AI good for internal back office and some limited front office activities; however, still need to see more adoption of QC in the Net and infrastructure in companies to expose their services and information to the public net & infrastructure. Deep learning, as explained by tech journalist Michael Copeland on Blogs.nvidia.com, is the newest and most powerful computational development thus far. It combines all prior research in artificial intelligence (AI) and machine learning. At its most fundamental level, Copeland explains, deep learning uses algorithms to peruse massive amounts of data, and then learn from that data to make decisions or predictions. The Defense Agency Advanced Project Research (DARPA), as Wired reports, calls this method "probabilistic programming."

The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) Having read, watched, and presented deep learning material over the past few years, I have to say that this is one of the best collection of introductory deep learning talks I've yet encountered. Torch Tutorial (Alex Wiltschko, Twitter) - https://youtu.be/L1sHcj3qDNc 11.

Deep learning, as explained by tech journalist Michael Copeland on Blogs.nvidia.com, is the newest and most powerful computational development thus far. It combines all prior research in artificial intelligence (AI) and machine learning. At its most fundamental level, Copeland explains, deep learning uses algorithms to peruse massive amounts of data, and then learn from that data to make decisions or predictions. The Defense Agency Advanced Project Research (DARPA), as Wired reports, calls this method "probabilistic programming." Mimicking the human brain's billions of neural connections by creating artificial neural networks was thought to be the path to AI in the early days, but it was too "computationally intensive."

From Chess to Go, board games have been the first frontier of artificial intelligence research for decades. Now, the team at Google's DeepMind wants to take AI to a whole new level in order to beat the online strategy game, StarCraft II. DeepMind announced its decision to partner with StarCraft's creator, Blizzard, at a conference in California. The two groups say that they look forward to programming a computer to react to strategic problems in real time. "DeepMind is on a scientific mission to push the boundaries of AI, developing programs that can learn to solve any complex problem without needing to be told how," wrote DeepMind in a blog post.

I recently attended the ReWork Deep Learning conference in London, a fantastic event bringing world leading academics, large multinationals and start-ups together to discuss the latest advances in this branch of artificial intelligence and how it can improve our lives. Although very few applications include AI at present, it is predicted to increase exponentially over the next few years as the technology currently in development gets released for general use. While there were many fascinating talks about advances in healthcare and construction, there were two distinct themes that are relevant to marketers that ran through almost all the talks I attended, and there is no doubt that both issues will need addressing soon. The first of the issues was the moral and legal agency of AI applications. If your doctor misdiagnoses you, you have somewhere to go to complain and have the potential for compensation.

I am very much torn on how smart this move is from many viewpoints. It makes sense, but it is definitely playing with fire.

Has a tumor shrunk during the course of treatment over several months, or have new tumors developed? To answer questions like these, physicians often perform CT and MRI scans. Tumors are usually evaluated only visually, and new tumors are often over- looked. "Our program package increases confidence during tumor measurement and follow-up," explains Mark Schenk from the Fraunhofer Institute for Medical Image Computing MEVIS in Bremen, Germany. "The software can, for example, determine how the volume of a tumor changes over time and supports the detection of new tumors."

The Google logo is displayed at the Google headquarters in Mountain View, California. Blizzard and Google are inviting developers to experiment with artificial intelligence in the game "StarCraft II." According to VentureBeat, Google and Blizzard announced their collaboration at the BlizzCon fan event in Anaheim, California, on Friday, Nov. 4. Google's DeepMind AI division explains the partnership on a blog post that clarifies why "StarCraft II" has been chosen for machine-learning research. Google's blog post states that "StarCraft" provides a useful bridge to the real-world, making an interesting testing environment for current AI research.

We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models, such as variational autoencoders (VAEs). However, these models typically assume that modalities are forced to have a conditioned relation, i.e., we can only generate modalities in one direction. To achieve our objective, we should extract a joint representation that captures high-level concepts among all modalities and through which we can exchange them bi-directionally. As described herein, we propose a joint multimodal variational autoencoder (JMVAE), in which all modalities are independently conditioned on joint representation. In other words, it models a joint distribution of modalities. Furthermore, to be able to generate missing modalities from the remaining modalities properly, we develop an additional method, JMVAE-kl, that is trained by reducing the divergence between JMVAE's encoder and prepared networks of respective modalities. Our experiments show that our proposed method can obtain appropriate joint representation from multiple modalities and that it can generate and reconstruct them more properly than conventional VAEs. We further demonstrate that JMVAE can generate multiple modalities bi-directionally.

The deep-learning software driving the modern artificial intelligence revolution has mostly run on fairly standard computer hardware. Some tech giants such as Google and Intel have focused some of their considerable resources on creating more specialized computer chips designed for deep learning. But IBM has taken a more unusual approach: It is testing its brain-inspired TrueNorth computer chip as a hardware platform for deep learning. Deep learning's powerful capabilities rely on algorithms called convolutional neural networks that consist of layers of nodes (also known as neurons). Such neural networks can filter huge amounts of data through their "deep" layers to become better at, say, automatically recognizing individual human faces or understanding different languages. These are the types of capabilities that already empower online services offered by the likes of Google, Facebook, Amazon, and Microsoft.