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Frame from The Great Debate @ARITH23: John Gustafson and William Kahan Plays via YouTube's privacy-enhanced embed

The Great Debate @ARITH23: John Gustafson and William Kahan

dancefan
Talks
YouTube
1 HN user recommended this talk1 month2017–2023 score 1
Years with an HN recommendation, 2007–2026

It is the go-to primary source whenever HN discusses posits versus IEEE floats, valued for Kahan's sharp critique despite the poor audio.

A 95-minute recorded debate at the ARITH23 computer arithmetic conference between John Gustafson, proponent of unums/posits, and William Kahan, architect of IEEE 754 floating point. They argue the merits of the new number formats versus conventional floats and interval arithmetic.

“Audio quality is not great but there are links to slides from Gustafson and Kahan” (HN, 2017)

1 recommendations · 3 citations · Talks · advanced

4 Hacker News comments

From the HN archive; up to 20 kept per video, favoring different commenters. Recommendations first.

Citations, criticism and jokes

@zasdffaa Sep 2022 Cites it
on HN thread Posits, a New Kind of Number, Improves the Math of AI
Read more
I did some reading on unum a while back, inc. Kahan's criticisms. There's another side. There's a vid on youtube with Kahan and Gustafson talking over it. Kahan goes in like a wolf onto a lamb, much to gustafson's shock and hurt. Gustafson points out some of Kahan's criticisms are plain wrong, even citing the page in the book, then says something like "we should be working together on this, why aren't we working together?" Kahan doesn't respond. Kahan may be right or wrong but his attitude is weirdly hostile, and Gustafson is no n00b about floats. Edit: I think this is it (but it's been a while) [this video]
Kahan criticizing Gustafson's unum work in a hostile debate
@SideQuark Jan 2023 Cites it
on HN thread Will Floating Point 8 Solve AI/ML Overhead?
Original posits are variable width, making them nearly useless for high performance parallel computations. Later versions don't add anything of use for low precision neural networks, and lack of hardware support anywhere make them too slow for anything other than toying around. See also [link] and [this video]
Debate over posits cited as further reading

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