Code Together

ChatGPT, DALL-E, Stable Diffusion and other generative AI have rapidly captivated the public. They provide new, easy ways, to create new content based on AI models that have been trained on large, public data sets. How does this technology work? What does it mean for society? In this podcast we discuss the technology behind generative AI, some of the potential legal and societal issues, and what the pros and cons of generative AI are.

Guests

Parallel programming models are often used on large scale systems, with huge datasets and a large amount of obvious parallelism. In this podcast, Tony talks to two university students that are building a chat bot using consumer hardware and standard frameworks like TensorFlow and oneAPI. They use TensorFlow to enable their AI model and SYCL to optimize various parts of their data pipeline.

They also talk about the Intel Student Ambassador program, Intel Discord, and how Josh and Harvey became part of the Intel social community.

For more information see:

TensorFlow

SYCL

oneAPI

Building LLVM to compile SYCL for NVIDIA GPUs

Intel Student Ambassador Program

Intel Discord

OpenAI – GPT-2, GPT-3 resources

Google LaMDA

Kaggle

AI Predicts Heart Disease Risk Using Single X-Ray

Guests

One of the greatest challenges to using AI/ML in industry is reducing your time to solution so that your data scientist and engineers have a chance to iterate. Setting up a machine learning pipeline from scratch can be a complexity nightmare for MLOps engineers.

Steve Huels and Raghu Moorthy join Tony to discuss the value of RedHat OpenShift Data Science, how the platform is built, and how it can significantly reduce the initial deployment challenge. They also discuss some of the software deployed as part of the RedHat OpenShift Data Science platform and how these pieces drive cost effective, accelerated AI solutions from Cloud to Edge.

Intel-Redhat Partner page:

Open Data Hub:

RedHat Open Shift Data Science Sandbox:

Guests:

Raghu Moorthy – Global Director Intel Sales and Marketing Group:

The Argonne Leadership Computing Facility (ALCF) and Intel are working together on Aurora, an Exascale supercomputer. As the ALCF and Intel prepare for Aurora, we consider how we will use this Exascale system to support AI for Science. Learn about how Aurora will expand the boundaries of existing science and how we can use Aurora to drive scientific research forward.

See what next-generation scientific problems are being slated to run on Aurora:

Argonne Leadership Computing Facility and Aurora

oneAPI

DAOS

Intel oneAPI Data Analytics Library (oneDAL)

Intel oneAPI Collective Communications Library

Horovod

The Argonne Leadership Computing Facility (ALCF) and Intel are working together on Aurora, an Exascale supercomputer. As the ALCF and Intel prepare for Aurora, learn about the convergence of High Performance Computing (HPC), Artificial Intelligence (AI) and big data analytics. Learn how the ALCF is preparing its software, and its users for Aurora, including some challenges that can be overcome utilizing some of the latest Intel technologies. We also talk about the science Aurora will enable, and why you never know what questions will be answered or arise when using a supercomputer.

See what next-generation scientific problems are being slated to run on Aurora

See also:

As AI continues to play an ever-increasing role in applications and decision-making, the challenges facing developers of these AI workloads continue to expand higher and higher. Through this talk, gain a deeper understanding of the current landscape and challenges facing AI developers and data scientists today, and Intel and Red Hat’s perspectives on the most pressing issues in this area. Learn how Intel and Red Hat’s partnership is addressing these challenges from local devices to the cloud by enabling Intel’s most popular AI developer optimizations on Red Hat OpenShift Data Science, and the potential opportunities for developers using this solution.

Intel-Redhat Partner page:

Intel AI Analytics Tookit Operator:

Boost OpenShift Data Science with the Intel®AI Analytics Tookit:

Intel AI/ML Overview

Guests

Rachel Oberman – AI Software Solutions Engineer at Intel

Intel, HPE, and Argonne National Lab are collaborating closely to deliver Aurora. Built on Intel’s architecture, Aurora will be Argonne’s first Exascale, high performance computing system. Its performance is expected to exceed exaflops, which is 2 billion billion calculations per second. It’s enormous scale and performance levels will accelerate research in science and engineering fields at a scale that is not possible today.

ACLF – Aurora:

Michael D’mello, Program Manager, Intel

The exponential growth of data generation and the need for it’s storage is driving the next phase of innovation in data storage. Scientists are now able to store information as molecules of DNA, which both scalable and accurate. With the right set of open-source tools and optimizations, synthetic DNA is paving the way for the next generation of advancements

Industry Guest: Raja Appuswamy, Assistant Professor, EURECOM – Data Science Department

Intel Guest: Sujata Tibrewala, Sr. Manager Open Source Alliance

Resources

Ever since its introduction in 2014, SYCL has grown in popularity and adoption. It is a royalty-free, cross-platform abstraction layer that enables code for heterogeneous processors, written in a “single-source” style using C++ standards.

The flexibility to deploy across multiple platforms, reuse code helps enable advanced hardware features that can be used by developers. This allows develops the flexibility that they are looking for.

Today’s conversation with our guests touches upon SYCL’s evolution and its adoption both in academia and in enterprise applications. We will discuss how academia and enterprise are learning how to program with SYCL in addition to what the future holds for this open standard.

For more information, see:

Roberto Di Remigio Eikås, Research Software Engineer at ENCCS

Noah Clemons, Staff Technical Consulting Engineer, Developer Tools Customer Engineering at Intel

The rapid advancement in machine learning and data science fields have aided data scientists in arriving at meaningful insights. However, it’s not been an easy task to optimize machine learning infrastructures to allow data scientists to focus on their core expertise. Today, we will discuss how certain tools and hardware optimizations are not only saving time, but also enabling data scientists to be more productive.

Learn more:

Guests: