Stanford graduate student Kaidi Cao will join fellow AI researchers Jing Liao, of City University of Hong Kong, and Lu Yuan of Microsoft at SIGGRAPH Asia in Tokyo this December to present their incredible caricature-drawing neural network. That's not bad, considering Cao was only an intern at the Visual Computing Group at the Microsoft Research Lab in Beijing when he worked on the project. The AI, actually a pair of generative adversarial networks (GAN), is called CariGANs. The first of its neural networks, CariGeoGAN, determines the geometry of a face in a photograph and maps it to a caricature model. CariStyGAN, the other half of CariGANs, does the "style transfer," or applies the artistic look to the geometry map.

In a webinar hosted by the MIT Sloan Management Review, Gerbert cited numbers from a new report, "Global Competition with AI in Business: How China Differs." The report said 91% of Chinese companies have increased their spending on AI projects since last year. In the U.S., that number is 74%. Within the last three years, 60% of Chinese companies have adjusted their business models to orient their operations around AI, while only 53% of U.S. companies have followed suit. Much of these changes in China's business realm are driven by the country's government.

Tumblr's app has disappeared on the iPhone App Store after child sex abuse images were found on its platform. The company says it is acting urgently to remove the content and to have the app restored onto iOS. The app mysterious disappeared last week, being removed from the App Store with no announcement. While that didn't take it from existing phones, it meant that the app would not update and that nobody could download it newly. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona.

You might not even be used to 4K, which – along with other new technologies like HDR – feels like it only just arrived and it is still expensive to buy the sets and difficult to find content to watch on them. But 4K is already old news, after Samsung revealed its first 8K TV meant for consumers – doubling the name, quadrupling the number of pixels on offer and heralding the future of screens. Samsung's newest television crams a full 33 million pixels into a vast display that it hopes becomes the ultimate way of watching the TV. Despite how it might sound, 8K is actually four times as many pixels as existing TVs, and offers what the company says is a significant improvement and innovation on that technology. But the 8K TV isn't only the consequence of wanting to cram in more pixels or to add bigger numbers to specifications.

Parallel tempering addresses some of the drawbacks of canonical Markov Chain Monte-Carlo methods for Bayesian neural learning with the ability to utilize high performance computing. However, certain challenges remain given the large range of network parameters and big data. Surrogate-assisted optimization considers the estimation of an objective function for models given computational inefficiency or difficulty to obtain clear results. We address the inefficiency of parallel tempering for large-scale problems by combining parallel computing features with surrogate assisted estimation of likelihood function that describes the plausibility of a model parameter value, given specific observed data. In this paper, we present surrogate-assisted parallel tempering for Bayesian neural learning where the surrogates are used to estimate the likelihood. The estimation via the surrogate becomes useful rather than evaluating computationally expensive models that feature large number of parameters and datasets. Our results demonstrate that the methodology significantly lowers the computational cost while maintaining quality in decision making using Bayesian neural learning. The method has applications for a Bayesian inversion and uncertainty quantification for a broad range of numerical models.

Abstract--Crude oil is a major component in most advanced economies of the world. Accurately predicting and understanding thebehavior of crude oil prices is important for economists, analysts, forecasters, and traders, to name a few. The price of crude oil has declined in the past decade and is seeing a phase of stability; but will this stability last? This work is an empirical study on how multivariate analysis may be employed to predict crude oil spot prices using neural networks. The concept of using neural networks showed promising potential. A very simple neural network model was able to perform on par with ARIMA models - the state-of-the-art model in time-series forecasting. Advanced neural network models using larger datasets may be used in the future to extend this proofof-concept toa full scale framework. I. INTRODUCTION Crude oil spot prices saw a tremendous uptick in the first decade of the 21 Since 2014, crude oil prices have fallen and may have stabilized now. However, there has always been a constant interest in accurately predicting crude oil prices; given that crude oil drives a major portion of the economy. Economists, scientists, data analysts, and traders are all interested in models that give them the best accuracy.

One of the most important applications of seismic reflection is the hydrocarbon exploration which is closely related to salt deposits analysis. This problem is very important even nowadays due to it's non-linear nature. Taking into account the recent developments in deep learning networks TGS-NOPEC Geophysical Company hosted the Kaggle competition for salt deposits segmentation problem in seismic image data. In this paper, we demonstrate the great performance of several novel deep learning techniques merged into a single neural network which achieved the 27th place (top 1%) in the mentioned competition. Using a U-Net with ResNeXt-50 encoder pre-trained on ImageNet as our base architecture, we implemented Spatial-Channel Squeeze & Excitation, Lovasz loss, CoordConv and Hypercolumn methods. The source code for our solution is made publicly available at https://github.com/K-Mike/Automatic-salt-deposits-segmentation.

Core-selection is a crucial property of rules in the literature of resource allocation. It is also desirable, from the perspective of mechanism design, to address the incentive of agents to cheat by misreporting their preferences. This paper investigates the exchange problem where (i) each agent is initially endowed with (possibly multiple) indivisible goods, (ii) agents' preferences are assumed to be conditionally lexicographic, and (iii) side payments are prohibited. We propose an exchange rule called augmented top-trading-cycles (ATTC), based on the original TTC procedure. We first show that ATTC is core-selecting and runs in polynomial time with respect to the number of goods. We then show that finding a beneficial misreport under ATTC is NP-hard. We finally clarify relationship of misreporting with splitting and hiding, two different types of manipulations, under ATTC.

Over recent years, deep learning has become a mainstream method in machine learning. More advanced networks are being actively developed to solve real-world problems in many important areas. Among successful features of network architectures, shortcut connections are well established to take the outputs of earlier layers as the inputs to later layers, and produce excellent results such as in ResNet and DenseNet. Despite the power of shortcuts, there remain important questions on the underlying mechanism and associated functionalities. For example, will adding shortcuts lead to a more compact structure? How to use shortcuts for an optimal efficiency and capacity of the network model? Along this direction, here we demonstrate that given only one neuron in each layer, the shortcuts can be sparsely placed to let the slim network become an universal approximator. Potentially, our theoretically-guaranteed sparse network model can achieve a learning performance comparable to densely-connected networks on well-known benchmarks.

Safe, controllable and credible artificial intelligence has been the goal which the humanity has been pursuing. In the field of machine learning, in order to achieve this goal, it is necessary for learning algorithm to really interact with the humanity; It is necessary for the learning algorithm to have the ability to correct errors, so as to avoid a prediction model with serious errors caused by unnecessary deviation in training data; It needs to be able to check its own learning process or decision-making process based on unsuccessful prediction results, especially for complex learning tasks; It is necessary to establish a learning algorithm for capturing and learning causal relationships in the world around us, so that the prediction model could predict what will happen under certain conditions, even if these conditions are significantly different from those of the past; It needs the learning algorithm which can really take full control of generalization performance of the prediction model. As big data accelerates transformation of scientific research pattern, scientific research is translating from a hypothetical drive mode to a data-driven one, which needs learning algorithm to discover new natural phenomena and laws through big data mining, statistic and analysis. However, recently, all of this is out of reach. The reason is that the prediction model and its training process are not yet understood by human beings, and are not covered by the knowledge base we currently have.