191–200 of 379 posts
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Can you explain what you mean about 'not needing to be solved'? There are versions of that kind of critique that would seem, at least on the surface, to better apply to finance or flash trading. I ask because scaling an system that a substantially chunk of the population finds incredibly useful, including for the more efficient production of public goods (scientific research, for example) does seem like a problem tha…
I think the problem I see with this type of response is that it doesn't take into context the waste of resources involved. If the 700M users per week is legitimate then my question to you is: how many of those invocations are worth the cost of resources that are spent, in the name of things that are truly productive? And if AI was truly the holy grail that it's being sold as then there wouldn't be 700M users per week…
The person attempting to learn either (hopefully) figures out the AI model was wrong, or sadly learns the wrong material. The level of impact is probably quite relative to how useful the knowledge is one's life.
The good or bad news, depending on how you look at it, is that humans are already great at rewriting history and believing wrong facts, so I am not entirely sure an LLM can do that much worse.
Maybe ChatGPT might just kill of the ignorant like it already has? GPT already told a user to combine bleach and vinegar, which produces chlorine gas. [1]
I work at Google on these systems everyday (caveat this is my own words not my employers)). So I simultaneously can tell you that its smart people really thinking about every facet of the problem, and I can't tell you much more than that. However I can share this written by my colleagues! You'll find great explanations about accelerator architectures and the considerations made to make things fast. https://jax-ml.git…
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A lot of really smart people working on problems that don't even really need to be solved is an interesting aspect of market allocation.
Well, we all thought advertising was the worst thing to come out of the tech industry, someone had to prove us wrong!
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Heat pump sure, but how is gas furnace more efficient than resistive load inside the house? Do you mean more economical rather than more efficient (due to gas being much cheaper/unit of energy)?
Depends where your electricity comes from. If you're burning fossil fuels to make electricity, that's only about 40% efficient, so you need to burn 2.5x as much fuel to get the same amount of heat into the house.
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What do you mean 10 years? You can pick up a DGX-1 on Ebay right now for less than $10k. 256 GB vRAM (HBM2 nonetheless), NVLink capability, 512 GB RAM, 40 CPU cores, 8 TB SSD, 100 Gbit HBAs. Equivalent non-Nvidia branded machines are around $6k. They are heavy, noisy like you would not believe, and a single one just about maxes out a 16A 240V circuit. Which also means it produces 13 000 BTU/hr of waste heat.
> 13 000 BTU/hr In sane units: 3.8 kW
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Someone's take on AI was that we're collectively investing billions in data centers that will be utterly worthless in 10 years. Unlike the investments in railways or telephone cables or roads or any other sort of architecture, this investment has a very short lifespan. Their point was that whatever your take on AI, the present investment in data centres is a ridiculous waste and will always end up as a huge net loss…
If it is all a waste and a bubble, I wonder what the long term impact will be of the infrastructure upgrades around these dcs. A lot of new HV wires and substations are being built out. Cities are expanding around clusters of dcs. Are they setting themselves up for a new rust belt?
AI development is for rich people right now. Maybe when the bubble pops and the hardware becomes more accessible, we'll start to see some actual value come out of the tech from small companies or individuals.
I work at Google on these systems everyday (caveat this is my own words not my employers)). So I simultaneously can tell you that its smart people really thinking about every facet of the problem, and I can't tell you much more than that. However I can share this written by my colleagues! You'll find great explanations about accelerator architectures and the considerations made to make things fast. https://jax-ml.git…
> So I simultaneously can tell you that its smart people really thinking about every facet of the problem, and I can't tell you much more than that. "we do 1970s mainframe style timesharing" there, that was easy
If the requests were regularly spaced, and they certainly won’t be, but for the sake of argument, then 1 machine could serve 17,000 requests per day, or 120,000 per week. At that rate, you’d need about 5,600 machines to serve 700M requests. That’s a lot to me, but not to someone who owns a data center.
Yes, those 700M users will issue more than 1 query per week and they won’t be evenly spaced. However, I’d bet most of those queries will take well under 1 second to answer, and I’d also bet each machine can handle more than one at a time.
It’s a large problem, to be sure, but that seems tractable.
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Even is the AI bubble does not pops, your prediction about those servers being available on ebay in 10 years will likely be true, because some datacenters will simply upgrade their hardware and resell their old ones to third parties.
Would anybody buy the hardware though? Sure, datacenters will get rid of the hardware - but only because it's no longer commercially profitable run them, presumably because compute demands have eclipsed their abilities. It's kind of like buying a used GeForce 980Ti in 2025. Would anyone buy them and run them besides out of nostalgia or curiosity? Just the power draw makes them uneconomical to run. Much more likely ev…