In this video from SC16, Intel demonstrates how Altera FPGAs can accelerate Machine Learning applications with greater power efficiency. “The demo was put together using OpenCL design tools and then compiled to FPGA. From an end-user perspective, they tied it together using Intel MKL-DNN with CAFFE on top of that. This week, Intel announced the DLIA Deep Learning Inference Accelerator that brings the whole solution together in a box.”
If basketball is of more interest to you than business intelligence, you’ll like this TED talk by Rajiv Maheswaran. If you want pithy examples of what algorithms and machine learning are, you’ll like Maheswaran’s talk even more. Algorithms are necessary to the functioning of any BI software, and machine learning has been called “the new BI.” Googling those terms is useful, but a little dull.
In the TEDMED talk below, Thomas Goetz looks at medical data, making a bold call to redesign it and get more insight from it. Your medical chart: it’s hard to access, impossible to read — and full of information that could make you healthier if you just knew how to use it.
In this video from the Intel HPC Developer Conference, Franz Kiraly from Imperial College London and the Alan Turing Institute describes why many companies and organizations are beginning to scope their potential for applying rigorous quantitative methodology and machine learning.
In this video from the Intel HPC Developer Conference, Elmoustapha Ould-ahmed-vall from Intel describes how the company is doubling down to optimize Machine Learning frameworks for Intel Platforms. Using open source frameworks as a starting point, surprising speedups are possible using Intel technologies.
In this TEDx talk, Ben Wellington discusses how to use data to tell stories. In fact, he draws on some key lessons from fields well outside computer science and data analysis to make his observations about New York City fascinating.
In the TEDx video presentation below, Kevin Novak, Senior Data Scientist at Uber, provides a description and history of Uber and how Uber’s data hacking made their explosion possible.
In the video presentation below, courtesy of our friends over at GridGain, Eric Karpman shares how some of the world’s largest financial institutions use in-memory computing to address the challenges of high frequency trading.
In this video from the 2016 HPC User Forum in Austin, John Feo from PNNL presents: Why use Tables and Graphs for Knowledge Discovery System? “GEMS software provides a scalable solution for graph queries over increasingly large data sets. As computing tools and expertise used in conducting scientific research continue to expand, so have the enormity and diversity of the data being collected. Developed at Pacific Northwest National Laboratory, the Graph Engine for Multithreaded Systems, or GEMS, is a multilayer software system for semantic graph databases. In their work, scientists from PNNL and NVIDIA Research examined how GEMS answered queries on science metadata and compared its scaling performance against generated benchmark data sets. They showed that GEMS could answer queries over science metadata in seconds and scaled well to larger quantities of data.”
In this presentation, Matthew Zeiler, Ph.D., Founder and CEO of Clarifai Inc, speaks about large convolutional neural networks. These networks have recently demonstrated impressive object recognition performance making real world applications possible.