{"id":318893,"date":"2025-08-05T04:27:11","date_gmt":"2025-08-05T04:27:11","guid":{"rendered":"https:\/\/www.europesays.com\/uk\/318893\/"},"modified":"2025-08-05T04:27:11","modified_gmt":"2025-08-05T04:27:11","slug":"industry-academia-and-government-come-together-at-tpc25","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/uk\/318893\/","title":{"rendered":"Industry, Academia, and Government Come Together at TPC25"},"content":{"rendered":"

Many of the advances in AI recently have come from the private sector, specifically the handful of giant tech firms with the resources and expertise to develop massive foundation models. While those advances have generated tremendous excitement and promise, a different group of stakeholders is looking to drive future AI breakthroughs in scientific and technical computing, which was a topic of some discussion this week at the Trillion Parameter Consortium\u2019s TPC25 conference in San Jose, California.<\/p>\n

One TPC25 panel discussion on this topic was especially informative. Led by moderator Karthik Duraisamy of the University of Michigan, the July 30 talk centered on how government, academia, national labs, and industry can work together to harness recent AI developments to drive scientific discovery for the betterment of the United States and, ultimately, humankind.<\/p>\n

Hal Finkel, the director of the Department of Energy\u2019s computational science research and partnerships division, was unequivocal in his department\u2019s support of AI. \u201cAll parts of DOE have a critical interest in AI,\u201d Finkel said. \u201cWe are investing very heavily in AI, and have been for a long time. But things are different now.\u201d\"\"<\/a><\/p>\n

DOE currently is looking at how it can leverage the latest AI enhancement to accelerate scientific productivity across a range of disciplines, Finkel said, whether it\u2019s accelerating the path to superconductors and fusion energy or advanced robotics and photonics.<\/p>\n

\u201cThere is just a huge amount of area where AI is going to be important,\u201d he said. \u201cWe want to be able to leverage our supercomputing expertise. We have exascale supercomputers now across DOE and several national laboratories. And we have testbeds, as I mentioned, in AI. And we\u2019re also looking at new AI technologies\u2026like neuromorphic technologies, things that are going to be important for doing AI at the edge, embedding in experiments using advanced robotics, things which could be dramatically more energy efficient than the AI that we have today.\u201d<\/p>\n

Vishal Shrotriya, a business development executive with Quantinuum<\/a>, a developer of quantum computing platforms, is looking forward to the day when quantum computers, working in concert with AI algorithms, are able to solve the toughest computational problems across areas like material science, physics, and chemistry.<\/p>\n

\u201cSome people say that true chemistry is not possible until we have quantum computers,\u201d Shrotriya said. \u201cBut we\u2019ve done such amazing work without actually having the ability to stimulate even small molecules precisely. That is what quantum computers will allow you to do.\u201d<\/p>\n

The combination of quantum computers and foundation models could be groundbreaking for molecular scientists by enabling them to create new synthetic data from quantum computers. Scientists will then be able to feed that synthetic data back into AI models, creating a powerful feedback loop that, hopefully, drives scientific discovery and innovation.<\/p>\n

\"\"<\/a>Quantinuum\u2019s Vishal Shrotriya (left) and Molly Presley of Hammerspace at TPC25 July 30, 2025<\/p>\n

\u201cThat is a big area where quantum computers can potentially allow you to accelerate that drug development cycle and move away from that trial and error to allow you to precisely, for example, calculate the binding energy of the protein into the site in a molecule,\u201d Shrotriya said.<\/p>\n

A capable defender of the vital importance of data in the new AI world was Molly Presley, the head of global marketing for Hammerspace. Data is absolutely critical to AI, of course, but the problem is, it\u2019s not evenly distributed around the world. Hammerspace helps by working to eliminate the tradeoffs inherent between the ephemeral representation of data in human minds and AI models, and data\u2019s physical manifestation.<\/p>\n

Standards are vitally important to this endeavor, Presley said. \u201cWe have Linux kernel maintainers, multiple of them on our staff, driving a lot of what you would think of as traditional storage services into the Linux kernel, making it where you can have standards based access that any data, no matter where it was created, [so that it] can be visible and used with the appropriate permissions in other locations.\u201d<\/p>\n

The world of AI could use more standards to help data be used more broadly, including in AI, Presley said. One topic that has come up repeatedly on her \u201cData Unchained<\/a>\u201d podcast is the need for greater agreement on how to define metadata.<\/p>\n

\u201cThe guests almost every time come up with standardization on metadata,\u201d Presley said. \u201cHow a genomics researcher ties their metadata versus an HPC system versus in financial services? It\u2019s completely different, and nobody knows who should tackle it. I don\u2019t have an answer.<\/p>\n

\u201cThis type of community probably is who could do it,\u201d Presley said. \u201cBut because we want to use AI outside of the location or the workflow or the data was created, how do you make that metadata standardized and searchable enough that someone else can understand it? And that seems to be a big challenge.\u201d<\/p>\n

The US Government\u2019s National Science Foundation was represented by Katie Antypas,<\/strong> a Lawrence Berkeley National Lab employee who was just renamed director of the Office of Advanced Cyber Infrastructure. Anytpas pointed to the role that the National Artificial Intelligence Research Resource (NAIRR) project plays in helping to educate the next generation of AI experts.<\/p>\n

\"\"<\/a>DOE\u2019s Hal Finkel (left) and Intel Labs Pradeep Dubey<\/p>\n

\u201cWhere I see a huge challenge is actually in the workforce,\u201d Antypas said. \u201cWe have so many talented people across the country, and we really need to make sure that we\u2019re developing this next generation of talent. And I think it\u2019s going to take investment from industry partnerships with industry as well as the federal government, to make those really critical investments.\u201d<\/p>\n

NAIRR started under the first Trump Administration, was kept under the Biden Administration, and is \u201cgoing strong\u201d in the second Trump Administration, Antypas said.<\/p>\n

\u201cIf we want a healthy AI innovation ecosystem, we need to make sure we\u2019re investing really that fundamental AI research,\u201d Antypas said. \u201cWe didn\u2019t want all of the research to be driven by some of the largest technology companies that are doing amazing work. We wanted to make sure that researchers across the country, across all domains, could get access to those critical resources.\u201d<\/p>\n

The fifth panelist was Pradeep Dubey, an Intel Senior Fellow at Intel Labs<\/a> and director of the the Parallel Computing Lab. Dubey sees challenges at multiple levels of the stack, including foundation model\u2019s inclination to hallucinate, the changing technical proficiency of users, and where we\u2019re going to get gigawatts of energy to power massive clusters.<\/p>\n

\u201cAt the algorithmic level, the biggest challenge we have is how do you come up with a model that is both capable and trusted at the same time,\u201d Dubey said. \u201cThere\u2019s a conflict there. Some of these problems are very easy to solve. Also, they are just hype, meaning you can just put the human in the loop and you can take care of those\u2026 the problems are getting solved and you\u2019re getting hundreds of year\u2019s worth of speedup. So putting a human in the loop is just going to slow you down.\u201d<\/p>\n

AI has come this far primarily because it has not figured out what\u2019s computationally and algorithmically hard to do, Dubey said. Solving those problems will be quite difficult. For instance, hallucination isn\u2019t a bug in AI models\u2013it\u2019s a feature.<\/p>\n

\"\"<\/a>NSF\u2019s Katie Antypas (left) and TPC25 moderator Karthik Duraisamy<\/p>\n

\u201cIt\u2019s the same thing in a room when people are sitting and some guy will say something. Like, are you crazy?\u201d the Intel Senior Fellow said. \u201cAnd that crazy guy is often right. So this is inherent, so don\u2019t complain. That\u2019s exactly what AI is. That\u2019s why it has come this far.\u201d<\/p>\n

Opening up AI to non-coders is another issue identified by Dubey. You have data scientists who prefer to work in an environment like MATLAB gaining access to GPU clusters. \u201cYou have to think of how you can take AI from library Cuda jail or Cuda-DNN jail, to decompile in very high level MATLAB language,\u201d he said. \u201cVery difficult problem.\u201d<\/p>\n

However, the biggest issue\u2013and one that was a recurring theme at TPC25\u2013was the looming electricity shortage. The huge appetite for running massive AI factories could overwhelm available resources.<\/p>\n

\u201cWe have enough compute at the hardware level. You cannot feed it. And the data movement is costing more than 30%, 40%,\u201d Dubey said. \u201cAnd what we want is 70 or 80% energy will go to moving data, not computing data. So now let us ask the question: Why am I paying the gigawatt bill if you\u2019re only using 10% of it to compute it?\u201d<\/p>\n

There are big challenges that the computing community must address if it\u2019s going to get the most out of the current AI opportunity and take scientific discovery to the next level. All stakeholders\u2013from the government and national labs, from industry to universities\u2013will play a role.<\/p>\n

\u201cIt has to come from the broad, aggregated interest of everyone,\u201d the DOE\u2019s Finkel said. \u201cWe really want to facilitate bringing people together, making sure that people understand where people\u2019s interests are and how they can join together. And that\u2019s really the way that we facilitate that kind of development. And it really is best when it is community-driven.\u201d<\/p>\n

Originally posted on our sister site BigDATAwire.<\/a><\/p>\n

\t\t\t\t\t\tSectors:
\n\t\t\t\t\t\tAcademia & Research<\/a>, Community<\/a>, Energy<\/a>, Financial Services<\/a>, Government<\/a>, Life Sciences<\/a>, Manufacturing<\/a>, Oil & Gas<\/a>, semiconductor<\/a>, Space & Physics<\/a>, Weather & Climate<\/a>\t\t\t\t\t<\/p>\n

\t\t\t\t\t\tTags:
\n\t\t\t\t\t\tAI for Science<\/a>, DOE<\/a>, grassroots<\/a>, Hal Finkel<\/a>, Karthik Duraisamy<\/a>, Katie Antypas<\/a>, Molly Presley<\/a>, NSF<\/a>, Pradeep Dubey<\/a>, TPC25<\/a>, Trillion Parameter Consortium<\/a>, Vishal Shrotriya<\/a>\t\t\t\t\t<\/p>\n","protected":false},"excerpt":{"rendered":"Many of the advances in AI recently have come from the private sector, specifically the handful of giant…\n","protected":false},"author":2,"featured_media":318894,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3164],"tags":[31550,3284,115813,33449,115814,115815,115816,115817,62703,115818,53,115819,115820,16,15,115821],"class_list":{"0":"post-318893","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-computing","8":"tag-ai-for-science","9":"tag-computing","10":"tag-doe","11":"tag-grassroots","12":"tag-hal-finkel","13":"tag-karthik-duraisamy","14":"tag-katie-antypas","15":"tag-molly-presley","16":"tag-nsf","17":"tag-pradeep-dubey","18":"tag-technology","19":"tag-tpc25","20":"tag-trillion-parameter-consortium","21":"tag-uk","22":"tag-united-kingdom","23":"tag-vishal-shrotriya"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@uk\/114974271001496807","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/318893","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/comments?post=318893"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/318893\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media\/318894"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media?parent=318893"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/categories?post=318893"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/tags?post=318893"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}