“From home assistants like the Amazon Echo to Google’s self-driving cars, artificial intelligence is slowly creeping into our lives. These new technologies could be enormously beneficial, but they also offer hackers unique opportunities to harm us. For instance, a self-driving car isn’t just a robot—it’s also an internet-connected device, and may even have a cell phone number.”
“Computational science has come a long way with machine learning (ML) and deep learning (DL) in just the last year. Leading centers of high-performance computing are making great strides in developing and running ML/DL workloads on their systems. Users and algorithm scientists are continuing to optimize their codes and techniques that run their algorithms, while system architects work out the challenges they still face on various system architectures. At SC16, I had the honor of hosting three of HPC’s thought leaders in a panel to get their ideas about the state of Artificial Intelligence (AI), today’s challenges with the technology, and where it’s going.”
Today ISC 2017 announced that data scientist, Prof. Dr. Jennifer Tour Chayes from Microsoft Research will give the opening keynote at the conference. “I’ll discuss in some detail two particular applications: the very efficient machine learning algorithms for doing collaborative filtering on massive sparse networks of users and products, like the Netflix network; and the inference algorithms on cancer genomic data to suggest possible drug targets for certain kinds of cancer,” explains Chayes.
Intel DAAL is a high-performance library specifically optimized for big data analysis on the latest Intel platforms, including Intel Xeon®, and Intel Xeon Phi™. It provides the algorithmic building blocks for all stages in data analysis in offline, batch, streaming, and distributed processing environments. It was designed for efficient use over all the popular data platforms and APIs in use today, including MPI, Hadoop, Spark, R, MATLAB, Python, C++, and Java.
In this week’s Sponsored Post, Katie Garrison, of One Stop Systems explains how GPUs and Flash solutions are used in radar simulation and anti-submarine warfare applications. “High-performance compute and flash solutions are not just used in the lab anymore. Government agencies, particularly the military, are using GPUs and flash for complex applications such as radar simulation, anti-submarine warfare and other areas of defense that require intensive parallel processing and large amounts of data recording.”
In this video from KAUST, Steve Scott from at Cray explains where supercomputing is going and why there is a never-ending demand for faster and faster computers. Responsible for guiding Cray’s long term product roadmap in high-performance computing, storage and data analytics, Mr. Scott is chief architect of several generations of systems and interconnects at Cray.
In this podcast, the Radio Free HPC team hosts Dan’s daughter Elizabeth. How did Dan get this way? We’re on a mission to find out even as Elizabeth complains of the early onset of Curmudgeon’s Syndrome. After that, we take a look at the Tsubame3.0 supercomputer coming to Tokyo Tech.
“In recent years, major breakthroughs were achieved in different fields using deep learning. From image segmentation, speech recognition or self-driving cars, deep learning is everywhere. Performance of image classification, segmentation, localization have reached levels not seen before thanks to GPUs and large scale GPU-based deployments, leading deep learning to be a first class HPC workload.”
Today ISC 2017 announced that it’s Distinguished Talk series will focus on Data Analytics in manufacturing and scientific applications. One of the Distinguished Talks will be given by Dr. Sabine Jeschke from the Cybernetics Lab at the RWTH Aachen University on the topic of, “Robots in Crowds – Robots and Clouds.” Jeschke’s presentation will be followed by one from physicist Kerstin Tackmann, from the German Electron Synchrotron (DESY) research center, who will discuss big data and machine learning techniques used for the ATLAS experiment at the Large Hadron Collider.
“Machine Learning and deep learning represent new frontiers in analytics. These technologies will be foundational to automating insight at the scale of the world’s critical systems and cloud services,” said Rob Thomas, General Manager, IBM Analytics. “IBM Machine Learning was designed leveraging our core Watson technologies to accelerate the adoption of machine learning where the majority of corporate data resides. As clients see business returns on private cloud, they will expand for hybrid and public cloud implementations.”