361–370 of 500 posts
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That analogy would be if right if a startup could dredge beach sand and pump put trillions of AI chips. What actually happened was a better algorithm was created and people are betting against the main game in town for running said algorithm. If someone came up with a CPU-superior AI that'd be worrying for NVidia.
Groq lpu interference chip?
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And then some chineese startup create an amazing compiler that takes cuda and moves it to X (AMD, Intel, Asic) and we are back at square one. So far it seems that the best investment is in RAM producers. Unlike compute the ram requirements seem to be stubborn.
Don't forget that "CUDA" involves more than language constructs and programming paradigms. With NVDA, you get tools to deploy at scale, maximize utilization, debug errors and perf issues, share HW between workflows, etc. These things are not cheap to develop.
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I saw such robot's demos doing exactly that on youtube/x - not very precisely yet, but almost sufficiently enough. And it is just a beginning. Considering that majority of the laundry is very similar (shirts, t-shirts, trousers, etc..) I think this will be solved soon with enough training.
Can you share what you've seen? Because from what I've seen, I'm far from convinced. E.g. there is this, https://youtube.com/shorts/CICq5klTomY , which nominally does what I've described. Still, as impressive as that is, I think the distance from what that robot does to what a human can do is a lot farther than it seems. Besides noticing that the folded clothes are more like a neatly arranged pile, what about all the…
https://proceedings.neurips.cc/paper/2020/file/747e32ab0fea7...
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The usage of existing but cheaper nvidia chips to make models of similar quality is the main takeaway. It'll be much harder to convince people to buy the latest and greatest with this out there.
The sweet spot for running local LLMs (from what I'm seeing on forums like r/localLlama) is 2 to 4 3090s each with 24GB of VRAM. NVidia (or AMD or Intel) would clean up if they offered a card with 3090 level performance but with 64GB of VRAM. Doesn't have to be the leading edge GPU, just a decent GPU with lots of VRAM. This is kind of what Digits will be (though the memory bandwidth is going to be slower with because…
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Nah, it's mostly just buffering :-) Plus the cell phone industry paved the way for VOIP by getting everyone used to really, really crappy voice quality. Generations of Bell Labs and Bellcore engineers would rather have resigned than be subjected to what's considered acceptable voice quality nowadays...
>> Plus the cell phone industry paved the way for VOIP by getting everyone used to really, really crappy voice quality What accounts for this difference? Is there something inherently worse about the nature of cell phone infrastructure over land-line use? I'm totally naive on such subjects. I'm just old enough to remember landlines being widespread, but nearly all of my phone calls have been via cell since the mid 00…
Great article. I still feel like very few people are viewing the Deepseek effects in the right light. If we are 10x more efficient it's not that we use 1/10th the resources we did before, we expand to have 10x the usage we did before. All technology products have moved this direction. Where there is capacity, we will use it. This argument would not work if we were close to AGI or something and didn't need more, but I…
What's totally unclear is what data they used for this reinforcement learning step. How many math problems of the right difficulty with well-defined labeled answers are available on the internet? (I see about 1,000 historical AIME questions, maybe another factor of 10 from other similar contests). Similarly, they mention LeetCode - it looks like there are around 3000 LeetCode questions online. Curious what others think - maybe the reinforcement learning step requires far less data than I would guess?
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Unique, ye, but isn't their method open? I read something about a group replicating a smaller variant of their main model.
Which brings the question, if LLMs are an asset of such strategic value, why did China allow the DeepSeek to be released? I see two possibilities here, either that the CCP is not that all-reaching as we think, or that the value of the technology isn't critical, and that the release was further cleared with the CCP and maybe even timed to come right after Trump's announcement of American AI supremacy.
And it did a great job. Nvidia stock's sunk, and investors are going to be asking if it's really that smart to give American AI companies their money when the Chinese can do something similar for significantly less money.