basic cognitive functions like memory, and a larger, modular architecture that re-uses the kernel in many ways. Omega includes eight representation languages and six classes of neural networks, which are briefly introduced. The architecture is intended to initially address data science automation, hence it includes many problem solving methods for statistical tasks. We review the broad software architecture, higher-order cognition, self-improvement, modular neural architectures, intelligent agents, the process and memory hierarchy, hardware abstraction, peer-to-peer computing, and data abstraction facility.

This paper develops a theory of clustering and coding which combines a geometric model with a probabilistic model in a principled way. The geometric model is a Riemannian manifold with a Riemannian metric, ${g}_{ij}({\bf x})$, which we interpret as a measure of dissimilarity. The probabilistic model consists of a stochastic process with an invariant probability measure which matches the density of the sample input data. The link between the two models is a potential function, $U({\bf x})$, and its gradient, $\nabla U({\bf x})$. We use the gradient to define the dissimilarity metric, which guarantees that our measure of dissimilarity will depend on the probability measure. Finally, we use the dissimilarity metric to define a coordinate system on the embedded Riemannian manifold, which gives us a low-dimensional encoding of our original data.

Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals weakly associated with outcomes among ultrahigh-dimensional predictors. However, existing screening methods, which typically ignore correlation information, are likely to miss these weak signals. By incorporating the inter-feature dependence, we propose a covariance-insured screening methodology to identify predictors that are jointly informative but only marginally weakly associated with outcomes. The validity of the method is examined via extensive simulations and real data studies for selecting potential genetic factors related to the onset of cancer.

Script event prediction requires a model to predict the subsequent event given an existing event context. Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. To remedy this, we propose constructing an event graph to better utilize the event network information for script event prediction. In particular, we first extract narrative event chains from large quantities of news corpus, and then construct a narrative event evolutionary graph (NEEG) based on the extracted chains. NEEG can be seen as a knowledge base that describes event evolutionary principles and patterns. To solve the inference problem on NEEG, we present a scaled graph neural network (SGNN) to model event interactions and learn better event representations. Instead of computing the representations on the whole graph, SGNN processes only the concerned nodes each time, which makes our model feasible to large-scale graphs. By comparing the similarity between input context event representations and candidate event representations, we can choose the most reasonable subsequent event. Experimental results on widely used New York Times corpus demonstrate that our model significantly outperforms state-of-the-art baseline methods, by using standard multiple choice narrative cloze evaluation.

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNN) in creating artistic imagery by separating and recombining image content and style. This process of using CNN to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. This review aims to provide an overview of the current progress towards NST, as well as discussing its various applications and open problems for future research.

A recent approach based on Bayesian inverse planning for the "theory of mind" has shown good performance in modeling human cognition. However, perfect inverse planning differs from human cognition during one kind of complex tasks due to human bounded rationality. One example is an environment in which there are many available plans for achieving a specific goal. We propose a "plan predictability oriented model" as a model of inferring other peoples' goals in complex environments. This model adds the bias that people prefer predictable plans. This bias is calculated with simple plan prediction. We tested this model with a behavioral experiment in which humans observed the partial path of goal-directed actions. Our model had a higher correlation with human inference. We also confirmed the robustness of our model with complex tasks and determined that it can be improved by taking account of individual differences in "bounded rationality".

In recent years data sets have grown in size and dimension with the proliferation of advanced data acquisition techniques. We have been able to use such data meaningfully not only because the computation power has increased to match the size, but also due to the paradigm shift in data analysis techniques that handle such data. A prime example is Machine Learning (ML). As a result new applications and techniques are emerging more frequently than ever before. Examples include object classification with applications in medicine (e.g., brain image analysis) and security (e.g., face classification) [1]. In many such applications, items in a data set are considered as points in some feature space of the underlying data, enabling us to interpret the data set as a "point cloud" in a suitably identified space. Even though, in certain cases the feature space is easily identifiable, in many other cases identifying a feature space could be a less obvious task.