TropicalDingdong
@TropicalDingdong@lemmy.world
- Comment on sussy baka 35 minutes ago:
Looks an awwwwful lot like spyrogrya…
- Comment on In the Green Zone 7 hours ago:
I don’t want to be an American idiot.
- Comment on The USA's economy is based on letting children decide what they want to do with money that isn't theirs 10 hours ago:
Roblox?
- Comment on California introduces age verification law for all operating systems, including Linux and SteamOS — user age verified during OS account setup 23 hours ago:
Colorado Dems pushing a similar law rn.
Fucking idiots.
- Comment on Hey Beter 1 day ago:
🅱️ensi
- Comment on Rabbit holes, girl; have you never heard of them? 1 day ago:
How’s it going down there?
- Comment on Stubborn, maybe, but if it ain't broke 1 day ago:
Because they already are and no one seems to be able to stop them.
Can’t out croc the croc.
- Comment on according to sugar daddy’s net worth actually 1 day ago:
- Comment on A modest proposal 1 day ago:
Would also be legit hilarious.
- Comment on Rabbit holes, girl; have you never heard of them? 2 days ago:
Find someone’s who looks at you the way their fiance looks at the Spanish monarchy.
- Comment on The script is mysterious and important. 3 days ago:
- Comment on The script is mysterious and important. 4 days ago:
Real eyes realize.
- Comment on Nvidia delivers first Vera Rubin AI GPU samples to customers — 88-core Vera CPU paired with Rubin GPUs with 288 GB of HBM4 memory apiece 4 days ago:
Yeah I’ve read that before. I don’t necessarily agree with their framework. And even working within their framework, this article is about a challenge to their third bullet.
I’m just not quite ready to rule out the idea that if you can scale single models above a certain boundary, you’ll get a fundamentally different/ novel behavior. This is consistent with other networked systems, and somewhat consistent with the original performance leaps we saw (the ones I think really matter are ones from 2019-2023, its really plateaued since and is mostly engineering tittering at the edges). It genuinely could be that 8 in a MoE configuration with single models maxing out each one could actually show a very different level of performance. We just don’t know because we just can’t test that with the current generation of hardware.
Its possible there really is something “just around the corner”; possible and unlikely.
- Comment on Nvidia delivers first Vera Rubin AI GPU samples to customers — 88-core Vera CPU paired with Rubin GPUs with 288 GB of HBM4 memory apiece 5 days ago:
288 GB HBM4 memory
jfc…
Looking at the spec’s… fucking hell these things probably cost over 100k.
I wonder if we’ll see a generational performance leap with LLM’s scaling to this much memory.
- Comment on We all forget that for a time they were out of style. This 1940 magazine proudly announced that they were back 5 days ago:
big ole pile
I too prefer the big ol’ pile of bobas
- Comment on ‘A feedback loop with no brake’: how an AI doomsday report shook US markets 5 days ago:
Well for one, that area already burned pretty recently. So its pretty unlikely to burn again any time soon.
But as part of a larger picture:
The area does experience fire-weather conditions for some portion of the year:
Here we’re looking at HDWI (hot dry windy index), where a “loose” definition of fire weather is if HDWI is above 200. HDWI is based on a few factors, namely, how hot it is, how dry it is, and how fast the air is moving. Hot dry air moving quickly = fire weather.
The number of fire weather days per year has been increasing, and in very recent years (the past decade) the rate of change has increased, and become statistically signficant:
So its not a particularly fire prone area, but its getting worse, and its getting worse at a faster rate.
That would be the first part of the analysis I would run. After that, we’d look for historically “anomalous” periods. Its not enough to look at averages; that will wash over important features in the data. We need to look for specific periods where fire weather manifests.
This is another way of thinking about fire risk. Here we’re going to count the amount of time, after 12 hours, that an area is in sustained fire-weather conditions. Basically, a bit of time in bad conditions isn’t the end of the world, but as you stay in fire weather conditions, fire risk increases exponentially (as plants/ fuels continue to dry out).
If I were writing an insurance product for you, I would count the number of events in a given magnitude bucket and give you a risk rating. Here, licking my thumb and sticking it in the air, I would say… “not that bad”.
Much of my work is around modeling in the wilderness urban interface. You picked an almost all wilderness area. Since there are no structures, I cant do the next analysis, but it would looks something like this:
Most of my work is about figuring out what the impacts of wildfire on the built environment are going to be. Also, the free structure dataset I have access to doesn’t cover Canada and I’m not going to spend money buying the structures for you (unless you REALLY want me to).
Those first figures are all specific to the coordinates you provided. The final figure is just an example.
- Comment on ‘A feedback loop with no brake’: how an AI doomsday report shook US markets 5 days ago:
Gimme some coordinates.
- Comment on ‘A feedback loop with no brake’: how an AI doomsday report shook US markets 5 days ago:
Tldr at the bottom
I’m literally submitting a transformers paper for publication this week. They’re truly incredible. They’re a huge step forwards from where we we at. But so was YOLO, and UNET, and lstm’s (kinda, they were a bit meh).
But there is a secondary claim about llms, chat bot/agentic llm specifically, that they’re doing things they simply arent. And I do pay for higher tier access so I at least think I’m using some of the state of the art of these things.
I think you are full of it if you dont admit they have revolutionized a large number of highly technical fields
I’m specifically saying they haven’t, at least, that if you are using Claude or chatgpt to do those things, you aren’t doing what you think you are doing. Domain experts who use these tools recognize their limitations, and limitation is a soft way of putting it. They just get shit fundamentally wrong. And sometimes, when you are working on a complex problem, if you don’t have the knowledge or experience to know when something is wrong, you’ll believe these machines are doing far more than they are.
Look I use them regularly. I can support up to 128gb models locally. I understand the claim that these things have utility. But after several years of working with them, I genuinely don’t think they actually are capable of supporting the claims businesses are making about them.
For one, while they can help you solve some problems faster, often, they just make the situation far, far worse, and you spend an inordinate amount of time trying to get the thing to do something a specific way, but it just won’t. I think this is related to the half glass of wine issue, which I’ll come back to.
Second, they, as far as I’ve been able to use them, are utter dogshit at returning to a codebase. If you are trying to get them to have some kind of long term comprehension of what’s happening in a project, good fucking luck. You end up with a codebase of constant refactors and stupid useless “sanity” checks that creates the appearance of good practices, but is all smoke and mirrors. They seem to work ok for single shot demos, but you could never run a business or build a program that’s worth keeping around where the llm is central to managing the process. And there is more to say in this because when you are building up a codebase, the most fundamental thing you are really building up is a vision of how it all fits together. When you outsource this to LLMs, you don’t get the vision, and frankly they don’t either. What you end up with is maybe functional at first, but inevitably unstable, and unsustainable.
Third, and maybe this is me, but I’ve never actually seen an llm come up with a clever solution to anything. Like not once have I seen it come up with a truly elegant, efficient solution. It’s almost always the most banal, solution, and more often then not, it’s not even a solution, but a work around that avoids the problem entirely while creating the impression of a solution.
And to be clear. I’m not talking about mundaun hello world statements. I’m talking about things that undergrads and graduate students miss all the time. I’m talking about gotchas and problems that you need somethings decades plus to know that the fundamental assumptions are flawed. There is something more inherent to the issues they create.
I think the half glass of wine issue has been papered over and remains the core limitation with LLMs, and represents a fundamental issue with either transformers, or maybe gradient decent, and I don’t think this current architecture is going to get us past it. You are probably familiar with the issue, it got traction a while back, but the hot fixed the phenomena and it lost media attention. However, if you know what you are looking for, you’ll find non image based examples of this all the time when using LLMs. They’ll constantly insist they’ve done something they haven’t. And there will be no obvious way to get them to recognize they haven’t done or aren’t doing the thing. I don’t believe any of the philosophy explanations given in the YouTube coverage of the issue. I think the problem is likely more core, more central to machine learning that credit is being given.
The concern I have is that this is something more fundamental, and were only noticing it because image generation and natural language are something humans can comprehend and notice the issue in. But what about when it becomes something incomprehensible to humans, like a sequence of weather data or output from a sensor. We would have no ability to notice if ml model is doing the same thing that an llm is doing, effectively lying about it’s about.
Long rant over shortly.
Tldr
I think don’t contend the massive advances transformers represent as an architecture. But there is clearly something rotten or missing at their core which makes them practically self destructive to rely upon beyond superficial, well solved issues. I think the rot is in the math or the approach to training and I don’t think there is any amount of engineering that can unbake the sawdust out of the cake.
- Comment on ‘A feedback loop with no brake’: how an AI doomsday report shook US markets 6 days ago:
I just…
Am I wrong here? Like, look, shame me. I work in machine learning and have since 2012. I don’t do any of the llm shit. I do things like predicting wildfire risk from satellite imagery or biomass in the amazon, soil carbon, shit like that.
I’ve tried all the code assistants. They’re fucking crap. There’s no building an economy around these things. You’ll just get dogshit. There’s no building institutions around these things.
- Comment on US military leaders pressure Anthropic to bend Claude safeguards 6 days ago:
They want to automate the kill bots and Anthropic is saying no.
- Comment on Did I discover a fake conspiracy theory? 6 days ago:
Fitzroy Fitzroy? In this economy?
- Comment on Did I discover a fake conspiracy theory? 6 days ago:
Fitzroy Barnaby
Theres no way thats a real persons name.
- Comment on AI Is Destroying Grocery Supply Chains 1 week ago:
i mean i guess total collapse is a form of revolution
- Comment on Down bract. 1 week ago:
based and spadix pilles
- Comment on Jack Dorsey's New Company Falling Apart as It Forces Employees to Use AI 1 week ago:
Is that thumbnail a scene from 12 monkeys?
- Comment on Judge scolds Mark Zuckerberg's team for wearing Meta glasses to social media trial 1 week ago:
a small amount of jailtime is a slap on the wrist. A scolding is nothing.
- Comment on Fancycles 1 week ago:
Weird desire to sit on your ballss’ lap and tell them what I want for Christmas.
- Comment on Dbzero has Defederated from Feddit.org following its Governance post about the later's Zionist Bar Problem 1 week ago:
This thread even 18 hrs later:
- Comment on Dbzero has Defederated from Feddit.org following its Governance post about the later's Zionist Bar Problem 1 week ago:
You insist on not trying to understand, I’m done.
No, its that you continue to insist that your “nuance” isn’t apoligism.
You insist on not trying to understand, I’m done.
This isn’t a train station. No need to announce your departure. Everyone here can read your words and judge you for themselves, and they are, and they see you apologizing for genocide. You should take the time of your departure to mediate on why that might be.
- Comment on Dbzero has Defederated from Feddit.org following its Governance post about the later's Zionist Bar Problem 1 week ago:
The thing is called a thing and should be called a thing.
Ok. Well thats not what you are doing and its not what German law or Feddit is doing. People are trying to call a thing a thing, and are getting push back saying “You can’t call this thing that thing”
