Hm, it's only something like 10% of German traffic fatalities that occur on the autobahn. And according to wikipedia, Germany doesn't rank high in terms of traffic fatalities, even by European standards. France has a similar number of highway deaths. I'm personally not a fan of the autobahn and especially not the unrestricted speed. It seems obvious that it should cause lots of fatalities, but the evidence for it just doesn't seem to be there.
But in general: freeways/motorways (whatever you want to call them) almost never account for the majority of fatalities anywhere — there’s a lot that makes them safer than the average rural road even given comparable speeds, and there’s fewer vulnerable road users around.
Maybe I expressed myself poorly. Generally, higher speed is associated with higher fatality rates, all else being equal. So, one would assume a highway without speed limits would cause lots of fatalities. Most people would probably be surprised to learn that this is not the case.
>In my interactions with my kids public school and their teachers, they're goal is ram content down their throat and test for retention, not foster an environment open to questions
Is that actually true though? Average American students (especially those in the public school system) are not excellent test takers, and they're even worse at rote memorization. If this is actually the goal they're not achieving that either.
I would "just" do C-B PgUp and then use vi-like movement keys like hj, gg/G etc., and q to escape the pager, but I realize now that I say it that it doesn't sound very convenient or discoverable.
The price at the pump affects not only a voter's commuter car, but also every truck that delivers goods across the US. This may have a much larger knock-on effect.
OTOH the US is the largest oil producer in the world [1]. Theoretically the US could keep domestic prices in check, but that would require rather drastic administrative pressure, likely only legal at wartime.
This has to be intentional, right? To reassure people that front-end developers still have a job? The data is interesting but the site itself is a complete embarrassment for several reasons.
I think what you're describing is what people working with recommender systems call serendipity. Maximizing serendipity, while maintaining relatively high relevance/recommendation success rate, is supposedly a pretty difficult problem to solve. I'm not sure if LLMs have changed that.
This will sound snarky, so forgive me, but I honestly don't know the answer. Is this actually true? Is there a reliable source containing statistics on LLM compute usage that includes training vs inference for the whole market?
I don’t understand why people don’t just use Gemini or some other AI web search to get an answer to these kinds of questions quickly (I excluded the sources, you can get them if you ask the same question).
> While AI training is often the most intense and expensive process for a single model, the majority of total AI compute usage (approximately 90%) is used for inference.
> Here is the breakdown of why this is the case:
> Inference as High-Volume
> Activity: Inference occurs every time a user interacts with an AI model (e.g., asking ChatGPT a question, using image recognition, or generating code). While a model is trained once (or updated infrequently), it runs millions or billions of inferences continuously.
> Cost Scaling: Training is a massive, one-time upfront cost, while inference is an ongoing, daily operational cost. As the number of AI users grows, the demand for inference compute scales faster than the need for training new, large models.
> The Shift to Efficiency: While early AI hype focused on the immense compute needed for training, the industry has shifted toward making inference cheaper and faster through specialized hardware and techniques like optimization, quantization, and small language models (SLMs).
And I finally figured out how to get links to answers instead of just inlining the content as before. Anyways, there it is. We live in a time where questions like "Does inference or training use more compute?" can be answered quickly by just pasting it into a search box.
The revenue numbers are public for the major AI companies. That's probably the best estimate for "inference for the whole market" we have, since most of that inference is billed in either API usage or subscriptions, and it won't include any in-house usage such as training.
You obviously don't believe that AGI is coming in two release cycles, and you also don't seem to have much faith in the new models containing massive improvements over the last ones. So the answer to who is going to pay for these custom chips seems to be you.
If benchmarks are fishy, it seems their bias would be to produce better scores than expected for proprietary models, since they have more incentives to game the benchmarks.
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