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I run thousands of robots in production. We can get a very high success rate but only for the task they're designed for. Production robots can't pick up stuff they drop yet. And this '80%' level is not actually acceptable or even state of art for just pick-and-place, but it's compelling for a robot that also knows how to do other things with equal quality (if JEPA does that).


I think actually you could do that if you wanted to; look up what notes mean, write some little program to make a sound if you had to. You could do it in a week if it was your only job.


but OP didn't give the AI a week it gave it less than a minute


We should not under-estimate the need for speed in supply chains. Predicting future demand is hard. To be more specific, we're talking about predicting ~100M unique products (the order of magnitude that moves on the pacific) and some of them have very lumpy demand (e.g. invent a new product, but it depends on 100 other obscure products).


We should also not over-estimate the need for speed. Just because some items need speed, it does not follow that all items need speed.


I think the core of the 'improved productivity' question will be ultimately impossible to answer. We would want to know if productivity was improved over the lifetime of a society; perhaps hundreds of years. We will have no clear A/B test from which to draw causal relationships.


This is exactly right. It also depends on how all the AGI promises shake out. If AGI really does emerge soon, it might not matter anymore whether students have any foundational knowledge. On the other hand, if you still need people to know stuff in the future, we might be creating a generation of citizens incapable of doing the job. That could be catastrophic in the long term.


This is a good point; a man or woman sitting behind a desk doing correlation analysis are going to look very similar in their function to a business. But they probably physically look pretty distinct to an x-ray picture.


Just because a whole industry is bullshit doesn't mean I should force it to not exist. I don't like musicals. I don't understand or care anything about their culture. But it has a right to exist. Some people are into musicals. Their existence or non-existence isn't my problem and it isn't my business. We cannot and should not try to engineer the world around what we personally find valuable and ignore what others find valuable, even if they got their opinions form an ad, or their parents did and they inherited it.


TINA = There is no alternative... to stocks. Lot of people lost money on long term bonds. Short term bonds are at least better than cash. Stocks are supposed to be about investing in an asset. I guess there is kind of an alternative out there.


Humans can do a lot of useful things with electrical energy. It's good to see our most powerful entities (companies) directly getting involved in getting the energy they need and still trying to do that in a climate-friendly way.


Of course, except that generative AI is not a "useful thing". While I'm not against nuclear energy, using that to off-set power hungry LLM's instead of... residences, businesses, or other anything else instead seems like a waste.


The jury is still on that long-term.


Sure, but on the other hand what happens if Gen AI bursts after all of this infra development?

- price of electricity goes wayyyyy down

- stigma around using atomic elements goes down

- price of excess available compute goes wayyy down (or the second hand market becomes side project budget range)

With excess electricity we can do a lot of things like Carbon Capture, water purification, power hadron colliders, replace gas stoves, etc. and with price of computer going down it makes more sense for certain industries to double down on tech. Essentially we are able utilise cheaper electricity + compute to patch over problems. On the other hand if the AI tech bros’s magic konch shell can lead innovations then I wouldn’t mind that either.


Sadly, that's still very non-causal. A lot of things in China were happening at the same time. We just need to be honest that we don't have causal data and we have to make decisions on correlation data (and not try to over-claim causal testing).


At a practical level, this is NetBSD story is a story of a machine that just does it's job and doesn't change. For example, there is no active experimentation, no machine learning added on, etc. It's like a wood-mill; good that it make 2x4s but also not a highly dynamic business. Probably not a business that's seen a lot of competition or having to adapt to changing markets and products.


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