331–340 of 630 posts
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Not really. Grad students are already essentially working for free.
How many lavishly-paid deans and bureaucrats and administrators are employed for each grad student?
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And what's the difference?
Agents act on their own. If the hammer looked at what you wanted nailed and said, "Sorry, Dave, I can't do that." There are degrees of autonomy, of course, and not all noncompliance is bad. Same as with humans; biological agents.
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Data centers need lots of power 24/7 and regardless of cloud cover. Solar is great to reduce your daytime bills but you still need other methods to cover the downtime. I would be surprised if data centers didn't put in gas _and_ solar.
That's why I'm surprised, they're doing like 100% gas.
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They make many bold promises, but their core goal is neatly encapsulated on the website: "Imagine a future where a handful of people can conduct scientific research and engineering tasks much more rapidly, and with higher quality, than massive teams of scientists and engineers do today." This is the same goal as every other AI company out there. Automate away the human employees and let a small number of "people" (no…
Humans have proven to be incapable of making real progress in science given the effort. Especially academia. We haven't cured many diseases and cancer when we should have (and no im not an amateur and yes I do believe we can "cure cancer". Debate me). We already have a trillionaire, whats the difference? The only difference I see is that people who were previously rich, but considered themselves middle class, are now…
There is currently a $800 SOTA blood test that can detect most cancers before any symptoms. Maybe a decade until it's a routine part of your annual blood test?
Whole genome sequencing costed $2.7 billion in 2003. You can get it done today using a mailed kit for $400.
HIV went from death sentence to all-but-cured in 50 years.
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> Note that Jeff and crew have cleverly structured their company to avoid problematic uses of AI Do you have more sources/info on this?
They do not call attention to this aspect of their new company, but it is implicit in their business model: https://xcancel.com/JeffDean/status/2085034604172603724 In short, they are in the business of using AI to automate the discovery of useful knowledge, not to sell general-purpose AI capabilities that could be directly used in troubling ways.
Sure, they'll keep it internal for a while to make sure their knowledge bank is more thorough than everyone else's, and because oftentimes discoveries can be far more convincing internally than externally (you need fewer sigmas for it to update your belief in a certain direction). But then how do they intend to profit from it in the end?
From Jeff's twitter post: > Our general approach is to automate the experimental loop. We think this approach is broadly applicable across many different fields of science and engineering. We’ll initially focus on ML research and engineering, but believe the approach can help with important subproblems in nearly every one of the fourteen NAE Grand Challenge problems. We think doing this well requires strong expertise…
Such a weird list. How is preventing nuclear terror an engineering problem?
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> As you said, Solar power is incredibly economical. Not if you include the cost of needed storage.
Yes It is. Even including batteries: https://www.iea.org/data-and-statistics/charts/lcoe-and-valu...
Sometime the sun goes away for more than 4hrs.
That may be OK for closed-ended systems (turn off the science at night and during storms), but not for open-ended systems with diverse user demand.
4hour batteries are competitive with gas peakers to match high demand during and after sunny times, but solar needs gas peakers or similar to over for non-sunny times.
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Not enough. The more economical it is, the better.
The paradox of solar is that more economical it is for the end users the less money is there to be made. So nobody wants to invest in it.
https://e360.yale.edu/digest/china-clean-tech-developing-cou...
From Jeff's twitter post: > Our general approach is to automate the experimental loop. We think this approach is broadly applicable across many different fields of science and engineering. We’ll initially focus on ML research and engineering, but believe the approach can help with important subproblems in nearly every one of the fourteen NAE Grand Challenge problems. We think doing this well requires strong expertise…
Would be great if they'd add: Reverse human aging. (Maybe a sub-topic under "Engineer Better Medicines".)