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21 hours agoOver 15% marketshare in India
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Cake day: July 5th, 2024
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3·4 days agoBasically nothing is ever truly zero
That’s why you need to pay for premium so you can get the 1080p premium bitrate.
flat earth society scientific newsletter
Such a contradictory sentence. Can’t wait for The Final Experiment to be done with, so we can eject any remaining flerfs from society.
I think the best bet is an entirely new system from the ground up that has an open architecture that every company can equally implement that from the ground up and is as simple as possible.
This keeps getting said by people who don’t understand operating systems. Even if you build something from the ground up, you still end up with an operating system very much like Linux and Windows. The choices that were made for each OS were not random. The principles of I/O, user input, graphics display, filesystems, etc, are more or less universal concepts across all OSes.
What you will accomplish is making an OS that no one will use. Linux, Windows, and macOS already fill every market that can be filled. Microsoft tried to become a third player in the mobile market and their product died pretty quickly.
Google has been trying to build Fuschia into a new OS and they’ve asked back their ambitions (from what I recall reading).
Fun fact: the nubs on the tail club are called osteoderms and the tail spikes on a stegosaurus is called a thagomizer.
Porn addicts get bored of regular porn and go down a dark rabbit hole.
This has been disproven over and over. The only people who go to the “darker stuff” are people who are already inclined. They just work themselves up to it by going through the regular stuff.
It’s the same thing with serial killers, they warm up to it with animals. Which is why someone killing animals is a massive warning sign.
No, I’m not comparing serial killers to porn addicts. I’m comparing the process of warming up to the extreme stuff by first doing the less extreme stuff.
Finally something we can agree on. Continuing this is a waste of time.
You don’t accept evidence, so there’s nothing left to be said.
Since we’re naming fallacies: appeal to authority. I’m a Astronaut Scientist Millionaire Cowboy and I say you’re wrong
Can you get your fallacy definitions right at least? It’s not appeal to authority if the person being referenced has the qualifications or experience in the subject being discussed. I have worked with the technology for a decade. I’ve trained countless neural network models for various purposes. I understand the technology.
Begging the question
No. You are literally trying to debate established facts.
Ad Hominem
This would be true if I didn’t address the point multiple times. This was me offering an explanation for why you keep getting it wrong.
Then you should have linked those, not Wikipedia
I did link to multiple scientific sources. You just gave up before even getting to halfway.
“Modeled loosely on the human brain.” So again your source straight up says it does not function like a human brain.
No, it literally says in multiple sections (that I quoted) that neural networks are designed by modelling biological brains. It doesn’t matter if it’s “loosely”, “exactly”, “somewhat”, or “kinda”. It’s modelled “loosely” because the human brain is incredibly complex. Quite possibly the most complex thing known of. The distinction here in the ONE quote you cherry-picked is that it said human brain. The distinction is the word “human”.
Interesting how you cut out the words “prevents the LLM from” that immediately preceded that.
I literally didn’t. It’s literally in my quote on italics. I’ll refer to my previous (ad hom) statement about your reading comprehension.
None of that indicates a capacity to reason.
Then go back to the links you conveniently skipped over.
I thought we were talking about LLMs, not ANNs, and an attempt to emulate does not imply success.
It hurts. You actually hurt my brain. An LLM is literally an artificial neural network. How do trolls like you actually think?
Someone with an actual argument doesn’t need to resort to personal attacks every other paragraph.
Nothing I said is a personal attack. Remaking that you must not have good reading comprehension is insulting, but not a personal attack.
They can simply present their argument.
I have; very simply, in fact. I just genuinely do not think you have the reading comprehension or capacity to understand.
Ah yes, you’ve been getting a lot of “support and agreement” from the other people reading your comments.
Sure, the 10 people who commented on this post who are not reading our convo is such an indication of support.
My god you’re thick.
What you just did is called “digging a deeper hole”.
Like I said, I’ve worked in the industry for over a decade. What I said isn’t even up for debate. If you had a shred of understanding you know how astoundingly wrong what you said is. In fact, if you had a shred of understanding you just flat out wouldn’t have said it.
Amazon is not a source.
Someone trying to sell their LLM to the general public, and therefore simplifying the language to convey a concept is not a source.
Straight up genetic fallacy.
Wikipedia is not a source.
You’re right. It’s not a “source”. It’s a source aggregator. You know that list of little tiny text at the bottom of each page? Those are “references” from credible sources that are cited.
I’ll give you an example. The quote from Wikipedia I provided has a little “1” and a little “2” right at the end of the sentence. If you click on them it’ll take you to the cited source.
The little “1” will bring you to the following page:
https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
Here are some excerpts:
Modeled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected.
particular network layouts or rules for adjusting weights and thresholds have reproduced observed features of human neuroanatomy and cognition, an indication that they capture something about how the brain processes information.
https://www.sciencedirect.com/topics/neuroscience/artificial-neural-network
It resembles the human brain in two respects: The knowledge is acquired by the network through a learning process, and interneuron connection strengths known as synaptic weights are used to store the knowledge.
They imitate somewhat the learning process of a human brain because they learn the relationship between the input parameters and the controlled and uncontrolled variables by studying previously recorded data.
ANN is a computational model that is based on a machine learning technique. It works like a human brain neuron system.
Directly linked to in the Science Direct page from Wikipedia:
https://www.sciencedirect.com/science/article/abs/pii/B9780444528551500118
Artificial neural networks (ANNs) are computational models that attempt to emulate the architecture and function of the human brain (Russell and Norvig, 1995).
So does not function like a brain does.
Now I know you’re either 14 or just not very smart. You directly quoted the source with This prevents LLMs from performing deliber- ate planning akin to human brains,
It’s literally in the sentence, it said “deliberate planning akin to human brains”. It doesn’t say anywhere in that sentence that neural networks aren’t modelled after brains and it doesn’t say anything about reasoning (the two things you keep refuting).
Aaand I’m going to stop checking your sources now
Convenient for your “argument”.
Read your sources and make sure they say what you think they do
I have. You just can’t read, have reading comprehension issues, or simply can’t understand them.
If you present me with another pile of links and the first one is invalid I won’t bother looking at the 2nd.
I don’t care if you do. Anyone else who reads these comments will see you’re out of your depth.
Citation needed.
Certainly!
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains
A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain.
A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain
*A neural network, or artificial neural network, is a type of computing architecture that is based on a model of how a human brain functions *
Would you like some more citations?
Thinking LLMs are capable of reasoning is the digital equivalent of putting eyes on a pencil then feeling bad when it gets broken in half.
In this paper, we present Reasoning via Planning (RAP), a novel LLM reasoning framework that equips LLMs with an ability to reason akin to human-like strategic planning
Source - Reasoning with Language Model is Planning with World Model
Motivated by the observation that adding more concise CoT examples in the prompt can improve LLM reasoning performance
LegalBench - a tool to evaluate the reasoning performance of an LLM in the legal domain.
A paper on benchmarking an LLMs temporal reasoning.
Shall I provide some more?
That’s a bad analogy, because the calculator wasn’t trained using an artificial neural network literally designed by studying biological brains (aka biological neutral networks).
And “understand” doesn’t equate to consciousness or sapience. For example, it is entirely and factually correct to state that an LLM is capable of reasoning. That’s not even up for debate. The accuracy of an LLM’s reasoning capability is one of the fundamental benchmarks used for evaluating its quality.
But that doesn’t mean it’s “thinking” in the way most people consider.
Edit: anyone up voting this CileTheSane clown is in the same boat of not comprehending how LLMs work.
Here you go
Coincidentally, this is exactly how llms work
Everyone who says this doesn’t actually understand how LLMs work.
Multivector word embeddings create emergent relationships that’s new knowledge that doesn’t exist in the training dataset.
Computerphile did a good video on this well before the LLM craze.
Both sentences are true. And based on vocabulary of both, the model can output the following sentences:
- Cats have feathers.
- Birds have fur
This is not how the models are trained or work.
Both are false but the model doesn’t “know” it. All that it knows is that “have” is allowed to go after both “cats” and “birds”, and that both “feathers” and “fur” are allowed to go after “have”.
Demonstrably false. This isn’t how LLMs are trained or built.
Just considering the contextual relationships between word embeddings that are created during training is evidence enough. Those relationships from the multi-vector fields are an emergent property that doesn’t exist in the dataset.
If you want a better understanding of what I just said, take a look at this Computerphile video from four years ago. And this came out before the LLM hype and before ChatGPT 3, which was the big leap in LLMs.
I got little to no patience towards people who distort what others say,
My original reply was meant to be tongue-in-cheek, but I guess I forgot about Poe’s law. I’m not a layman, for the record. I’ve worked with AI for over a decade
Not wasting my time further with you.
Ditto. Have a nice day.
I know you are, but the argument that an LLM doesn’t understand context is incorrect. It’s not human level understanding, but it’s been demonstrated that they do have a level of understanding.
And to be clear, I’m not talking about consciousness or sapience.
Dunno. I don’t live there.