NVIDIA CEO Jensen Huang addresses 1,400+ attendees of SC19, the annual supercomputing conference. in Denver

He introduced a reference design for building GPU-accelerated Arm servers, announced the world’s largest GPU-accelerated cloud-based supercomputer on Microsoft Azure, and unveiled NVIDIA Magnum IO storage software to eliminate data transfer bottlenecks for AI, data science, and HPC workloads.

Here’s an interesting announcement that may or may not be related to a recent DataPoint I recorded at MLADS.

Nvidia and Microsoft have joined forces to offer a cloud HPC capability based on the GPU vendor’s V100 Tensor Core chips linked via an Infiniband network scaling up to 800 graphics processors. The partners announced during this week’s Supercomputer ’19 event that the GPU-accelerated supercomputer running on the Azure […]

NVIDIA had some interesting ideas and hardware to show off at the recent MWC LA.

Here’s their promo video.

At MWC LA, NVIDIA founder and CEO Jensen Huang introduced the NVIDIA EGX Edge Supercomputing Platform, a high-performance, cloud-native edge computing platform optimized to take advantage of three key revolutions – AI, IoT and 5G – and provide the world’s leading companies the ability to build next-generation services.

Learn more: https://www.nvidia.com/en-us/data-center/products/egx-edge-computing/

 

Siraj Raval just posted this video on defending AI against adversarial attacks

Machine Learning technology isn’t perfect, it’s vulnerable to many different types of attacks! In this episode, I’ll explain 2 common types of attacks and 2 common types of defenses using various code demos from across the Web. There’s some really dope mathematics involved with adversarial attacks, and it was a lot of fun reading about the ‘cat and mouse’ game between new attack techniques, followed by new defense techniques. I encourage anyone new to the field who finds this stuff interesting to learn more about it. I definitely plan to. Let’s look into some math, code, and examples. Enjoy!

Slideshow for this video:
https://colab.research.google.com/drive/19N9VWTukXTPUj9eukeie55XIu3HKR5TT

Demo project:
https://github.com/jaxball/advis.js

 

Siraj Raval explores generative modeling technology.

This innovation is changing the face of the Internet as you read this. It’s now possible to design automated systems that can write novels, act as talking heads in videos, and compose music.

In this episode, Siraj explains how generative modeling works by demoing 3 examples that you can try yourself in your web browser. 

IoT will be the next driver of AI innovation. By 2025, there will be 55 billion IoT devices (Business Insider Intelligence), and  Due to to latency, cost, privacy and connectivity issues, being able to analyze data at the edge where it’s created is critical because it improves the speed of analysis and decision-making.

Data analytics has generally relied on human-defined classifiers or “feature extractors” which are rules that can be as simple as a linear regression, to more complicated machine learning algorithms. But can you imagine building a human-defined perfect rule-based system to model everything?