AI Thought Leaders on Capitol Hill

By Tiffany Trader

July 14, 2018

‘Big Data Challenges and Advanced Computing Solutions’ Focus of House Committee Meeting

On Thursday, July 12, the House Committee on Science, Space, and Technology heard from four academic and industry leaders – representatives from Berkeley Lab, Argonne Lab, GE Global Research and Carnegie Mellon University – on the opportunities springing from the intersection of machine learning and advanced-scale computing.

“As the nation’s largest federal sponsor of basic research in the physical sciences, with expertise in big data science, advanced algorithms, data analytics and high performance computing, the Department of Energy (DOE) is uniquely equipped to fund robust fundamental research in machine learning,” said Energy Subcommittee Chairman Randy Weber (R-Texas), who opened the meeting.

Weber noted there are broad applications for machine learning and advanced computing in the DOE mission space, including high energy physics, fusion energy sciences and nuclear weapons development. He also emphasized the importance of data-driven technologies for academia and industry, citing the Rice University researchers who are exploring machine learning-based approaches for modeling flood waters and aiding in evacuation planning. “In Texas, we are still recovering from Hurricane Harvey—the wettest storm in United States history!” he said.

Kathy Yelick, associate laboratory director for Computing Sciences at Lawrence Berkeley National Laboratory, described some of the large-scale data challenges in the DOE Office of Science and gave an overview of how machine learning, and specifically deep learning, are poised to impact scientific discovery. “Machine learning has revolutionized the field of artificial intelligence and it requires three things: Large amounts of data, fast computers and good algorithms,” Yelick stated. “DOE has all of these. Scientific instruments are the eyes, ears and hands of science, but unlike artificial intelligence the goal is not to replicate human behavior but to augment it with superhuman measurement, control and analysis capabilities, empowering scientists to handle data at unprecedented scales, provide new scientific insights and solve societal challenges.”

“Machine learning does not replace the need for the high-performance computing simulations, but adds a complimentary tool for science,” Yelick said. “Recent earthquake simulations of the Bay Area show that just a three-mile difference in location of an identical building makes a significant difference in the safety of that building; it really is all about location, location, location. The team that did this work is looking at taking data from embedded sensors and eventually from smart meters to give even more detailed location-specific results.”

There is tremendous enthusiasm for machine learning in science, Yelick observed, but there’s also a need for caution. “Machine learning results are often lacking in explanations, interpretations, or error bars–a frustration for scientists–and scientific data is complicated and often incomplete,” she said. Bias in algorithms is also a concern, for example, a self driving car trained on one regional dialect may not recognize drivers from another region, or a cosmic event in the Southern hemisphere may not be recognized by a model that was trained on Northern hemisphere data.

“Foundational research in machine learning is needed along with a network to move the data to the computers and share it with the community and make it as easy to search for scientific data as it is to find a used car online,” she said.

In her full testimony report, Yelick highlighted DOE’s investments in supercomputing that are advancing machine learning, referencing early work on the recently-deployed pre-exascale systems Summit (at Oak Ridge National Lab) and Sierra (at Lawrence Livermore).

“One of the key computational kernels in deep learning is multiplying two matrices, which also is the dominant kernel in the Linpack benchmark used for the TOP500 list, where Summit and Sierra are in the #1 and #3 spots respectively,” said Yelick. “Finalists for the 2018 Gordon Bell prize include a deep learning computation at over 200 petaops /sec computation on Summit, which was a partnership between NERSC, OLCF, Nvidia, and Google, that was used to analyze data from cosmology and extreme weather events. A second finalist is a project lead by Oak Ridge National Laboratory with researchers from the University of Missouri in St. Louis, which used an entirely different algorithm to learn relationships between genetic mutations across an enormous set of genomes, with potential applications in biomanufacturing and human health. This algorithm can also be mapped to matrix-multiply like operations. It runs at a impressive 1.88 exaop/second! These are the fastest deep learning and other machine learning computations to date.”

Bobby Kasthuri, researcher at Argonne National Laboratory and assistant professor at University of Chicago, spoke passionately about the importance of investing in brain research. “Understanding how [the human brain] functions will be the great intellectual achievement of the 21st century, revealing the physical bases of our most human abilities like reasoning and serving as the blueprint for reverse engineering those abilities into algorithms and robots,” he said in his testimony. Kasthuri detailed the deep financial and structural barriers that face the neuroscience community. It’s a gap that the DOE and the national lab system are perfectly suited to addressing, Kasthuri asserted, drawing a parallel to the DOE’s support for the mapping of the human genome.

Offering industry perspective, Matthew Nielsen, principal scientist with Industrial Outcomes Optimization, GE Global Research, discussed the challenge of effectively integrating AI and machine learning into a business operation to differentiate products and services. GE, he said, has been on this journey for more than a decade.

Nielson’s testimony focused on the industrial applications of AI and machine learning that GE is developing with its customers and with federal agencies like the DOE to address key challenges with cybersecurity related to critical power assets. Applications underway include the Industrial Internet of Things and the Industrial Immune system, which will be able to detect and neutralize cyber threats using advanced AI techniques combined with a deep understanding of the machines’ physics. “It is a great example of how public-private research partnerships can advance technically risky but universally needed technologies,” said Nielson.

At Carnegie Mellon, U.S. steel professor of materials science and engineering Anthony Rollett helps lead the NextManufacturing Center, where researchers are combining 3D printing and machine learning to monitor the quality of manufactured components in real-time. He also participates in the Manufacturing Futures Initiative—a campus wide effort focused on accelerating innovation and enhancing manufacturing in the Greater Pittsburgh region.

Rollett advocated for investment in 3D printing, which is a key component of advanced manufacturing. “It is clear to me that this is a seriously revolutionary technology because it forces us to think differently about how to make things,” he said in his testimony. “The design of a part is as intimately coupled to the printing process and the chosen material as a Stradivarius is to its wood and crafting. The difference is the importance of data as input and as output. Imagine that in a few years we will be able to, e.g., build a rocket that is tailored to the particular mission, instead of forcing the payload to match one of a limited set of vehicles. Or that ‘mass production’ is transmuted into ‘mass individualization’ such that Ford’s proverbial ‘any color so long as black’ becomes ‘any choice of color and size for dozens if not hundreds of parts of a car.’”

The field of materials science has some unique requirements when it comes to employing machine learning. Rollett gave the example that computer vision is well developed for identifying cats, dogs and cars, etc., in images or videos, but the manufacturing domain produces cross-sectional images that are more complex with less obvious features.

The hearing was a lively one by Capitol Hill standards, drawing probing questions from committee members on topics ranging from the potential existential threat posed by artificial intelligence, the need to address bias in algorithms, the “black box” problem, and the best course for federal investment in research.

We’ve only scratched the surface of the informative and insightful hearing – watch it in full below:

LINK: https://www.youtube.com/watch?v=xllA-fu0bdw#t=26m34s

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

HPC in Life Sciences Part 1: CPU Choices, Rise of Data Lakes, Networking Challenges, and More

February 21, 2019

For the past few years HPCwire and leaders of BioTeam, a research computing consultancy specializing in life sciences, have convened to examine the state of HPC (and now AI) use in life sciences. Without HPC writ large, modern life sciences research would quickly grind to a halt. It’s true most life sciences research computing... Read more…

By John Russell

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized silicon designs catered toward general-purpose cloud computing Read more…

By Tiffany Trader

The Internet of Criminal Things—Trust in the Gods but Verify!

February 20, 2019

“Are we under attack?” asked Professor Elmarie Biermann of the Cyber Security Institute during the recent South African Centre for High Performance Computing’s (CHPC) National Conference in Cape Town. A quick show Read more…

By Elizabeth Leake, STEM-Trek

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

powercloud_blog.jpgFor decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

The Perils of Becoming Trapped in the Cloud

Terms like ‘open systems’ have been bandied about for decades. While modern computer systems are relatively open compared to their predecessors, there are still plenty of opportunities to become locked into proprietary interfaces. Read more…

Machine Learning Takes Heat for Science’s Reproducibility Crisis

February 19, 2019

Scientists are raising red flags about the accuracy and reproducibility of conclusions drawn by machine learning frameworks. Among the remedies are developing new ML systems that can question their own predictions, show Read more…

By George Leopold

HPC in Life Sciences Part 1: CPU Choices, Rise of Data Lakes, Networking Challenges, and More

February 21, 2019

For the past few years HPCwire and leaders of BioTeam, a research computing consultancy specializing in life sciences, have convened to examine the state of HPC (and now AI) use in life sciences. Without HPC writ large, modern life sciences research would quickly grind to a halt. It’s true most life sciences research computing... Read more…

By John Russell

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

Insights from Optimized Codes on Cineca’s Marconi

February 15, 2019

What can you do with 381,392 CPU cores? For Cineca, it means enabling computational scientists to expand a large part of the world’s body of knowledge from the nanoscale to the astronomic, from calculating quantum effects in new materials to supporting bioinformatics for advanced healthcare research to screening millions of possible chemical combinations to attack a deadly virus. Read more…

By Ken Strandberg

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

UC Berkeley Paper Heralds Rise of Serverless Computing in the Cloud – Do You Agree?

February 13, 2019

Almost exactly ten years to the day from publishing of their widely-read, seminal paper on cloud computing, UC Berkeley researchers have issued another ambitious examination of cloud computing - Cloud Programming Simplified: A Berkeley View on Serverless Computing. The new work heralds the rise of ‘serverless computing’ as the next dominant phase of cloud computing. Read more…

By John Russell

Iowa ‘Grows Its Own’ to Fill the HPC Workforce Pipeline

February 13, 2019

The global workforce that supports advanced computing, scientific software and high-speed research networks is relatively small when you stop to consider the magnitude of the transformative discoveries it empowers. Technical conferences provide a forum where specialists convene to learn about the latest innovations and schedule face-time with colleagues from other institutions. Read more…

By Elizabeth Leake, STEM-Trek

Trump Signs Executive Order Launching U.S. AI Initiative

February 11, 2019

U.S. President Donald Trump issued an Executive Order (EO) today launching a U.S Artificial Intelligence Initiative. The new initiative - Maintaining American L Read more…

By John Russell

Celebrating Women in Science: Meet Four Women Leading the Way in HPC

February 11, 2019

One only needs to look around at virtually any CS/tech conference to realize that women are underrepresented, and that holds true of HPC. SC hosts over 13,000 H Read more…

By AJ Lauer

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

AMD Sets Up for Epyc Epoch

November 16, 2018

It’s been a good two weeks, AMD’s Gary Silcott and Andy Parma told me on the last day of SC18 in Dallas at the restaurant where we met to discuss their show news and recent successes. Heck, it’s been a good year. Read more…

By Tiffany Trader

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The Read more…

By John Russell

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC Read more…

By Tiffany Trader

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s Read more…

By John Russell

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

HPC Reflections and (Mostly Hopeful) Predictions

December 19, 2018

So much ‘spaghetti’ gets tossed on walls by the technology community (vendors and researchers) to see what sticks that it is often difficult to peer through Read more…

By John Russell

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, product photos, and even a beautiful image of supernovae... Read more…

By John Russell

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

Intel Confirms 48-Core Cascade Lake-AP for 2019

November 4, 2018

As part of the run-up to SC18, taking place in Dallas next week (Nov. 11-16), Intel is doling out info on its next-gen Cascade Lake family of Xeon processors, specifically the “Advanced Processor” version (Cascade Lake-AP), architected for high-performance computing, artificial intelligence and infrastructure-as-a-service workloads. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This