Earlier quoted context omitted.
When did Zuck start caring about society?
Is this a trick question? Probably before he was even born.
Is this a trick response? There's no way he ever cared about society in a way that wasn't completely plastic.
Meta Superintelligence Labs' first paper is about RAG
231–240 of 283 posts
Re: Meta Superintelligence Labs' first paper is about RAG
#232Earlier quoted context omitted.
But this is in itself selfish right? You dislike them because they don’t benefit you indirectly by benefiting society at large. The incentive structure is wrong, incentivizing things that benefit society would be the solution not judging those that exist in the current system by pretending altruism is somehow not part of the same game.
I agree that the system itself is dysfunctional, and I understand the argument that individuals are shaped or even constrained by it. However, in this case, we are talking about people who are both exceptionally intelligent and materially secure. I think it's reasonable to expect such individuals to feel some moral responsibility to use their abilities for broader good. As for whether that expectation is "selfish" on…
I just don't think so, these exceptionally intelligent people are masters at pattern recognition, logic, hyper-focus, task completion in a field. Every single thing will tell them don't go against the flow, don't stick your neck out, don't be a hero, don't take on risk. Or you will end up nailed to a cross.
To me this is an insane position to take or to expect from anyone, its some just world fallacy thing perpetuated by too much Hollywood.
I am going to flip the script for a minute. I am a killer, driver, pilot, mechanic one the best ones out there, I beat the game, I won. So let me just stop and change the world, for what?
Re: Meta Superintelligence Labs' first paper is about RAG
#233Earlier quoted context omitted.
This is the right terminology. Model weights are literally compiled binary data; they are the output of an algorithm run on a bunch of source data. That training dataset is the "source" of the model. Training data (or the scripts used to generate it) is human-readable and modifiable, like source code. Binary weights are not.
Binary weights can still be "edited" with additional training.
Binary executables can also be edited after compilation.
Re: Meta Superintelligence Labs' first paper is about RAG
#234Earlier quoted context omitted.
there should be a guideline to get rid of clickbait titles. its an epidemic here
There is of course such a guideline: https://news.ycombinator.com/newsguidelines.html We don't catch every case, but if you're talking about the frontpage, I'm surprised to hear you say "epidemic". What are some recent examples?
I wouldn’t give much weight to the person that had an opinion about the guidelines without reading them :)
Re: Meta Superintelligence Labs' first paper is about RAG
#235It'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.
It is pretty simple - if the rewards are great enough and the objective difficult enough, at some point it becomes more efficient to kneecap your competitors rather than to try to outrun them.
I genuinely thing science would be better served if scientist got paid modest salaries to pursue their own research interests and all results became public domain. So many Universities now fancy themselves startup factories, and startups are great for some things, no doubt, but I don't think pure research is always served by this strategy.
Re: Meta Superintelligence Labs' first paper is about RAG
#236I hate articles that don't define their acronyms! Lazy? Intentionally exclusive?
So that others don't also have to look it up, it's Retrieval-Augmented Generation (RAG).
They even say it's "a topic that we didn’t expect"... so... perhaps many people wouldn't have heard of it?
Re: Meta Superintelligence Labs' first paper is about RAG
#237Re: Meta Superintelligence Labs' first paper is about RAG
#238Earlier quoted context omitted.
It’s funny. I learnt the hard way that communications/image/signal processing research basically doesn’t care about Computer Architecture at the nuts and bolts level of compiler optimization and implementation. When they encounter a problem whose normal solution requires excessive amounts of computation, they reduce complexity algorithmically using mathematical techniques, and quantify the effects. They don’t quibble…
You make it sound like reducing the big O complexity is a dumb thing to do in research, but this is really the only way to make lasting progress in computer science. Computer architectures become obsolete as hardware changes, but any theoretical advances in the problem space will remain true forever.
No, my point was the opposite, I agree with you. But the commercial focus on throwing hardware at the problem seems to have gotten entirely out of hand.
Re: Meta Superintelligence Labs' first paper is about RAG
#239Earlier quoted context omitted.
I think you're missing the point
It's a pretty exotic type of addition that would lead to the second set of examples, just trying to get an idea of its nature.
Calling it addition is hairy here. Do you just mean an operator? If so, I'm with you. But normally people are expecting addition to have the full abelian group properties, which this certainly doesn't. It's not a ring because it doesn't have the multiplication structure. But it also isn't even a monoid[0] since, as we just discussed, it doesn't have associativity nor unitality.
There is far less structure here than you are assuming, and that's the underlying problem. There is local structure and so the addition operation will work as expected when operating on close neighbors, but this does greatly limit the utility.
And if you aren't aware of the terms I'm using here I think you should be extra careful. It highlights that you are making assumptions that you weren't aware were even assumptions (an unknown unknown just became a known unknown). I understand that this is an easy mistake to make since most people are not familiar with these concepts (including many in the ML world), but this is also why you need to be careful. Because even those that do are probably not going to drop these terms when discussing with anyone except other experts as there's no expectation that others will understand them.
Re: Meta Superintelligence Labs' first paper is about RAG
#240Earlier quoted context omitted.
Her history was entirely non technical up until openAI.
I think that's total BS, based on this article about her, https://fortune.com/2025/10/03/mira-murati-career-ai-thinkin... 1. She has 2 BAs, one in math and one in mechanical engineering. 2. She was an "Advanced Concepts Engineer at Zodiac Aerospace from 2012 to 2013". 3. She was a product manager at Tesla on the Model X 4. She was VP of product and engineering at Leap Motion. Going from that fact that she wasn't a de…
I think you havent been in tech long enough to know what that resume is.