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Advancements in machine learning for machine learning

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Re: Advancements in machine learning for machine learning

#51

Post by huytersd »

kccqzy wrote: post #23
sbierwagen wrote: post #20

Earlier quoted context omitted.

It is interesting how persistently dominant GPT-4 is: https://twitter.com/lmsysorg/status/1735729398672716114 Off the top of my head, I can think for at least five foundation models (Llama, Claude, Gemini, Falcon, Mistral) that are all trading blows, but GPT is still a head above them and has been for a year now. Transformer LLMs are simple enough that, demonstrably, anyone with a million bucks of GPU time can make o…

Their only special sauce is the first-mover advantage. Then it attracted users (data), brand recognition, talent and became a positive feedback cycle.

That’s not it. It’s not just hype. The underlying model is better.

Re: Advancements in machine learning for machine learning

#52

Post by nodogoto »

sbierwagen wrote: post #44
m3kw9 wrote: post #19

Earlier quoted context omitted.

I want to see it come out with a cure for a disease that is tough to cure first. Singularity itself is pointless unless it benefits humans which is mainly in health/lower suffering

In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environment. Similarly, to avoid Thalidomide-style disasters, it would have to simulate the conception, development and growth to adulthood of a human. These things are... physically possible, but have WBE and uploads as a hard requirement. Those are going to affect a…

>In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environment.

Humans aren't capable of doing this, but still make useful drug discoveries. AI can be empowered to conduct research in the real world, it doesn't need to simulate everything.

Re: Advancements in machine learning for machine learning

#53

Post by jocoda »

kypro wrote: post #13
SirMaster wrote: post #11

Earlier quoted context omitted.

I don’t see why not. I like programming how I do now. I don’t plan to stop. People do lots of things manually that machines have been able to do for a long time.

You can do what you want, you just won't be paid to do it anymore.

I think most are going to wait on the side lines watching the bleeding edge. The promise is great but so is the risk of disaster. Imo it's going to be a generational shift.

Re: Advancements in machine learning for machine learning

#54

Post by falcor84 »

DeathArrow wrote: post #33
RyanShook wrote: post #4

The pace that ML seems to be advancing right now is amazing. I don’t believe in the singularity but it’s changing software and then society in ways no one can predict.

For me it's just another gold rush after dotcom, mobile, cloud, VR.

I'm not sure what the purpose of the word "just" there is. There indeed seems to be quite a lot of gold to be had by whoever gets a foothold.

Re: Advancements in machine learning for machine learning

#55

Post by cornel_io »

impegh wrote: post #28
educaysean wrote: post #22

Earlier quoted context omitted.

I'd say advancement in mathematics, computer science, and heck, even art is far from "pointless". Why does it feel like goalposts get moved everytime there is a significant progress in AI?

I read this same empty “goalposts” lament multiples times a day when reading this website, and though I know what the words mean I’m confused what you all think they mean. https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu... Is this just some performative grousing or do you really think what has been developed to date is “artificial intelligence”? These comments all conveniently fail to define their author…

There was a very recent time when passing some version of the Turing Test would have been a fairly commonly accepted goalpost. Many experts thought that was 20+ years away, and were perfectly comfortable saying that it was their "bar", primarily because they thought generating convincing conversational text was so difficult that you'd basically have to solve All The Problems(TM) first.

Notice how nobody is talking about the Turing Test anymore now that it's either already been passed or is very damn close? We can argue back and forth about whether the real stupidity was the earlier expectation that the Turing Test was a useful threshold for AI, but it's impossible to claim that it wasn't a somewhat common and well-known one, so that goalpost really has been moved in a very dramatic way (or rather, removed altogether and replaced with nothing in particular other than a vague "I'll know it when I see it", in most cases).

Re: Advancements in machine learning for machine learning

#56

Post by IanCal »

tommychillfiger wrote: post #25
wait_a_minute wrote: post #8

Earlier quoted context omitted.

This + FunSearch make it seem like Singularity is imminent. https://deepmind.google/discover/blog/funsearch-making-new-d...

At great risk of sounding completely ignorant, this approach is basically what I thought the point of machine learning was - cleverly using feedback loops to improve things automatically. The thing that sticks out to me as particularly cool about FunSearch is the use of programs as inputs/outputs and the fact that they managed to automate feedback. I'm pretty naive in terms of granular understanding here as I am bare…

The important thing is "how do you change X so that it heads towards the goal". And "how to do it quickly and efficiently".

Otherwise the description is the same as "select randomly, keep the best, iterate".

The goal is also complex. You might be thinking of "find the most efficient program" but that's not what we're doing here iiuc. We're trying to get a program that makes other unseen programs more efficient. That's hard to define as a goal.

Re: Advancements in machine learning for machine learning

#57

Post by zer00eyz »

AnthonyMouse wrote: post #49
sbierwagen wrote: post #44

Earlier quoted context omitted.

In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environment. Similarly, to avoid Thalidomide-style disasters, it would have to simulate the conception, development and growth to adulthood of a human. These things are... physically possible, but have WBE and uploads as a hard requirement. Those are going to affect a…

> In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environment. Similarly, to avoid Thalidomide-style disasters, it would have to simulate the conception, development and growth to adulthood of a human. This is how the human doctors who have cured things in the past have done it, is it? The way this is going to work,…

>> The way this is going to work...

Google is already doing something like this: https://arstechnica.com/ai/2023/11/googles-deepmind-finds-2-...

This is great if you want to use well understood pathways or make new drugs that you can then patent and mark up.

New pathways are gonna require feeding data into these models in the first place. Your not getting ozempic out of ML without doing the ground work first: https://globalnews.ca/news/9793403/ozempic-canada-scientist-...

Re: Advancements in machine learning for machine learning

#58

Post by creer »

cornel_io wrote: post #55
impegh wrote: post #28

Earlier quoted context omitted.

I read this same empty “goalposts” lament multiples times a day when reading this website, and though I know what the words mean I’m confused what you all think they mean. https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu... Is this just some performative grousing or do you really think what has been developed to date is “artificial intelligence”? These comments all conveniently fail to define their author…

There was a very recent time when passing some version of the Turing Test would have been a fairly commonly accepted goalpost. Many experts thought that was 20+ years away, and were perfectly comfortable saying that it was their "bar", primarily because they thought generating convincing conversational text was so difficult that you'd basically have to solve All The Problems(TM) first. Notice how nobody is talking ab…

Exactly so. The posts have been moved safely waayyyy over there at AGI, and at "super-human" or at "critical thinking". And several stages or degrees of AGI have been hierarchized. There is a serious reluctance at accepting how dumb an algorithm can be and still compare with humans.

But it is also true that numerous ground techniques are issue of the field of AI and generally called AI as they come out. It makes for good press. And that too was silly.

Re: Advancements in machine learning for machine learning

#59

Post by sbierwagen »

AnthonyMouse wrote: post #49
sbierwagen wrote: post #44

Earlier quoted context omitted.

In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environment. Similarly, to avoid Thalidomide-style disasters, it would have to simulate the conception, development and growth to adulthood of a human. These things are... physically possible, but have WBE and uploads as a hard requirement. Those are going to affect a…

> In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environment. Similarly, to avoid Thalidomide-style disasters, it would have to simulate the conception, development and growth to adulthood of a human. This is how the human doctors who have cured things in the past have done it, is it? The way this is going to work,…

Then AI will have no effect on the drug industry at all.

The rate limiting step isn't "thinking up molecules." The University of Bern enumerated all possible molecules composed only of hydrogen, carbon, nitrogen, oxygen, sulfur and chlorine, up to 17 atoms. That produced 166 billion molecules. https://pubs.acs.org/doi/10.1021/ci300415d There are commercial drugs considerably larger than that. We've got molecular structures out the nose. There is no shortage of molecules.

The problem is the clinical trial. Putting drugs in humans and seeing what they do. That's the part that takes years and tens of millions of dollars. Using AI for anything else is like saying Microsoft Powerpoint accelerated drug development. Sure, it made presentations easier, but did it do anything for the problem of putting chemicals in people?

Re: Advancements in machine learning for machine learning

#60

Post by Fbnkigffb66tfbj »

sublinear wrote: post #41
Der_Einzige wrote: post #17

Earlier quoted context omitted.

They’re making fun of your typo, but you’re right. Pretty much every software job in 5 years will be an AI job. This rustles a lot of feathers, but ignoring the truth will only hurt your career. I think the era of big tech paying fat stacks to a rather larger number of technical staff will start to wane as well. Better hope you have top AI paper publications and deep experience with all parts of using LLMs/whatever f…

Have you ever worked in tech and had to deal with the typical illiteracy and incompetence of management and execs? If LLMs got this good, the brick wall these orgs will hit is what will really ruffle feathers. Leadership will have to be replaced by their technical workers in order for the company to continue existing. There's simply not enough information in the very high level plain english requirements they're used…

> you very likely cannot feed that half-assed junk to any LLM no matter how advanced and expect useful results

Why don't you think that a sufficiently advanced AI can do the same as what technical humans do today with vague directions from managers?

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