It is intuitive that NLP tasks for logographic languages like Chinese should benefit from the use of the glyph information in those languages. However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found. In this paper, we address this gap by presenting the Glyce, the glyph-vectors for Chinese character representations. We make three major innovations: (1) We use historical Chinese scripts (e.g., bronzeware script, seal script, traditional Chinese, etc) to enrich the pictographic evidence in characters; (2) We design CNN structures tailored to Chinese character image processing; and (3) We use image-classification as an auxiliary task in a multi-task learning setup to increase the model's ability to generalize. For the first time, we show that glyph-based models are able to consistently outperform word/char ID-based models in a wide range of Chinese NLP tasks. Using Glyce, we are able to achieve the state-of-the-art performances on 13 (almost all) Chinese NLP tasks, including (1) character-Level language modeling, (2) word-Level language modeling, (3) Chinese word segmentation, (4) name entity recognition, (5) part-of-speech tagging, (6) dependency parsing, (7) semantic role labeling, (8) sentence semantic similarity, (9) sentence intention identification, (10) Chinese-English machine translation, (11) sentiment analysis, (12) document classification and (13) discourse parsing

Learning to predict solutions to real-valued combinatorial graph problems promises efficient approximations. As demonstrated based on the NP-hard edge clique cover number, recurrent neural networks (RNNs) are particularly suited for this task and can even outperform state-of-the-art heuristics. However, the theoretical framework for estimating real-valued RNNs is understood only poorly. As our primary contribution, this is the first work that upper bounds the sample complexity for learning real-valued RNNs. While such derivations have been made earlier for feed-forward and convolutional neural networks, our work presents the first such attempt for recurrent neural networks. Given a single-layer RNN with $a$ rectified linear units and input of length $b$, we show that a population prediction error of $\varepsilon$ can be realized with at most $\tilde{\mathcal{O}}(a^4b/\varepsilon^2)$ samples. We further derive comparable results for multi-layer RNNs. Accordingly, a size-adaptive RNN fed with graphs of at most $n$ vertices can be learned in $\tilde{\mathcal{O}}(n^6/\varepsilon^2)$, i.e., with only a polynomial number of samples. For combinatorial graph problems, this provides a theoretical foundation that renders RNNs competitive.

TL;DR: Despite its ubiquity in deep learning, Tensor is broken. It forces bad habits such as exposing private dimensions, broadcasting based on absolute position, and keeping type information in documentation. This post presents a proof-of-concept of an alternative approach, named tensors, with named dimensions. This change eliminates the need for indexing, dim arguments, einsum- style unpacking, and documentation-based coding. The prototype PyTorch library accompanying this blog post is available as namedtensor.

Big data is changing the future of the construction industry in the United States. Many industry experts and data scientists have talked about some of the ways that big data has changed the industry. Rachel Berger has said that big data has played a role in everything from improving CAD designs to streamlining invoicing. However, there are other benefits of using big data that don't get as much attention. One factor is the use of snag lists.

A few months back, we gave our take on a survey from the O'Reilly folks regarding interest in deep learning. The survey reported that interest was more than latent, but there's little question that the bulk of the action today is in the (relatively) better understood confines of machine learning (ML). So on this go round, O'Reilly jumped into the shallower side of the pond to survey the people who subscribe to its publications and go to its big data-related Strata and AI conferences regarding ML. Before diving in, let's put some perspective on this cohort: it's likely a group that on average is ahead of the curve by virtue of its attendance at these big data events or consumption of O'Reilly learning services that are skewing increasingly toward the AI domain. Nonetheless, it provides a useful counterpoint to their earlier work exploring interest in deep learning.

Summary: There are two definitions currently in use for AI, the popular definition and the data science definition and they conflict in fundamental ways. If you're going to explain or recommend AI to a non-data scientist, it's important to understand the difference. For a profession as concerned with accuracy as we are, we do a really poor job at naming things, or at least being consistent in the naming. "Big Data" – totally misleading (since it incorporates velocity and variety in addition to volume). How many times have you had to correct someone on that?

The Helsinki Doctoral Education Network in Information and Communications Technology (HICT) is a joint initiative by Aalto University and the University of Helsinki, the two leading universities within this area in Finland. The network involves at present over 60 professors and over 200 doctoral students, and the participating units graduate altogether more than 40 new doctors each year. The quality of research and education in both HICT universities is world-class, and the education is practically free as there are no tuition fees for doctoral students in the Finnish university system. In terms of the living environment, Helsinki has been ranked as one of the world's top-10 most livable cities (Economist, 2017), and Finland is among the best countries in the world with respect to many quality of life indicators, including being the overall #1 country in human wellbeing. Helsinki is in the second place in the world's startup city comparison (Valuer, 2018) and is also the Mobile Data Capital of the World (IEEE Spectrum, 2018). The participating units of HICT have currently funding available for exceptionally qualified doctoral students. We offer the possibility to join world-class research groups, with multiple interesting research projects to choose from. If you wish to be considered as a potential new doctoral student in HICT you can apply to one or a number of doctoral student positions (listed below).

Unity, a leading maker of game development tools, announced today that it's created a new, unprecedented type of video game: one designed not to be played by humans, but by artificial intelligence. The game is called Obstacle Tower, and it's a piece of software created to judge the level of sophistication of an AI agent by measuring how efficiently it can maneuver up to 100 levels that change and scale in difficulty in unpredictable ways. Each level is procedurally generated, so it changes every time the AI attempts it. With Obstacle Tower, and a $100,000 pool of prizes set aside for participants to claim as part of a contest, Unity hopes it can provide AI researchers with a new benchmarking tool to evaluate self-learning software. "We wanted to give the researchers something to really work with that would to an extreme degree challenge the abilities of the AI systems that are currently in research and development around the world," Danny Lange, Unity's vice president of AI and machine learning, told The Verge.

AI, the impact of which is simultaneously over-hyped and under-rated in most sectors, is catalyzing revolutionary use cases in healthcare. Deep learning has come a long way from identifying cats and dogs and can now perform independent image-based diagnosis with comparable or better accuracy than (human) doctors. X-Ray or scan-based diagnosis of tumours, fractures, strokes, electrodiagnosis can be entirely done by algorithms. While several of these options have received FDA approval and starting a clinical trial or beginning to be used in production, researchers are attempting selfie diagnosis to detect 50 diseases from abnormalities in eye colour (Nature,2018) and diagnosis from (molecules emitted from) body odour. Using data from wearables or behavioural observations such as changes in gait, driving patterns, mouse usage etc., machine learning algorithms can predict the onset of physiological (particularly neurological or cardiovascular) or psychiatric disorders, assist management of chronic conditions such as diabetes or epilepsy, and even raise real-time warnings.

Studies show that lots of Americans are worried that AI is coming for their jobs -- Uber and Lyft drivers, couriers, receptionists, even software engineers. A remarkable exhibition match today suggested that another group that should be worried is ... pro video gamers? In a stunning demonstration of how far AI capabilities have come, AlphaStar -- a new AI system from Google's DeepMind -- competed with pro players in a series of competitive StarCraft games. StarCraft is a complicated strategy game that requires players to consider hundreds of options at any given moment, to make strategic choices with payoffs a long way down the road, and to operate in a fast-changing environment with imperfect information. More than 200,000 games are played every day.