Microsoft is releasing its Computational Network Toolkit (CNTK) on GitHub, making the very efficient AI tools used by its own researchers available to the broad developer and data science community. Xuedong Huang, Microsoft's Chief Speech Scientist, and his team were anxious to make faster improvements to how well computers can understand speech, and the tools they had to work with were slowing them down. So, a group of volunteers set out to solve this problem using a homegrown solution that stressed performance over all else. CNTK is the outcome of that project, and has proved more efficient than other popular computational toolkits used by developers to create deep learning models for speech and image recognition. Microsoft is internally using CNTK on a set of powerful computers that use graphics processing units, or GPUs.

In a major breakthrough for artificial intelligence, a computing system developed by Google researchers in Great Britain has beaten a top human player at the game of Go, the ancient Eastern contest of strategy and intuition that has bedeviled AI experts for decades. Machines have topped the best humans at most games held up as measures of human intellect, including chess, Scrabble, Othello, even Jeopardy!. But with Go--a 2,500-year-old game that's exponentially more complex than chess--human grandmasters have maintained an edge over even the most agile computing systems. Earlier this month, top AI experts outside of Google questioned whether a breakthrough could occur anytime soon, and as recently as last year, many believed another decade would pass before a machine could beat the top humans. But Google has done just that.

On March 15, DeepMind's AlphaGo, a computer powered by a self-learning artificial intelligence computer program, defeated Go grandmaster Lee Sedol. As the AI community celebrates this major milestone in making machines smart, the debate of "man vs. machine" is heating up. Over the past 25 years -- especially the last five years -- the AI community has transformed theoretical machine learning constructs to solve useful problems. AI techniques such as self-learning, reinforcement learning, and deep neural networks were developed to recognize traffic signs and classify images. The recent rapid progress in AI was powered by the dramatic increase in financial investments in AI.

Nvidia chief executive Jen-Hsun Huang announced that the company has created a new chip, the Tesla P100, with 15 billion transistors for deep-learning computing. It's the biggest chip ever made, Huang said. Huang made the announcement during his keynote at the GPUTech conference in San Jose, California. He unveiled the chip after he said that deep-learning artificial intelligence chips have already become the company's fastest-growing business. "We are changing so many things in one project," Huang said.

I built deepjazz in 36 hours for HackPrinceton, Spring 2016. It uses Keras & Theano, two deep learning libraries, to generate jazz music. Specifically, it builds a two-layer LSTM, learning from the given MIDI file. It uses deep learning, the AI tech that powers Google's AlphaGo and IBM's Watson, to make music -- something that's considered as deeply human.

Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness assumptions on the structure of the function to be modeled. To model complex and non-differentiable functions, these smoothness assumptions are often too restrictive. One way to alleviate this limitation is to find a different representation of the data by introducing a feature space. This feature space is often learned in an unsupervised way, which might lead to data representations that are not useful for the overall regression task. In this paper, we propose Manifold Gaussian Processes, a novel supervised method that jointly learns a transformation of the data into a feature space and a GP regression from the feature space to observed space. The Manifold GP is a full GP and allows to learn data representations, which are useful for the overall regression task. As a proof-of-concept, we evaluate our approach on complex non-smooth functions where standard GPs perform poorly, such as step functions and robotics tasks with contacts.

Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitation, in this work, we propose a recurrent attentional convolutional-deconvolution network (RACDNN). Using spatial transformer and recurrent network units, RACDNN is able to iteratively attend to selected image sub-regions to perform saliency refinement progressively. Besides tackling the scale problem, RACDNN can also learn context-aware features from past iterations to enhance saliency refinement in future iterations. Experiments on several challenging saliency detection datasets validate the effectiveness of RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection methods.

Hewlett Packard Enterprise (HPE) has announced a range of workload-optimised compute platforms and solutions to help boost its customers' innovation as they flock to high-performance computing applications. It means organisations using high-performance computing (HPC) solutions are able to use big data workloads to aid their modeling, simulation, high frequency trading and deep learning efforts. At the centre of its new offering, sits HPE's GPU-accelerated deep learning platform - Apollo 6500 - which runs on up to eight high performance NVIDIA GPU cards to offer improved learning systems to organisations that need to quickly model results without dramatically increased costs. It will be especially useful for those working in the financial sector, where scalable, big data applications are in demand to help process high volumes of data generated by real-time trading. The Apollo 4520 system is a dual-node system designed to lower costs for organisations that need to support parallel file system architectures as part of their HPC implementation. It can be used in conjunction with Lustre solutions either via an HPE supported Lustre solution, based on the Intel Enterprise Edition for Lustre software or Open Source Lustre with community support.

Nvidia chief executive Jen-Hsun Huang has ridden the game industry to glory in the graphics chip business. But now those chips are being used for more than just games. And Nvidia now has a software development kit for deep-learning A.I. developers. That software kit will enable developers to create better deep-learning applications to solve problems such as enabling self-driving cars to recognize pedestrians. Nvidia is creating its own deep-learning chip and technology for self-driving cars, Huang said in a keynote speech at the GPUTech conference in San Jose, California.

I have a decent conceptual understanding of deep learning, but I'm not much of a programmer (read: I know enough code to create graphs but not much else). I'd like to start doing deep learning rather than just reading/thinking about it. What's the quickest way for me to get started with deep learning?