I mean…there’s been plenty of people making PoCs showing Graphene isn’t really THAT secure, it’s probably just more obscure to a point. They’re pissed the cops have to work at it, but even somebody using Samsung or Google tools to properly sandbox certain data has the same capability to do so AFAIK.
Not only is this inaccurate, it still doesn’t make sense when you’re talking about a bipedal manufacturing robot.
Like motion capture, all you need to capture from remote operation of the unit is the input articulation from the operator, which is then translated into acceptable operation movements on the unit with input from its local sensors. The majority of these things (if using pre-cap operating data) is just trained on iterative scenarios and retrained for major environmental changes. They don’t use tele-operation live because it’s inherently dangerous and takes a lot of the local sensor inputs offline for obvious reasons.
OC is saying what all Robotics Engineers have been saying about these bipedal “PR Bots” for years: the power and effort to simply make these things walk is incredibly inefficient, and makes no sense in a manufacturing setting where they will just be doing repetitive tasks over and over.
Wheels move faster than legs, single purpose mechanisms will be faster and less error-prone, and actuation takes less time to train.
You never mentioned you were trying to mount live files or your home directory…that’s an entirely different thing.
Yes, it does matter.
RUST AGAIN.
Just throwing this out because I’ve been hammering this Rustholes up and down these threads who claim it’s precious and beyond compare 🤣
I will almost certainly link back to this comment in the future.
Rich assholes firing people to make the leftovers work twice as hard to “incorporate AI” into their workflow is though.
The job losses are fucking REAL, and everyone expects you to use this tedious bullshit now.
Did it…not have that already? I swear it did, but honestly I thought Exchange was dead long ago.
From your own linked paper:
To design a neural long-term memory module, we need a model that can encode the abstraction of the past history into its parameters. An example of this can be LLMs that are shown to be memorizing their training data [98, 96, 61]. Therefore, a simple idea is to train a neural network and expect it to memorize its training data. Memorization, however, has almost always been known as an undesirable phenomena in neural networks as it limits the model generalization [7], causes privacy concerns [98], and so results in poor performance at test time. Moreover, the memorization of the training data might not be helpful at test time, in which the data might be out-of-distribution. We argue that, we need an online meta-model that learns how to memorize/forget the data at test time. In this setup, the model is learning a function that is capable of memorization, but it is not overfitting to the training data, resulting in a better generalization at test time.
Literally what I just said. This is specifically addressing the problem I mentioned, and goes on further to exacting specificity on why it does not exist in production tools for the general public (it’ll never make money, and it’s slow, honestly). In fact, there is a minor argument later on that developing a separate supporting system negates even referring to the outcome as an LLM, and the supported referenced papers linked at the bottom dig even deeper into the exact thing I mentioned on the limitations of said models used in this way.
It most certainly did not…because it can’t.
You find me a model that can take multiple disparate pieces of information and combine them into a new idea not fed with a pre-selected pattern, and I’ll eat my hat. The very basis of how these models operates is in complete opposition of you thinking it can spontaneously have a new and novel idea. New…that’s what novel means.
I can pointlessly link you to papers, blogs from researchers explaining, or just asking one of these things for yourself, but you’re not going to listen, which is on you for intentionally deciding to remain ignorant to how they function.
Here’s Terrence Kim describing how they set it up using GRPO: https://www.terrencekim.net/2025/10/scaling-llms-for-next-generation-single.html
And then another researcher describing what actually took place: https://joshuaberkowitz.us/blog/news-1/googles-cell2sentence-c2s-scale-27b-ai-is-accelerating-cancer-therapy-discovery-1498
So you can obviously see…not novel ideation. They fed it a bunch of trained data, and it correctly used the different pattern alignment to say “If it works this way otherwise, it should work this way with this example.”
Sure, it’s not something humans had gotten to get, but that’s the entire point of the tool. Good for the progress, certainly, but that’s it’s job. It didn’t come up with some new idea about anything because it works from the data it’s given, and the logic boundaries of the tasks it’s set to run. It’s not doing anything super special here, just very efficiently.
Nah, I’m just not going to write a novel on Lemmy, ma dude.
I’m not even spouting anything that’s not readily available information anyway. This is all well known, hence everybody calling out the bubble.
🤦🤦🤦 No…it really isn’t:
Teams at Yale are now exploring the mechanism uncovered here and testing additional AI-generated predictions in other immune contexts.
Not only is there no validation, they have only begun even looking at it.
Again: LLMs can’t make novel ideas. This is PR, and because you’re unfamiliar with how any of it works, you assume MAGIC.
Like every other bullshit PR release of it’s kind, this is simply a model being fed a ton of data and running through thousands of millions of iterative segments testing outcomes of various combinations of things that would take humans years to do. It’s not that it is intelligent or making “discoveries”, it’s just moving really fast.
You feed it 102 combinations of amino acids, and it’s eventually going to find new chains needed for protein folding. The thing you’re missing there is:
It’s a tool for moving fast though data, a.k.a. A REALLY FAST SORTING MECHANISM
Nothing at any stage if developed, is novel output, or validated by any models, because…they can’t do that.
I sure do. Knowledge, and being in the space for a decade.
Here’s a fun one: go ask your LLM why it can’t create novel ideas, it’ll tell you right away 🤣🤣🤣🤣
LLMs have ZERO intentional logic that allow it to even comprehend an idea, let alone craft a new one and create relationships between others.
I can already tell from your tone you’re mostly driven by bullshit PR hype from people like Sam Altman , and are an “AI” fanboy, so I won’t waste my time arguing with you. You’re in love with human-made logic loops and datasets, bruh. There is not now, nor was there ever, a way for any of it to become some supreme being of ideas and knowledge as you’ve been pitched. It’s super fast sorting from static data. That’s it.
You’re drunk on Kool-Aid, kiddo.
Animal brains have pliable neuron networks and synapses to build and persist new relationships between things. LLMs do not. This is why they can’t have novel or spontaneous ideation. They don’t “learn” anything, no matter what Sam Altman is pitching you.
Now…if someone develops this ability, then they might be able to move more towards that…which is the point of this article and why the guy is leaving to start his own project doing this thing.
So you sort of sarcastically answered your own stupid question 🤌
Lol 🤣 I’m SO EMBARRASSED. You’re totally right and understand these things better than me after reading a GOOGLE BLOG ABOUT THEIR PRODUCT.
I’ll never speak to this topic again since I’ve clearly been bested with your knowledge from a Google Blog.
LLMs are just fast sorting and probability, they have no way to ever develop novel ideas or comprehension.
The system he’s talking about is more about using NNL, which builds new relationships to things that persist. It’s deferential relationship learning and data path building. Doesn’t exist yet, so if he has some ideas, it may be interesting. Also more likely to be the thing that kills all human.
This is code for “Hey government, you better be ready to bail us all out”.
They’re overpriced crap now. You buy at a 3x premium for the hardware, and you get the OS. That’s it.
Framework has a refurb store with deep discounts. No need to buy new.
MSI is going to be second on the list because they’ll regularly replace things out of warranty if they have spare parts lying around, or sell you entire main boards at a steep discount if a laptop board goes bad out of warranty. Same with all other parts.
Framework is the only correct answer here.
Just based on experience in the community and professional experience, I can solidly say that your take on FOSS not being successful is just wrong, and I don’t mean that like you’re stupid or I’m shooting you down, you just wouldn’t realize how huge contributions are unless you know where to look.
Here’s a big example: look how many companies hire for engineers writing Python, Ruby, Rust, Go, Node…whatever. ALL OPEN SOURCE LANGUAGES. You bootstrap a project in any of these, and you’re already looped into the FOSS community. 100% of the companies I have personally worked with and for write everything based on FOSS software, and I can tell you hands down as a fact: never met a single person writing in any closed source IDEs or languages, because very few exist.
If you want to see where all the community stuff happens, find any project on GitHub and look at the “Issues” section for closed tickets with PRs attached. You’ll see just how many people write quick little fixes to nags or bugs, not just on their own behalf, but on behalf of the companies paying them. That’s sort of the beauty of the FOSS community in general in that if you want to build on community projects, you’ll be giving back in one form another simply because, as my last comment said, NOBODY wants to maintain a private fork. Submodules exist for a reason, and even then people don’t want to mess with that, they’d rather just commit fixes and give back. Companies are paying engineers for their time, and engineers committing PR fixes is defacto those companies putting back into the community.
To your Oracle point, I think the biggest thing there you may have been Java. That one is tricky. Java existed long before it was ever open sources by Sun Microsystems, and was available for everyone sometime in the early '00s (not bothering to look that up). Even though it was created by an engineer at Sun, it was always out there and available for use, it just wasn’t “officially” licensed as Open Source for contributions until some time. Sun still technically owned the trademarks and all of that though, and Oracle acquired them at some point, bringing the trademarks under their ownership. There wete a number of immediate forks, but I think the OpenJDK crew was further out in front and sort of won that battle. To this day I don’t know a single Java project using Oracle’s official SDK and tools for that language aside from Oracle devs, which is a pretty small community in comparison, but you’re right in that was essentially a corporate takeover of a FOSS project. How successful it was in bringing people to bear that engagement I think is up for discussion, but I’m sure the community would rightly say “Fuck, Oracle” and not engage with their tooling.
No, that’s not what novel ideation is whatsoever 🤦
Again…these models work from a list of boundaries, logic, and rules made by humans. They don’t make it up themselves because…they.fucking.cant.
If they could make their own rules and conclusions without human intervention, then you have novel ideas. But…they.100%.FUCKING.CANT.DO.THAT.