OpenWALDO Webinar: Reducing AI Data Risk
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- Written by: Douglas Eadline
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From the found him department
Do you know the difference between closed-weight models, open-weight models, open-data, and open-source? More importantly, do you think it matters? In a recent webinar, the CEO of CIQ, Gregory Kurtzer, was joined by Addison Snell, CEO of Intersect360 Research, to discuss an emerging issue in the AI market: Unknown risk of model failure due to closed-data training. (e.g., not knowing where the model talking to your customers learned its manners, if at all.) The lack of open-data provenance in popular Foundation models may introduce risk for many organizations that are integrating AI into their business processes.
HPC Precision Wars: Satoshi Matsuoka Plants the Ozaki Flag
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- Written by: Douglas Eadline
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From the FP64 is not as boring as it used to be department
A recent paper submitted to ArXiv by famed HPC scientist Satoshi Matsuoka, Director of the RIKEN Center for Computational Science in Kobe, Japan, has shaken the tried-and-true FP64 HPC relationship to its core. The paper is entitled: FP8 is All You Need (Part 1): Debunking Hardware FP64 as the HPC Holy Grail: A Tensor–Memory Equilibrium Model and Implementation Strategy for Ozaki Scheme II on Memory-Bound Workloads in the Post-FP64 Era.
There is quite a lot to unpack in the paper, let alone the title. Matsuoka is basically positing that FP64 (64-bit double-precision arithmetic) hardware is not the best way to perform certain HPC computations that require 64-bit precision on newer Nvidia GPUs. Instead, he demonstrates how FP8 (8-bit floating-point operations) found in abundance on modern GPUs can be combined using the Ozaki Scheme II to achieve faster computation at the same 64-bit precision. This rebuke of legacy 64-bit floating-point technology is Matsuoka's flag-in-the-sand moment, and it will have consequences throughout the HPC industry. This article attempts to summarize some of the paper's major points; however, consulting the paper provides greater breadth and support for Satoshi's arguments and predictions.
Genesis Content: Where Does GenAI Get Its Next Meal?
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- Written by: Douglas Eadline
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From the well if you think about it department
Something about building GenAI LLMs bugs me. Before I begin, let me be clear: I am a supporter of AI technologies, particularly in science. Lately, however, a question keeps surfacing that I find hard to understand. GenAI promoters and sellers like to talk about "AI for Business" as a way to reduce costs and replace workers. Well, okay, all new technologies have that effect to some extent, but the GenAI industry seems to be touting GenAI as a replacement for the people who created the genesis content on which their technology was created. On the surface, this seems absurd; on closer inspection, it seems almost self-destructive. Talk about burning the ships while still at sea. There are many sectors where this question hits hard, but two specific topics of interest to me as a technical writer and sometimes programmer are content creation and programming.
A (mostly) Non-Technical AI Primer
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- Written by: Douglas Eadline
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from the long, but worthwhile article department
I have studied and watched artificial intelligence grow over the last forty years. Like many, back in 1968, I was inspired by HAL 9000 in Stanley Kubrick's 2001: A Space Odyssey. The question on my mind at the time was, "Is HAL 9000 possible?" Thus began my interest in learning all I could about computers and something called Artificial Intelligence.
By 1970, my excitement grew when I read a quote by AI pioneer Marvin Minsky (in Life magazine), "In three to eight years, we will have a machine with the general intelligence of an average human being." The future was so bright I was beyond inspired.
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