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Meta Superintelligence Labs' first paper is about RAG

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Re: Meta Superintelligence Labs' first paper is about RAG

#91

Post by godelski »

crystal_revenge wrote: post #82
godelski wrote: post #40

Earlier quoted context omitted.

A lot of people also don't know that many of the well known papers are just variations on small time papers with a fuck ton more compute thrown at the problem. Probably the strongest feature that correlates to successful researcher is compute. Many have taken this to claim that the GPU poor can't contribute but that ignores so many other valid explanations... and we wonder why innovation has slowed... It's also weird…

> A lot of people also don't know that many of the well known papers are just variations on small time papers with a fuck ton more compute thrown at the problem. I worked for a small research heavy AI startup for a bit and it was heart breaking how many people I would interact with in that general space with research they worked hard and passionately on only to have been beaten to the punch by a famous lab that could…

  > only to have been beaten to the punch by a famous lab that could rush the paper out quicker and at a larger scale.
This added at least a year to my PhD... Reviewers kept rejecting my works saying "add more datasets" and such comments. That's nice and all, but on the few datasets I did use I beat out top labs and used a tenth of the compute. I'd love to add more datasets but even though I only used a tenth of the compute I blew my entire compute budget. Guess state of the art results, a smaller model, higher throughput, and 3rd party validation were not enough (use an unpopular model architecture).

I always felt like my works were being evaluated as engineering products, not as research.

  > a few instances of high-probability plagiarism
I was reviewing a work once and I actually couldn't tell if the researchers knew that they ripped me off or not. They compared to my method, citing, and showing figures using it. But then dropped the performance metrics from the table. So I asked. I got them in return and saw that there was no difference... So I dove in and worked out that they were just doing 99% my method with additional complexity (computational overhead). I was pretty upset.

I was also upset because otherwise the paper was good. The results were nice and they even tested our work in a domain we hadn't. Were they just upfront I would have gladly accepted the work. Though I'm pretty confident the other reviewers wouldn't have due to "lack of novelty."

It's a really weird system that we've constructed. We're our own worst enemies.

  > their job at the big labs is to churn out papers.
I'd modify this slightly. Their job is to get citations. Churning out papers really helps with that, but so does all the tweeting and evangelizing of their works. It's an unfortunate truth that as researchers we have to sell our works, and not just by the scientific merit that they hold. People have to read them after all. But we should also note that it is easier for some groups to get noticed more than others. Prestige doesn't make a paper good, but it sure acts as a multiplying factor for all the metrics we use for determining if it is good.

Re: Meta Superintelligence Labs' first paper is about RAG

#93

Post by godelski »

vasco wrote: post #88
godelski wrote: post #84

Earlier quoted context omitted.

So what is your argument, that it doesn't apply everywhere therefore it applies nowhere? You're misunderstanding the root cause. Your example works as the the metric is well aligned. I'm sure you can also think of many examples where the metric is not well aligned and maximizing it becomes harmful. How do you think we ended up with clickbait titles? Why was everyone so focused on clicks? Let's think about engagement…

> So what is your argument, that it doesn't apply everywhere therefore it applies nowhere? I never said that. Someone said the law collapses, someone asked for a link, I gave an example to prove it does break down in some cases at least, but many cases once you think more about it. I never said all cases. If it works sometimes and not others, it's not a law. It's just an observation of something that can happen or no…

  > I never said all cases.
You're right. My bad. I inferred that through the context of the conversation.

  > If it works sometimes and not others, it's not a law.
I think you are misreading and that is likely what lead to the aforementioned misunderstanding. You're right that it isn't a scientific law, but the term "law" gets thrown around a lot in a more colloquial manner. Unfortunately words are overloaded and have multiple meanings. We do the same thing to "hypothesis", "paradox", and lots of other things. I hope this clarifies the context. (even many of the physics laws aren't as strong as you might think)

But there are many "laws" used in the same form. They're eponymous laws[0], not scientific ones. Read "adage". You'll also find that word used in the opening sentence on the Wiki article I linked as well as most (if not all) of them in [0]

[0] https://en.wikipedia.org/wiki/List_of_eponymous_laws

Re: Meta Superintelligence Labs' first paper is about RAG

#94

Post by xvector »

hamasho wrote: post #62
godelski wrote: post #15

It's kinda funny, Meta has long had some of the best in the field, but left them untapped. I really think if they just took a step back and stop being so metric focused and let their people freely explore then they'd be winning the AI race. But with this new team, I feel like meta mostly hired the people who are really good at gaming the system. The people that care more about the money than the research. A bit of th…

My theory is that as more people compete, the top candidates become those who are best at gaming the system rather than actually being the best. Someone has probably studied this. My only evidence is job applications for GAFAM and Tinder tho.

I have seen absolutely incredible, best in the world type engineers, much smarter than myself, get fired from my FAANG because of the performance games.

I persist because I'm fantastic at politics while being good enough to do my job. Feels weird man.

Re: Meta Superintelligence Labs' first paper is about RAG

#95

Post by calf »

jongjong wrote: post #4

Interesting. All developers I know who tinkered around with embeddings and vector similarity scoring were instantly hooked. The efficiency of computing the embeddings once and then reusing as many times as needed, comparing the vectors with a cheap IMO vector embedding is the most important innovation in computing of the last decade. There's something magical about it. These people deserve some kind of prize. The ide…

The idea of reducing language to mere bits, in general, sounds like it would violate the Godel/Turing theorems about computability.

Re: Meta Superintelligence Labs' first paper is about RAG

#96

Post by xvector »

fishmicrowaver wrote: post #23
Workaccount2 wrote: post #18

Earlier quoted context omitted.

I think at this point it's software devs looking for the value unlock. Non-software devs are actually making functional programs for themselves for the first time ever. The value is crazy.

Sure but I'm the real world do you think businesses are going to deploy piles of code into production generated this way? No, non technical people will continue to whip up MS PowerApps. AI generated code has no value to many businesses.

The value of AI is not in generating code. That's just a "nice-to-have."

The value of AI is in having a scalable, human-like decision maker that you can plug into anything, anywhere. This has unlocked countless use cases for my team, that we could scarcely imagine a few years ago.

Re: Meta Superintelligence Labs' first paper is about RAG

#97

Post by godelski »

MR_Bulldops wrote: post #86
vasco wrote: post #76

Earlier quoted context omitted.

Metric: time to run 100m Context: track athlete Does it cease to be a good metric? No. After this you can likely come up with many examples of target metrics which never turn bad.

Do you have an example that doesn't involve an objective metric? Of course objective metrics won't turn bad. They're more measurements than metrics, really.

  > an objective metric
I'd like to push back on this a little, because I think it's important to understanding why Goodhart's Law shows up so frequently.

*There are no /objective/ metrics*, only proxies.

You can't measure a meter directly, you have to use a proxy like a tape measure. Similarly you can't measure time directly, you have to use a stop watch. In a normal conversation I wouldn't be nitpicking like this because those proxies are so well aligned with our intended measures and the lack of precision is generally inconsequential. But once you start measuring anything with precision you cannot ignore the fact that you're limited to proxies.

The difference of when we get more abstract in our goals is not too dissimilar. Our measuring tools are just really imprecise. So we have to take great care to understand the meaning of our metrics and their limits, just like we would if we were doing high precision measurements with something more "mundane" like distance.

I think this is something most people don't have to contend with because frankly, very few people do high precision work. And unfortunately we often use algorithms as black boxes. But the more complex a subject is the more important an expert is. It looks like they are just throwing data into a black box and reading the answer, but that's just a naive interpretation.

Re: Meta Superintelligence Labs' first paper is about RAG

#99

Post by godelski »

bjornsing wrote: post #77
hamasho wrote: post #62

Earlier quoted context omitted.

My theory is that as more people compete, the top candidates become those who are best at gaming the system rather than actually being the best. Someone has probably studied this. My only evidence is job applications for GAFAM and Tinder tho.

Yeah I think this is a general principle. Just look at the quality of US presidents over time, or generations of top physicists. I guess it’s just a numbers game: the number of genuinely interested people is relatively constant while the number of gamers grows with the compensation and perceived status of the activity. So when compensation and perceived status skyrockets the ratio between those numbers changes drasti…

I think the number of generally interested people goes up. Maybe the percent stays the same? But honestly, I think we kill passion for a lot of people. To be cliche, how many people lose the curiosity of a child? I think the cliche exists for a reason. It seems the capacity is in all of us and even once existed.

Re: Meta Superintelligence Labs' first paper is about RAG

#100

Post by contrarian1234 »

godelski wrote: post #15

It's kinda funny, Meta has long had some of the best in the field, but left them untapped. I really think if they just took a step back and stop being so metric focused and let their people freely explore then they'd be winning the AI race. But with this new team, I feel like meta mostly hired the people who are really good at gaming the system. The people that care more about the money than the research. A bit of th…

> Labs used to hire researchers and give them a lot of free reign.

I can't think of it ever really paying off. Bell Labs is the best example. Amazing research that was unrelated to the core business off the parent company. Microsoft Research is another great one. Lots of interesting research that .. got MS some nerd points? But has materialized into very very few actual products and revenue streams. Moving AI research doesn't help Meta build any motes or revenue streams. It just progresses our collective knowledge.

On the "human progress" scale it's fantastic to put lots of smart people in a room and let them do their thing. But from a business perspective it seems to almost never pay off. Waiting on the irrational charity of businesses executive is probably not the best way to structure thing.

I'd tell them to go become academics.. but all the academics I know are just busy herding their students and attending meetings

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