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Discovery Loop

discoveryloop.com

581–590 of 625 posts

Re: Discovery Loop

#581

Earlier quoted context omitted.

Yes! But if science is bottlenecked by funding, making it cheaper might help?

Not really. Grad students are already essentially working for free.

But they require large support systems (typically rich parents) to work for "free". Scholarships mean they're not free.

Re: Discovery Loop

#582
post #263

Earlier quoted context omitted.

These people are all already making 9 figure compensation packages, I think if they thought they could do the work they wanted at Google, they would.

9 figure is hardly enough when some kid sells their vscode fork to them for more, is it? Why not just boomerang and get $$$.

Did that really happen? What was the fork you are talking about?

Re: Discovery Loop

#583
post #241

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…

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…

I've bet a portion of my current companys platform against this (laboratory operating platform for people doing the biolab work)... uh oh. In all seriousness I think the reality is this is just going to create a lot more work for humans to have to verify and test in lab so I think it will net out in the end as a good thing for me if the barrier for new labs is lowered while the amount of required data and verification lab work is expanded by AI work being published.

Re: Discovery Loop

#584

I keep being reminded of Eisenhower’s “plans are useless, but planning is indispensable”. While “useless” might be a harsh term, surely he is onto something in attaching a higher value to the process that produced a result than the result itself. What if “science” wasn’t about the results? What happens if you keep the “plans” but drop the “planning”? I deeply wonder how AI will impact our personal ability to remain c…

We'll adapt. So far, it seems like every technological increase has demanded more cognitive ability, not less. Sure, we may not need 100 people who are experts at shoveling, but the people developing, maintaining, and even operating the excavators all have to be "smarter". All the thinking, learning, and training needed to stay alive in the cold might have been mostly lost when fire was controlled, but it comes with its own risks and learning curve.

I know AI is "smart", so it might hinder us there, but I doubt it can be as damaging as doomscrolling has been on human brains.

Re: Discovery Loop

#585
post #483

Earlier quoted context omitted.

I don’t understand why all of these companies need to frame it like this. Why not a large amount of people doing an even larger amount of science?

When you define prosperity in large amount of people, it makes you sound like a communist, and America spent decades to avoid that. The other alternative is to sound like a capitalist, which is very much ok by today’s standards.

Guys I’m joking btw, left the comment during my jest mood

Re: Discovery Loop

#586
One thing I wonder about is how these high-profile startups handle their engineering hiring - in this case, it's just a link to an Ashby form.

They probably already got 10,000 resumes in the past 24 hours, wonder what they do and how effective this is.

Anyone already apply there, what was the process?

Re: Discovery Loop

#587
post #511

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…

Sounds a bit like everything and anything to be honest. Focus?

Looks like they are concentrating on No. 14 and it remains to be seen how much serious progress can be made if more of Google's resources are focused on this than anybody else has ever done or could compare to.

Plus it should be plain to see how complete the solution is to 14, whether it will be fully solved, or almost completely, before diverting resources toward moving up the list to tackle other worthwhile objectives. If not fully solved I would not call that abandonment, but nobody could deny it would amount to an intentional slowdown regardless.

I still remember the day when Google became available on the general internet, and it's been a while. Take it from an old science dude, a lot can be detected over decades of observation that you can not get any other way. Under laboratory conditions or not ;) Looking at the list there are a few standouts that I can't imagine Google wouldn't be worlds ahead by now if they had only doubled-down on the "Don't Be Evil" mission every time they had the chance, rather than watering it down as we have seen.

Naturally I'm biased after doing 14 my whole life without real justification for moving up the list myself, since I still have no complete solution, I'm only human after all.

Re: Discovery Loop

#588

I am siding with the "intelligence is not the bottleneck" crowd. Science takes more than reading literature and making a hypothesis. You have to run the experiment. And that is where messy reality will crush the naive, and resist any attempt to package it up into a factory-like innovation engine. But they will take your money, should you have some to invest.

I'm a scientist. On the one hand I take some comfort in thinking that I will always have an advantage in the lab. On the other hand I'm not taking anything for granted. And my advantage in the lab has to translate into an employer being smart enough to keep me around until if and when the AI takes over, which kind of translates into their investors wanting to keep me around. We know what happened to manufacturing whe…

Are you suggesting outsourcing? AI produces hypothesis, a chief scientist approves, and it’s outsourced to a lab in Vietnam to execute.

Re: Discovery Loop

#589
post #586

One thing I wonder about is how these high-profile startups handle their engineering hiring - in this case, it's just a link to an Ashby form. They probably already got 10,000 resumes in the past 24 hours, wonder what they do and how effective this is. Anyone already apply there, what was the process?

I assume that, like most really trendy companies, they hire mostly by internal referral.

Re: Discovery Loop

#590

Earlier quoted context omitted.

> Make Solar Energy Economical Isn’t it already?

People on HN keep saying it is, but I'm still not seeing it. Companies are building datacenters in vast, sun-blasted deserts and still choosing to power those with natural gas. This in turn makes people complain about emissions pledges being reversed, but if it were economical, no pledge would be needed. Of course, USA has cheaper oil/gas than other countries. But if you look elsewhere, rich countries are subsidizing…

To power an AI data center with off-grid solar panels, you'd need about 45,000 acres of land devoted to your solar farm. The panels themselves could be literally free and the natural gas plant would probably be more economical.
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