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That's the intention. Fill it up with enough jargon and gobbledegook that it looks impressive to investors, while hiding the fact that there's no real technolog
by ipsum2 11mo ago
That's the intention. Fill it up with enough jargon and gobbledegook that it looks impressive to investors, while hiding the fact that there's no real technology underneath.
- frozenseven 11mo ago>jargon and gobbledegook >no real technology underneath They're literally shipping real hardware. They also put out a paper + posted their code too. Flippant insults will not cut it.
- ipsum2 11mo agoNice try. It's smoke and mirrors. Tell me one thing it does better than a 20 year old CPU.
- frozenseven 11mo agoMore insults and a blanket refusal to engage with the material. Ok.
- ipsum2 11mo agoIf you think comparing hardware performance is an insult, then you have some emotional issues or are a troll.
- frozenseven 11mo agoAh, more insults. This will be my final reply to you. I'll say it again. The hardware exists. The paper and code are there. If someone wants to insist that it's fake or whatever, they need to come up with something better than permutations of "u r stoopid" (your response to their paper: https://news.ycombinator.com/item?id=45753471 https://news.ycombinator.com/item?id=45753471). Just engage with the actual material. If there's a solid criticism, I'd like to hear it too.
- maradan 11mo agoThis hardware is an analog simulator for Gibbs sampling, which is an idealized physical process that describes random systems with large scale structure. The energy efficient gains come from the fact that it's analog. It may seem like jargon, but Gibbs sampling is an extremely well known concept with decades of work with connections to many areas of statistics, probability theory, and machine learning. The algorithmic problem they need to solve is how to harness Gibbs sampling for large scale ML tasks, but arguably this isn't really a huge leap, it's very similar to EBM learning/sampling but with the advantage of being able to sample larger systems for the same energy.
- theamk 11mo ago> The algorithmic problem they need to solve is how to harness Gibbs sampling for large scale ML tasks, but arguably this isn't really a huge leap, Is it? The paper is pretty dense, but Figure 1 is Fashion-MNIST which is "28x28 grayscale images" - which does not seem very real-life for me. Can they work on a bigger data? I assume not yet, otherwise they'd put something more impressive for figure 1. In the same way, it is totally unclear what kind of energy are they talking about, in the absolute terms - if you say "we've saved 0.1J on training jobs" this is simply not impressive enough. And how much overhead is it - Amdahl law is a thing, if you super-optimize the step that takes 1% of the time, the overall improvement would be negligible even if savings for that step are enormous. I've written a few CS papers myself back in the day, and the general idea was to always put the best results at the front. So they are either bad communicators, or they don't highlight answers to my questions because they don't have many impressive things (yet?). Their website is nifty, so I suspect the latter.
- rcxdude 11mo agoThe fact that there's real hardware and a paper doesn't mean the product is actually worth anything. It's very possible to make something (especially some extremely simplified 'proof of concept' which is not actually useful at all) and massively oversell it. Looking at the paper, it looks like it may have some very niche applications but it's really not obvious that it would be enough to justify the investment needed to make it better than existing general purpose hardware, and the amount of effort that's been put into 'sizzle' aimed at investors makes it look disingenuous.
- frozenseven 11mo ago>The fact that there's real hardware and a paper doesn't mean the product is actually worth anything. I said you can't dismiss someone's hardware + paper + code solely based on insults. That's what I said. That was my argument. Speaking of which: >disingenuous >sizzle >oversell >dubious niche value >window dressing >suspicious For the life of me I can't understand how any of this is an appropriate response when the other guy is showing you math and circuits.
- rcxdude 11mo agoNo, they're not showing just math and circuits, they're also showing a very splashy and snazzy front page which makes all kinds of vague, exciting sounding claims that aren't really backed up by the very boring (though sometimes useful) application of that math and circuits (neat how the design of those circuits may be). If this was just the paper, I'd say 'cool area of research, dunno if it'll find application though'. I'm criticizing the business case and the messaging around it, not the implementation. Two important questions I think illustrate my point: 1) The paper shows an FPGA implementation which has a 10x speedup compared to a CPU or GPU implementation. Extropic's first customer would have leapt up and started trying to use the FPGA version immediately. Has anyone done this? 2) The paper shows the projected real implementation being ~10x faster than the FPGA version. This is similar to the speedup going from an FPGA to an ASIC implementation of a digital circuit, which is a standard process which requires some notable up-front cost but much less than developing and debugging custom analog chips. Why not go this route, at least initially?
- maradan 11mo ago"no really technology underneath" zzzzzzzzzzz
- fastball 11mo agoYou not comprehending a technology does not automatically make it vaporware.