Earlier quoted context omitted.
Very interesting take
I don't understand what is interesting about it: it's the default. Markets don't run on altruism.
DSpark: Speculative decoding accelerates LLM inference [pdf]
81–90 of 393 posts
Re: DSpark: Speculative decoding accelerates LLM inference [pdf]
#82Earlier quoted context omitted.
Chinese companies (and labs) operate in conjunction with the CCP so whatever they're doing, it's because it's Chinese state policy. What became clear when DeepSeek came onto the scene was that China was seeking to commoditize LLMs. They consider it an issue of national security not to be beholden to US tech companies when it comes to AI. And I, for one, fully endorse this policy. Another data point on this is the bla…
> Another data point on this is the black market for Claude tokens in China [1]. The chat logs themselves are a commodity to train models. anyone with IQ higher than 130 (thus qualified for actual AI R&D) would be questioning something obvious here - if they are already doing such dodgy stuff with the aim to maximize profits, why would those resellers have large amount of logs with actual American model responses to…
Re: DSpark: Speculative decoding accelerates LLM inference [pdf]
#83Earlier quoted context omitted.
Which is a good thing. Self-serving motives are more reliable than altruistic ones.
The world runs on incentives. Altruism/Self-serving are down stream of that. Wikipedia is altruistic, and serves humanity quite well.
Re: DSpark: Speculative decoding accelerates LLM inference [pdf]
#84Earlier quoted context omitted.
Chinese labs are also still behind, so they’re incentivized to collaborate and have no reason to do it in private. I suspect their tune will change if they ever take the lead..
Projection is a funny thing. It causes people to misread situations all the time. Southern slaveowners feared violent retribution from freed slaves, for example [1]. It was pure projection and said more about the South than it did the slaves. The reality was there was no violent retribution. It was the opposite where the former slaveowners continued to inflict violence on the formerly enslaved. I say this because we…
Meanwhile, Xi Jinping has published his 5th book on how governance in China works and what they're after. These are not books written for a western audience: they're compilations of speeches that he already gave to the Chinese party and state apparatus, so the contents are not sanitized for foreign audiences. But there are no English reviews of summaries of this 5th book at all by the usual China experts that distribute what western audience know about China.
This extends to beyond the government. Even though "for the people but only against the government" is an often-heard mantra, nobody seems to listen to what Chinese AI companies themselves say about why they publish open models. DeepSeek and GLM have said multiple times publicly what their motivations are, yet people on HN still speculate like they usually do.
Truly mind-boggling. I get that a lot of people don't like China. But setting aside the question of whether their dislike is justified, it would at least be rational to properly understand China, even if it's to defeat it. And listening to what China says themselves is absolutely essential for proper understanding. But people don't bother to? And they seem mostly happy with sticking to speculations that match preconceived notions, even if that hurts their chances of defeating China.
Re: DSpark: Speculative decoding accelerates LLM inference [pdf]
#85DeepSeek continues to not only push the boundaries but also publish these incredible papers explaining how they achieved their gains - something the American labs no longer do unfortunately. Chinese labs are doing the most interesting work in AI right now.
Probably because American AI companies are on the hook for quite a lot of investment money. I think they are trying to find the magical moat to justify their valuation. Revealing optimizations similar to these would pretty much reduce their competitive position.
Re: DSpark: Speculative decoding accelerates LLM inference [pdf]
#86DeepSeek continues to not only push the boundaries but also publish these incredible papers explaining how they achieved their gains - something the American labs no longer do unfortunately. Chinese labs are doing the most interesting work in AI right now.
R1 was very influential on US models development.
Re: DSpark: Speculative decoding accelerates LLM inference [pdf]
#87Earlier quoted context omitted.
Probably because American AI companies are on the hook for quite a lot of investment money. I think they are trying to find the magical moat to justify their valuation. Revealing optimizations similar to these would pretty much reduce their competitive position.
Who is financing DeepSeek and what are they expecting in return?
Re: DSpark: Speculative decoding accelerates LLM inference [pdf]
#88DeepSeek continues to not only push the boundaries but also publish these incredible papers explaining how they achieved their gains - something the American labs no longer do unfortunately. Chinese labs are doing the most interesting work in AI right now.
Chinese companies (and labs) operate in conjunction with the CCP so whatever they're doing, it's because it's Chinese state policy. What became clear when DeepSeek came onto the scene was that China was seeking to commoditize LLMs. They consider it an issue of national security not to be beholden to US tech companies when it comes to AI. And I, for one, fully endorse this policy. Another data point on this is the bla…
Re: DSpark: Speculative decoding accelerates LLM inference [pdf]
#89Earlier quoted context omitted.
Publishing by necessity I wonder? American labs on the cutting edge pioneering the way forward, so Deepseek open sourcing what they’ve got is to help even the playing field. Hopefully the experts here can offer insight. The above is just my hunch and I’m not a specialist in this field.
Wouldn’t that just help the American labs anyway though? Or do they assume they’ve actually already figured this stuff out and kept it secret?
So, despite hiring the cream of the crop of math graduates, who could read the papers of free academia, but whose own result the free world could not access - they fell behind.
I have a theory explaining why. I think it's because science is an interactive process. NSA cryptographers could read papers, but they couldn't talk openly with the authors of those papers, because of secrecy demands - even asking question might indicate what they were working on. You can easily imagine them spending months on something they could have avoided by going to the original authors and getting told "Oh, we tried that for a long time, it doesn't work".
Whether that theory is right or not, cryptography is a concrete example of a domain where public research with fewer resources beat private research with a lot more resources.
Re: DSpark: Speculative decoding accelerates LLM inference [pdf]
#90DeepSeek continues to not only push the boundaries but also publish these incredible papers explaining how they achieved their gains - something the American labs no longer do unfortunately. Chinese labs are doing the most interesting work in AI right now.
Publishing by necessity I wonder? American labs on the cutting edge pioneering the way forward, so Deepseek open sourcing what they’ve got is to help even the playing field. Hopefully the experts here can offer insight. The above is just my hunch and I’m not a specialist in this field.