So if you're looking to learn one of the most desirable skills in tech, you've come to the right place. We've already gone over the top machine learning libraries and open source projects, so now we're taking a close look at frameworks. Developed by a team at the National University of Singapore, Apache Singa is a flexible and scalable deep learning platform for big data analytics. This deep learning framework provides a flexible architecture for scalable distributed training on large volumes of data. Singa is extensible to run over a wide range of hardware.

Intel today unveiled "Intel AI: In Production," a new program that makes it easier for developers to bring their artificial intelligence prototypes to market. Since its introduction last July, the Intel Movidius Neural Compute Stick (NCS) has gained a developer base in the tens of thousands. Once developers have a prototype, the next step is to take it into production, which can be challenging and costly for small companies and entrepreneurs. To make it easier, Intel selected AAEON Technologies, a leading manufacturer of advanced industrial and embedded computing platforms, as the first Intel AI: In Production partner. Through the program, AAEON provides two streamlined production paths for developers integrating the low-power Intel Movidius Myriad 2 Vision Processing Unit (VPU) into their product designs.

In this blog I aim to provide an introduction to TensorFlow and the SAP HANA integration, give you an understanding of the landscape and outline the process for using External Machine Learning with HANA. There's plenty of hype around Machine Learning, Deep Learning and of course Artificial Intelligence (AI), but understanding the benefits in an enterprise context can be more challenging. Being able to integrate the latest and greatest deep learning models into your enterprise via a high performance in-memory platform could provide a competitive advantage or perhaps just keep up with the competition? With HANA 2.0 SP2 onwards we have the ability to call TensorFlow (TF) models or graphs as they are known. HANA now includes a method to call External Machine Learning (EML) models via a remote source.

The minute hand on the robot apocalypse clock just inched a little closer to midnight. DeepMind, the Google sister-company responsible for the smartest AI on the planet, just taught machines how to figure things out for themselves. AI that only exists to parse data, such as neural networks that decide whether something is a hotdog or not, have relatively little to concentrate on compared to the near-infinite number of things a physical robot has to figure out. To solve this problem DeepMind built a new learning paradigm for AI-powered robots called'Scheduled Auxiliary Control (SAC-X).' This new paradigm gives robots a simple goal like'clean up this playground' and rewards it for completion. The auxiliary tasks we define follow a general principle: they encourage the agent to explore its sensor space.

Recently Google DeepMind program AlphaGo Zero achieved superhuman level without any help - entirely by self-play! Here is the Nature paper explaining technical details (also PDF version: Mastering the Game of Go without Human Knowledge) One of the main reasons for success was the use of a novel form of Reinforcement learning in which AlphaGo learned by playing itself. The system starts with a neural net that does not know anything about Go. It plays millions of games against itself and tuned the neural network to predict next move and the eventual winner of the games. The updated neural network was merged with the Monte Carlo Tree Search algorithm to create a new and stronger version of AlphaGo Zero, and the process resumed.

January 25 & 26 in San Francisco will see the sixteenth global Deep Learning Summit and the fifth global AI Assistant Summit joined by the first ever Deep Learning for Enterprise Summit. Use code KDNUGGETS to save 20% on Early Bird passes!

According to dual process theory human reasoning consists of two different kinds of thinking. System 1 is a fast, unconscious and automatic mode of thought, also known as intuition. System 2 is a slow, conscious, explicit and rule-based mode of reasoning that is believed to be an evolutionarily recent process. When learning to complete a challenging planning task, such as playing a board game, humans exploit both processes: strong intuitions allow for more effective analytic reasoning by rapidly selecting interesting lines of play for consideration. Repeated deep study gradually improves intuitions.

Graphcore emerged from stealth mode today with news of a $30 million Series A round to help finance ongoing development of its machine learning (ML) and deep learning acceleration solutions, including a PCIe card that plugs directly into a server's bus. The company says the combination of its development framework, called Poplar, and its PCIe-based Intelligent Processing Unit (IPU) can speed up ML and deep learning workloads by 10x to 100x. The IPU card plugs into the PCI buses of standard X86 servers to provide a processing boost. Armed with multiple IPU cards, a company could enjoy the benefits of "massively parallel, low-precision floating-point compute" at "much higher compute densities" than other solutions. Graphcore is positioning its IPU cards to take on the workloads that some are looking to run on more exotic hardware, such as graphics processing units (GPUs) or field programmable gate arrays (FPGAs).

Boston biotech FDNA has teamed up with several research organizations to create a consortium that will try to apply artificial intelligence and machine learning to the development of new medicines--and it's looking for other partners. The Genomics Collaborative launched with the aim of using computational techniques to analyze genotype and phenotype data and try to tease out physiological relationships that could lead to new drug targets and, it says, "help millions of undiagnosed patients globally". FDNA said it is making its AI and "deep learning" technologies--which can analyze (anonymously) data gleaned from diverse sources such as images, clinical notes and voice and video recordings--to organizations signing up to the program. So far, the offer has enticed South Carolina's Greenwood Genetic Center (GGC), Lausanne University Hospital in Switzerland and Seattle Children's Hospital to start research projects using FDNA's next-generation phenotyping or NGP platform. Two patient advocacy groups--Bridge the Gap representing patients with Fragile X, Angelman and other related syndromes as well as Kabuki syndrome group All Things Kabuki--have also come on board.

You are free to share this article under the Attribution 4.0 International license. Neural networks could pave the way for smarter, safer, and more sustainable cities by better predicting demand for taxi and ride-sharing services. In a study, the researchers used two types of neural networks--computational systems modeled on the human brain--that analyzed patterns of taxi demand. This deep learning approach, which lets computers learn on their own, could then predict the demand patterns significantly better than current technology. "Ride sharing companies, like Uber in the United States, and Didi Chuxing in China, are becoming more and more popular and have really changed the way people approach transportation," says Jessie Li, associate professor of information sciences and technology at Penn State.