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TACC’s “Horizon” Supercomputer Sets The Pace For Academic Science

As we expected, the “Vista” supercomputer that the Texas Advanced Computing Center installed last year as a bridge between the current “Stampede-3” and “Frontera” production system and its future “Horizon” system coming next year was indeed a precursor of the architecture that TACC would choose for the Horizon machine.

What TACC does – and doesn’t do – matters because as the flagship datacenter for academic supercomputing at the National Science Foundation, the company sets the pace for those HPC organizations that need to embrace AI and that have not only large jobs that require an entire system to run (so-called capability-class machines) but also have a wide diversity of smaller jobs that need to be stacked up and pushed through the system (making it also a capacity-class system). As the prior six major supercomputers installed at TACC aptly demonstrate, you can have the best of both worlds, although you do have to make different architectural choices (based on technology and economics) to accomplish what is arguably a tougher set of goals.

Some details of the Horizon machine were revealed at the SC25 supercomputing conference last week, which we have been mulling over, but there are still a lot of things that we don’t know. The Horizon that will be fired up in the spring of 2026 is a bit different than we expected, with the big change being a downshift from an expected 400 petaflops of peak FP64 floating point performance down to 300 petaflops. TACC has not explained the difference, but it might have something to do with the increasing costs of GPU-accelerated systems. As far as we know, the budget for the Horizon system, which was set in July 2024 and which includes facilities rental from Sabey Data Centers as well as other operational costs, is still $457 million. (We are attempting to confirm this as we write, but in the wake of SC25 and ahead of the Thanksgiving vacation, it is hard to reach people.)

Godfather of AI Predicts Total Breakdown of Society

Geoffrey Hinton, one of the three so-called “godfathers” of AI, never misses an opportunity to issue foreboding proclamations about the tech he helped create.

During an hour-long public conversation with Senator Bernie Sanders at Georgetown University last week, the British computer science laid out all the alarming ways that he forecasts AI will completely upend society for the worst, seemingly leaving little room for human contrivances like optimism. One of the reasons why is that AI’s rapid deployment will be completely unlike technological revolutions in the past, which created new classes of jobs, he said.

“The people who lose their jobs won’t have other jobs to go to,” Hinton said, as quoted by Business Insider. “If AI gets as smart as people — or smarter — any job they might do can be done by AI.”

Developer to build data center near Samsung in Taylor

TAYLOR, Texas (ABJ) — A Dallas-based developer is proposing to turn a 220-acre parcel directly northeast of Samsung Electronics Co. Ltd.’s rising factory in Taylor into a data center campus.

KDC will be considered by the Taylor Planning and Zoning Commission on Nov. 12 for an employment center plan for the site at 1,051 County Road 401 for what it’s calling “Project Comal.” Details are minimal, but it would have primary data center uses along with a small lot of space for commercial, civic and other uses, according to city documents.

KDC representatives declined to comment.

Japan’s vision for AI robots to empower humans

What if instead of replacing us in our jobs, AI-enabled robots were to help us become the best versions of ourselves? Prompted by the ageing crisis and a projected shortfall of carers, a research team in Japan is seeking to create a new robotic paradigm, where AI-enabled robots help us to help ourselves.

“By 2050, I’d like to realize a smarter, more inclusive society, where everyone will be able to use AI robots anytime and anywhere,” says Yasuhisa Hirata, a mechanical engineer at Tohoku University in Sendai, Japan1. Hirata is the project manager on the ‘Adaptable AI-enabled Robots to Create a Vibrant Society’ project of the Japanese Government’s Moonshot Research and Development Program.

He envisages future AI-enabled robots functioning somewhere between a carer and a coach — a tool that can provide support, but which makes users feel as though they are performing tasks independently rather than being assisted by a robot. Such tasks might range from people standing up out of a chair, lifting a heavy object, or expressing themselves through dance.

Taiwan-based tech company to locate first US manufacturing facility in Georgetown

Pegatron officials will start construction on the Georgetown facility before the end of the year, the news release states. The company will invest a minimum of $35 million in capital in the city, and will hire at least 100 employees within the first three years of opening.

“The jobs and investment this corporation is bringing to Georgetown mark a milestone in our community’s economic growth,” Georgetown Mayor Josh Schroeder said in the news release. “Their decision to put down roots here will have a lasting, positive impact on our community and the broader region for generations.”

Large language models prioritize helpfulness over accuracy in medical contexts, finds study

Large language models (LLMs) can store and recall vast quantities of medical information, but their ability to process this information in rational ways remains variable. A new study led by investigators from Mass General Brigham demonstrated a vulnerability in that LLMs are designed to be sycophantic, or excessively helpful and agreeable, which leads them to overwhelmingly fail to appropriately challenge illogical medical queries despite possessing the information necessary to do so.

Findings, published in npj Digital Medicine, demonstrate that targeted training and fine-tuning can improve LLMs’ abilities to respond to illogical prompts accurately.

“As a community, we need to work on training both patients and clinicians to be safe users of LLMs, and a key part of that is going to be bringing to the surface the types of errors that these models make,” said corresponding author Danielle Bitterman, MD, a faculty member in the Artificial Intelligence in Medicine (AIM) Program and Clinical Lead for Data Science/AI at Mass General Brigham.

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