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Discovery Loop

discoveryloop.com

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Re: Discovery Loop

#391

Post by xyzsparetimexyz »

ajam1507 wrote: post #381
ramon156 wrote: post #60

"Our mission is straightforward" continued by the most complex sentence on that page. Wondering what the definition of straightforward is now

> Our mission is straightforward: we are building AI solutions that can automatically solve important problems in machine learning, science, and engineering. Genuinely curious which part you found complex.

We are building _ solutions

(building solutions != building a thing. Can't you just say 'solving'?)

That can _ solve _ problems in _, domains

(Wait so the solutions are only the thing that solves the actual thing?)

Re: Discovery Loop

#392

Post by DannyBee »

BrenBarn wrote: post #344
sejje wrote: post #247

Earlier quoted context omitted.

Why shouldn't they? Also, wouldn't anyone with half a brain use the human-free system to produce another human-free system that was no longer controlled by the "small number of 'people'"?

> Why shouldn't they? Because it's evil?

It's what the vast majority of software engineers have been doing for decades in practice, and i guess pretending they weren't?

They only seem to care now because it affects them.

Re: Discovery Loop

#393

Post by analog31 »

SubiculumCode wrote: post #372

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 when investors were no longer interested in it.

Re: Discovery Loop

#395

Post by xyzsparetimexyz »

jschveibinz wrote: post #350

Here are just a few of viewpoints on what constitute world problems to solve: https://80000hours.org/problem-profiles/ https://en.wikipedia.org/wiki/List_of_global_issues https://encyclopedia.uia.org/ Interestingly, one list identifies "AI" as a top world problem! One person's problem is another person's solution, I guess--and vice versa, as well. An extreme example: curing a disease is good for patients but bad for…

No its not. Everyone dies. Healthcare is invoked in everyone's life. Generally the longer you live the more healthcare you'll need.

Re: Discovery Loop

#396

Post by malaproping »

anon-3988 wrote: post #356
beloch wrote: post #241

Earlier quoted context omitted.

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…

> "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." I don't think the US have this capability because you guys don't really have manufacturing that is really necessary for scientific research. For example, if I want a highly toxic chemical, how difficult it would be to…

> you guys don't really have manufacturing that is really necessary for scientific research

Yea no high quality science research happens in the US? What?

Re: Discovery Loop

#397

Post by unclebucknasty »

cjbarber wrote: post #18

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…

This reads as hype of the kind: "well, we can't reliably do these rather mundane things with AI, but we're going to run it extra hard and extra long in some novel way, and it will do amazing things".

The issues are with verification and with detecting drift from the goal. These are related, if not roughly the same issue. And, if they can solve this, then they will have essentially fixed AI. Maybe even AGI.

But, if this were the goal, then it seems more reasonable to solve the relatively more mundane verifiable challenges (e.g. generating solid, reliable code). Then, working up from there.

And, that's exactly what gives this the hype smell. No use for solving problems that don't get the oohs and aahs. Just straight to NAE Grand Challenge problems.

Re: Discovery Loop

#398

Post by xyzsparetimexyz »

roughly wrote: post #131

Two to keep in mind with these kinds of things - 1. There’s some irreducible costs in terms of time and material in the physical world that are not amenable to the kind of optimization or parallelization or even just the raw speedup from Moore’s law or computational architecture improvements we’re used to with software. My experience is primarily in biology, where the examples here are things like “it takes 20 minute…

Thats why you simulate e coli at 30x in a sim environment that fable slopped together. Duh

Re: Discovery Loop

#400

Post by anr0 »

with billion dollar seed rounds becoming the norm, it seems like there's no longer an advantage to build from within these bloated giants. can be much nimbler and have access to the same budget out the gates
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