Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information. We do this within an encoder-decoder sequence learning framework that integrates the following data: 1) offline geographical and social attributes. For example, the geographical structure of roads or public social events such as national celebrations; 2) road intersection information. In general, traffic congestion occurs at major junctions; 3) online crowd queries. For example, when many online queries issued for the same destination due to a public performance, the traffic around the destination will potentially become heavier at this location after a while. Qualitative and quantitative experiments on a real-world dataset from Baidu have demonstrated the effectiveness of our framework.

Deep learning might be one of the hottest topics in software development right now. With Polyaxon, managing large scale applications for deep learning and machine learning is easier than ever. Whether you're looking for full control of your data and cluster or simplifying resource management, Polyaxon makes it easier than ever to enable collaboration and stay independent while deploying deep learning applications. Polyaxon allows organizations to build, train, and monitor scalable apps for deep learning with its open source platform. It comes with a built-in set of tools and proven algorithms to let developers start innovating right out of the box.

A lot of companies are trying to make it easier to use artificial intelligence, but few are making it as simple as Lobe. The startup, which launched earlier this year, offers users a clean drag-and-drop interface for building deep learning algorithms from scratch. It's mainly focused on machine vision. That means if you want to build a tool that recognizes different houseplants or can count the number of birds in a tree, you can do it all in Lobe without typing a single line of code. Company co-founder Mike Matas told The Verge that Lobe isn't designed to compete with software used by machine learning professionals (tools like PyTorch and TensorFlow).

Every industry today is both impacted by and also seeking exploit AI technologies already widely used in the'new economy'. Tata Consultancy Services' "Business 4.0" framework guides this'digital transformation' of traditional enterprises by focusing on extreme personalization, developing ecosystems and embracing risk to drive exponential value: Enterprise AI developed with agility and deployed on the cloud form the critical technical enablers for Business 4.0. In his talk, Gautam Shroff guides attendees through how TCS Research is applying AI in traditional enterprises spanning the spectrum from automation to amplification, beginning with its hands-on experience of developing and deploying a deep-learning based semantic system for virtual assistance as well as knowledge synthesis within TCS, at scale, on its internal collaboration platform. From the transformative effects of AI and IOT on manufacturing to supply change management, and the necessity of embracing risk for deploying AI in the field, come explore the intersection of AI and business in this special lecture on June 12th at 4:00 pm in Room 1190 of the Bahen Centre, 40 St. George St., Toronto.

The NVIDIA Deep Learning SDK offers powerful tools and libraries to data scientist to design and deploy deep learning applications. It contains solutions for both neural network training and in inference. Introduced in 2016, the SDK requires CUDA toolkit for building new GPU-accelerated DL algorithms. It includes libraries for deep learning primitives, inference, video analytics, linear algebra, sparse matrices and multi-GPU communications. According to NVIDIA, the kit brings high-performance GPU acceleration to widely used deep learning frameworks such as TensorFlow, Caffe, Theano and Torch.

Jeremy Howard was pioneering ways for deep learning to help physicians interpret medical data better when the challenge he was tackling suddenly hit close to home. When Jeremy Howard's wife, Rachel, was diagnosed with a brain cyst while she was pregnant with their first child three years ago, Jeremy and Rachel did what comes naturally to data scientists like them. It contained: possible treatments, their known likelihood of success and failure, the value they assigned to different outcomes, and the potential problems if things went wrong. When most of us fall ill, we find ourselves thrust into a world of frantic Googling, confusing choices, and fear of the unknown. We place trust in our doctors to know what's best. When it comes to making decisions, from investing money, to raising children, to taking medicine, he uses probabilities, priors and statistics.

IBM, NVIDIA, and the U.S. Department of Energy (DOE) recently announced that they have completed testing the world's fastest supercomputer, Summit, at the Oak Ridge National Laboratory in Oak Ridge, Tennessee. Capable of over 200 petaflops (200 quadrillion operations per second), Summit consists of 4600 IBM dual socket Power 9 nodes, connected by over 185 miles of fiber optic cabling. Each node is equipped with 6 NVIDIA Volta TensorCore GPUs, delivering total throughput that is 8 times faster than its predecessor, Titan, for double precision tasks, and 100 times faster for reduced precision tasks common in deep learning and AI. China has held the top spot in the Top 500 for the last 5 years, so this brings the virtual HPC crown home to the USA. Figure 1: The Summit Supercomputer at the Department of Energy's Oak Ridge National Labs is now the fastest computer in the world. Some of the specifications are truly amazing; the system exchanges water at the rate of 9 Olympic pools per day for cooling, and as an AI supercomputer, Summit has already achieved (limited) "exascale" status, delivering 3 exaflops of AI precision performance.

The longevity and biotechnology industries are focusing on aging in a big way, and it's beginning to show. The fields of Artificial Intelligence (AI) and regenerative medicine are putting their money on combating aging and age-related diseases, and the benefits are likely to be immense. While biotechnology and AI are relatively new concepts, the announcements of funding and collaboration yesterday by and between three companies are bringing those concepts that much closer to the forefront of medicine. Insilico Medicine, a Baltimore-based next-generation AI company specializing in the application of deep learning for target identification, drug discovery and aging research, yesterday announced a collaboration agreement with WuXi AppTec, a leading global contract research outsourcing provider based in Shanghai, China, serving the pharmaceutical, biotech, and medical device industries. "It's a big step not only for Insilico Medicine but for AI and the pharmaceutical industries," said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.

You can find more info and examples on the Github repo linked above. Magnitude is "a fast, simple vector embedding utility library." A feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. It is primarily intended to be a simpler / faster alternative to Gensim, but can be used as a generic key-vector store for domains outside NLP. The repo provides links to a variety of popular embedding models which have been prepared in the .magnitude

Sentient Analytics helps improve agent performance by providing detailed customer emotion insights over time and call topics. Sentient Analytics highlights important events and hidden trends in all calls to ensure attention is paid on where it's most needed to provide excellent service. Sentient Analytics highlights what customers are complaining about and why they are calling. Drill-down allows you to search every call by keyword. "Sentient Machines uses deep learning to bring customer understanding to the call center industry." "This startup uses speech-to-text software to help call centres speed up their work.