Next Platform TV for September 15, 2020

On today’s program, some kickoff analysis of the Nvidia/Arm deal; a look at geospatial AI and what it requires in terms of future hardware and frameworks; physics-informed AI and what it means for future simulations and use cases; the Summit supercomputer and the “right” architecture for large-scale, multidisciplinary  COVID-19 research; the elements of a hyperconverged data platform; infrastructure as code; much more in today’s interview lineup. More info and timestamps below.

We have a wide-ranging program today, kicking off with an analysis of the geospatial industry and its growing adoption of deep learning to speed products to market. The challenge is that existing AI frameworks aren’t suited to data types and requirements and the hardware will also take some changes over time with San Gunawardana, co-founder and CEO of Enview.

Also on today’s show we talk with Christopher Lamb, VP of Computing Software at Nvidia about physics-informed neural networks and where these are taking hold. We talk about where this approach might shave off training times and model data for a wide range of potential use cases.

We spoke to Darshan Rawal, founder and chief executive officer at Isima, which has just dropped out of stealth and which has just created a new hyperconverged data platform that is made to make it so data scientists don’t have to be human data management engines to do their day jobs.
We also had a chat with Joe Duffy, founder and chief executive officer of Pulumi, about infrastructure as code and how the Pulumi platform is different from Chef, Puppet, and Terraform and how it can be used to manage infrastructure across clouds and allow companies to not have to use a hodge-podge of cloud-specific tools.

We also talk with Dan Jacobson, lead researcher and computational systems biologist at Oak Ridge National Laboratory about base of research targeting COVID-19 on the Summit supercomputer. We look ahead to future architectures and project what they might be able to lend to similar research at exascale in the future.

Timestamps

2:23 – Geospatial AI: The Current State and Future System, Framework Requirements

14:45 – Infrastructure as Code: Filling in the Missing Gaps

24:19 – Physics-Informed AI: What it Means for Training Times/Efficiency, Use Cases

34:23 – Summit of COVID-19 Research: Large-Scale Modeling and Future Architectures

43:53 – Piecing Together the Elements of a Hyperconverged Data Platform

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