Just like machine learning is a subset of artificial intelligence, deep learning is a subset of machine learning. Going back to that workshop definition, deep learning is the part of machine learning that focuses on forming "abstractions and concepts." Deep learning systems ingest large quantities of data and generalize categories and features related to that data through supervised or unsupervised learning. To understand how this works, consider the problem of teaching a computer to distinguish pictures of cats from pictures of dogs. Programmers could try to come up with a set of rules that explains exactly what a cat is and exactly what a dog is, but even though humans can easily distinguish a cat from a dog, it's really hard to explain that difference using algorithms that a computer can understand. However, a deep learning system can analyze a whole bunch of pictures of animals and come to its own generalizations about what distinguishes a cat from a dog.

Enlisting computational technologies in the war on cancer isn't new but it has taken on an increasingly decisive role. At SC17, Eric Stahlberg, director of the HPC Initiative at Frederick National Laboratory for Cancer Research in the Data Science and Information Technology Program, and two colleagues will lead the third Computational Approaches for Cancer workshop being held the Friday, Nov. 17, at SC17. It is hard to overstate the importance of computation in today's pursuit of precision medicine. Given the diversity and size of datasets it's also not surprising that the "new kids" on the HPC cancer fighting block – AI and deep learning/machine learning – are also becoming the big kids on the block promising to significantly accelerate efforts understand and integrate biomedical data to develop and inform new treatments. In this Q&A, Stahlberg discusses the goals of the workshop, the growing importance of AI/deep learning in biomedical research, how programs such as the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) are progressing, the need for algorithm assurance and portability, as well as ongoing needs where HPC technology has perhaps fallen short.

The third session has been more an "educational presentation" around deep learning, and how at high level a deep learning system work, however we have seen in this talk some interesting topics: The fourth session has been one also an interesting session, because the speaker clearly explained the current possibilities and limits of the current application development landscape and in particular of the enterprise bots. Key take away: Bots are far from being smart and people don't want to type text. Suggested approach bots are new apps that are reaching their "customers" in the channels that they already use (slack for example) and those new apps using the context and channel functionalities have to extend and at the same time simplify the IT landscape. Example: bot in a slack channel that notifies manager of an approval request and the manager can approve/deny directly in slack without leaving the app. The fourth and the fifth talk have been rather technical/educational on specific frameworks (IBM System ML for Spark) and on models portability (PMML) with some good points around hyper parameter tuning using a spark cluster in iterative mode and DNN auto encoders.

Most of the prominent companies agree that Artificial Intelligence is the future for sales and marketing, as reported by Enrique Dans who wrote for Forbes recently. It shouldn't come as a surprise when the tech giants are already researching and improving their proprietary AI products. Its iconic "Google Brain" is a big deep learning research project that has been underway since 2011. It has been instrumental in improving the company's existing products such as image searching, Google assistant, etc. To showcase the power of Artificial Intelligence and Machine Learning the company has also provided many nifty AI experiments on their website for the users to interact with.

Yoshua Bengio is one of the foremost thinkers in a field within artificial intelligence known as artifical neural networks and deep learning. Although significant progress has been made in recent years due to (among other factors) the combination of the proliferation of data, the decreasing cost of compute, and the tremendous amount of money and talent now devoted to artificial intelligence, Bengio chose this as a field of study during the 1980s, in the throes of what some referred to as the AI winter, seeing through a period when money and enthusiasm for artificial intelligence had dried up. Bengio is the co-author (with Ian Goodfellow and Aaron Courville) of Deep Learning, a book that Elon Musk referred to as "the definitive textbook on deep learning." On top of his growing influence in this field, he has also been enormously influential in shaping Montreal to become a hotbed for artificial intelligence. Bengio co-founded Element AI in 2016, which has a stated mission to "turn the world's leading AI research into transformative business applications."

Artificial intelligence is seemingly everywhere these days, from self-driving cars and virtual assistants to medical innovations. References to AI are so pervasive, it also seems to have different names. Depending on the project at hand, you might hear talk of machine learning, deep learning or cognitive computing, all of which produce a kind of "thinking machine." But while those terms sometimes get used interchangeably, they're not exactly the same thing. AI, of course, is the most commonly used term, because it is the umbrella that sits over the others and also because it has long been alive in the cultural imagination, for both good and ill, from the beneficent Data in "Star Trek: The Next Generation," to the malevolent Agent Smith in "The Matrix," and many other manifestations in between.

Microsoft and Cray just announced a strategic alliance that gives enterprise users of the Microsoft Azure cloud platform access to dedicated Cray supercomputing systems. As a result of the agreement, moving forward, Microsoft and Cray will both be able to offer customers access to Cray supercomputing systems in Microsoft Azure datacenters, to run AI, advanced analytics, and other HPC-class workloads. "Our partnership with Microsoft will introduce Cray supercomputers to a whole new class of customers that need the most advanced computing resources to expand their problem-solving capabilities, but want this new capability available to them in the cloud," said Peter Ungaro, president and CEO of Cray. "Dedicated Cray supercomputers in Azure not only give customers all of the breadth of features and services from the leader in enterprise cloud, but also the advantages of running a wide array of workloads on a true supercomputer, the ability to scale applications to unprecedented levels, and the performance and capabilities previously only found in the largest on-premise supercomputing centers." Availability of Cray supercomputer resources in Azure, allows researchers, analysts, and other professionals to do things like train AI deep learning models, perform whole genome sequencing, conduct crash simulation, perform computational fluid dynamic simulations, or run any other type of HPC workload that would typically require massive hardware and IT management investments, from machines attached to the cloud.

Pentagon official: AI and machine learning to revolutionize the US intelligence community 2. How AI could spot lung cancer sooner – and save lives 3. AI researchers can now access optimized deep learning framework containers in the cloud 4. AI4ALL improves student access to AI resources through NVIDIA partnership 5. READ BLOG 6. HOW AI COULD SPOT LUNG CANCER SOONER – AND SAVE LIVES Lung cancer is the most common cancer worldwide. More than 80 percent of people with lung cancer die within five years of being diagnosed, and half die within a year. H. Michael Park, co- founder of startup Innovation DX, is working to improve those odds. In December, his St. Louis-based medical analytics company plans to release its first product -- a GPU-accelerated AI system that detects lung cancer in its early stages from a simple chest X-ray. "Lung cancer is so deadly today because it's diagnosed so late. READ BLOG 7. AI RESEARCHERS CAN NOW INNOVATE IN MINUTES, NOT WEEKS, WITH NVIDIA GPU CLOUD NVIDIA announced the NVIDIA GPU Cloud (NGC), a cloud-based platform that gives developers convenient access – deskside or the cloud -- to a comprehensive software suite for harnessing the transformative powers of AI. Jim McHugh, vice president and general manager of DGX Systems at NVIDIA, shared: "We're designing a cloud platform that will unleash AI developers, so they can build a smarter world.

Advancements in artificial intelligence, or AI, are revolutionizing healthcare and leading to breakthrough results in prediction and prevention. AI has impacted a variety of industries already, but nowhere are developments in artificial intelligence more important than in the healthcare industry--they can be life-saving. A new report from HPE focuses on how tech tools like GPUs and deep learning platforms are changing and advancing the healthcare industry. Recent advancements in AI technology have made it hard to ignore its potential to make health care professionals lives easier, as well as advance medicine. Just imagine the amount of data that health professionals have to review manually before they diagnose or treat a patient.

For consumer and enterprise users, viruses and malware are a never ending cause of trouble. However in the enterprise market there are bigger things at stake – businesses have much more sensitive data and services in place that can't afford to be compromised in any way. Year on year, attacks on enterprise networks have steadily grown, and recently a surge of intelligent malware and ransomware have been crippling networks and systems around the globe. These systems do have measures in place to prevent these attacks from happening, but they often tend to be from different vendors or don't provide adequate protection from all possible fronts. "On the operational side of things a lot of customers have different solutions that work well, but there are just too many different consoles and portals to log into when managing them all," comments John Shier, Senior Security Advisor, Sophos, at GITEX Technology Week earlier this year.