Go master Cho Chikun triumphed Wednesday in his final game against DeepZenGo, a Japanese artificial intelligence system, to win the three-game series 2-1. Cho, 60, has won 74 titles, the largest number in Japan, over a long career. He defeated DeepZenGo with the 167th move in the third game of the series, which was played on even terms with no handicaps. DeepZenGo uses deep learning and other advanced technologies. It is being developed with support mainly from Dwango Co., a video-sharing website operator, and the University of Tokyo. "I felt as if I was playing with a human, because (DeepZenGo) has both strong and weak points," Cho said after the final game.

Soon after the invention of photography, a few criminologists began to notice patterns in mugshots they took of criminals. Offenders, they said, had particular facial features that allowed them to be identified as law breakers. One of the most influential voices in this debate was Cesare Lombroso, an Italian criminologist, who believed that criminals were "throwbacks" more closely related to apes than law-abiding citizens. He was convinced he could identify them by ape-like features such as a sloping forehead, unusually sized ears and various asymmetries of the face and long arms. Indeed, he measured many subjects in an effort to prove his view although he did not analyze his data statistically.

Amazon.com, Inc.'s (AMZN) dominant cloud service Amazon Web Services (AWS) is now becoming more intelligent. But how advanced will AWS prove to be when compared with Microsoft Corporation's (MSFT) competing platform Azure? Over the past week, we've chronicled the advances and advantages these platforms have tried to create over each other. Microsoft has even joined the foundation of Linux, its once-hated rival, with the hope of boosting the adoption of Azure. Amazon is now taking AWS a step further and is reportedly working on a new cloud service focused on a branch of artificial intelligence called deep learning or machine learning (ML), which helps train computers to recognize speech, images and objects.

The list of activities in which artificial intelligence machines have bested humans is increasing at an alarming rate. Face recognition, object recognition, chess, Go, various video games, and numerous other tasks have all fallen in this battle. So it's natural to ask about the types of tasks that machines still have difficulty with. Where do humans still rule the roost? Today, we get an answer of sorts thanks to the work of Mohit Iyyer at the University of Maryland in College Park and a few pals.

We survey results on neural network expressivity described in "On the Expressive Power of Deep Neural Networks". The paper motivates and develops three natural measures of expressiveness, which all display an exponential dependence on the depth of the network. In fact, all of these measures are related to a fourth quantity, trajectory length. This quantity grows exponentially in the depth of the network, and is responsible for the depth sensitivity observed. These results translate to consequences for networks during and after training. They suggest that parameters earlier in a network have greater influence on its expressive power -- in particular, given a layer, its influence on expressivity is determined by the remaining depth of the network after that layer. This is verified with experiments on MNIST and CIFAR-10. We also explore the effect of training on the input-output map, and find that it trades off between the stability and expressivity.

Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, including both linear ones and nonlinear ones, supervised ones and unsupervised ones. Particularly, deep architectures are widely applied for representation learning in recent years, and have delivered top results in many tasks, such as image classification, object detection and speech recognition. In this paper, we review the development of data representation learning methods. Specifically, we investigate both traditional feature learning algorithms and state-of-the-art deep learning models. The history of data representation learning is introduced, while available resources (e.g. online course, tutorial and book information) and toolboxes are provided. Finally, we conclude this paper with remarks and some interesting research directions on data representation learning.

Complex nonlinear models such as deep neural network (DNNs) have become an important tool for image classification, speech recognition, natural language processing, and many other fields of application. These models however lack transparency due to their complex nonlinear structure and to the complex data distributions to which they typically apply. As a result, it is difficult to fully characterize what makes these models reach a particular decision for a given input. This lack of transparency can be a drawback, especially in the context of sensitive applications such as medical analysis or security. In this short paper, we summarize a recent technique introduced by Bach et al. [1] that explains predictions by decomposing the classification decision of DNN models in terms of input variables.

We address a question answering task on real-world images that is set up as a Visual Turing Test. By combining latest advances in image representation and natural language processing, we propose Ask Your Neurons, a scalable, jointly trained, end-to-end formulation to this problem. In contrast to previous efforts, we are facing a multi-modal problem where the language output (answer) is conditioned on visual and natural language inputs (image and question). We provide additional insights into the problem by analyzing how much information is contained only in the language part for which we provide a new human baseline. To study human consensus, which is related to the ambiguities inherent in this challenging task, we propose two novel metrics and collect additional answers which extend the original DAQUAR dataset to DAQUAR-Consensus. Moreover, we also extend our analysis to VQA, a large-scale question answering about images dataset, where we investigate some particular design choices and show the importance of stronger visual models. At the same time, we achieve strong performance of our model that still uses a global image representation. Finally, based on such analysis, we refine our Ask Your Neurons on DAQUAR, which also leads to a better performance on this challenging task.

Reuters – OpenAI, the non-profit artificial intelligence research firm backed by Tesla Motors Inc's Elon Musk and other prominent tech executives, has signed an agreement to run most of its large-scale experiments on Microsoft Corp's flagship cloud service, Azure. OpenAI will use Azure for its experiments in deep learning and AI, and Microsoft will collaborate with the company on advancing research and creating new tools and technologies. Musk, along with venture capitalist Sam Altman, co-chairs OpenAI, whose backers apart from Musk include Amazon Web Services and tech investor Peter Thiel and LinkedIn Corp co-founder Reid Hoffman. OpenAI is an early adopter of Microsoft's Azure N-Series Virtual Machines service, which will be generally available from December. These cloud-computing services, which are powered by Nvidia Corp graphics chips, are designed for the most intensive computing workloads, including deep learning and simulations.

Dr. Damian Borth is the Director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern and founding co director of Sociovestix Labs, a social enterprise in the area of financial data science. Damian's research focuses on large scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams. His work has been awarded by the Best Paper Award at ACM ICMR 2012, the McKinsey Business Technology Award 2011, and a Google Research Award in 2010. Damian currently serves as a member of the assessment committee for the Investment Innovation Benchmark (IIB) and several other steering and program committees of international conferences and workshops. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.