Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Intel's $1 billion investment in the AI ecosystem is one of the well-publicized talking points at the processor company. The Intel empire boasts a breadth of AI technologies it has amassed by acquisition and Intel Capital investments in AI startups. The acquired companies seemingly useful to Intel's AI ambitions thus far include Altera (2015), Saffron (2015), Nervana (2016), Movidius (2016) and Mobileye (2017). Intel Capital has also fattened its AI portfolio with startups Mighty AI, Data Robot, Lumiata, CognitiveScale, Aeye Inc., Element AI and others. Unclear is how Intel is going to stitch all this together.

I recently completed all available material (as of October 25, 2017) for Andrew Ng's new deep learning course on Coursera. I found all 3 courses extremely useful and learned an incredible amount of practical knowledge from the instructor, Andrew Ng. Ng does an excellent job of filtering out the buzzwords and explaining the concepts in a clear and concise manner. For example, Ng makes it clear that supervised deep learning is nothing more than a multidimensional curve fitting procedure and that any other representational understandings, such as the common reference to the human biological nervous system, are loose at best. The specialization only requires basic linear algebra knowledge and basic programming knowledge in Python.

One of the main user complaints about TensorFlow was the constraint imposed by having to structure your computations as a static graph. Relaxing this requirement was one of my projects when I was at Google Brain, eventually open-sourced as imperative mode. However it relied on private/unstable APIs which became too costly to maintain over time. Luckily, PyTorch coming out crystallized researcher needs/wants, and there has been a concerted effort to support this kind of mode as a first-class citizen. It's still under active development but the version available in nightly release is quite usable, to try it out: Note that there's no longer need to deal with graph or session and execution happens immediately.

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.

Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities. One of the hottest topics in deep learning is GANs, which have the potential to create systems that learn more with less help from humans. Rather than train a single neural network to recognize pictures, researchers train two competing networks. The sparring networks learn from each other. As one works hard to find fake images, for example, the other gets better at creating fakes that are indistinguishable from the originals.

The content reconstructions from lower layers (a,b,c) are almost exact replicas of the original image. In the higher layers of the network however, the detailed pixel information is lost while the high-level structures and details remain the same (d,e). Then, the style representation draws connections between the different features in different layers of the CNN. This creates images that match the style on an increasing scale as you move through the network's hierarchy.

HPE has just announced a bunch of new things to help organisations everywhere tap into AI. The new offerings include an integrated hardware-software solution, a set of guiding tools, a research collaboration platform, and a place to get access to the latest expertise. Deep Learning, as a subset of AI, is key for things like facial or voice recognition, image classification or other challenging tasks. It requires a high performance compute infrastructure to build and train learning models that can handle vasts amount of data, and that is something many organisations lack. This is also the core problem HPE is trying to solve with its new solution.

Nvidia said it has expanded partnerships and added industry-specific courses to teach deep learning and artificial intelligence under its Deep Learning Institute effort. The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started. The Deep Learning Institute launched about a year ago, with the aim of teaching new skills to data scientists, students and developers. Under the expansion, the Deep Learning Institute (DLI) will partner with Booz Allen Hamilton and deeplearning.ai

When I was beginning my journey in data science, I often faced the problem of choosing the most appropriate algorithm for my specific problem. If you're like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The paradox is that this doesn't make it easier to choose which one to use. In this article for Statsbot, I will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms for different tasks. At the end of the article, you'll find a structured overview of the main features of described algorithms.