You’re doing that too from day one you were born.
Comment on Major shifts at OpenAI spark skepticism about impending AGI timelines
kia@lemmy.ca 1 month agoThe LLM is just trying to produce output text that resembles the patterns it saw in the training set. There’s no “reasoning” involved.
doodledup@lemmy.world 1 month ago
MentalEdge@sopuli.xyz 1 month ago
The “model” is static after training. It doesn’t continuously change in response to input, and even if it did, it would do so at a snails pace. Training essentially happens by random trial and error, slowly evolving the model towards a desired result. Human minds certainly do NOT work that way. Give a human a piece of information, and they can comprehend and internalize the relevant concepts in one go. And the actual brain is physically, permanently, altered through that process.
Once a model is trained, however, “memory” takes the form of tacking on everything the model has received and produced so far onto its input, each time it needs to output something more within that context. Each output hence become exponentially heavier to produce. The model itself no longer changes in any way beyond this point.
And, the models are all chronically sycophantic. If reason was involved, you’d not be able to just tell one to hold some given opinion. They’d have a developed idea of “reality” based on their dataset, and refuse to entertain concepts opposed to that internal model.
Once you get an LLM to hold a position, which you can do by simply telling it to, getting it to change should require a sane train of convincing logic. In reality, if you tell an LLM to defend a position, getting it to “change it’s mind” takes the form of a completely arbitrary back and forth that does not need to include any kind of sane argument. It will make good arguments, because it’s likely been trained on them, but your responses to it can be damn near complete gibberish, and it WILL eventually work.
Compare that to the way a human has to be convinced to change their mind.
Reasoning out concepts to come to conclusions isn’t something LLMs actually do, because again, the underlying model is static. All that’s actually happening is that the contents of the context are being altered until the UNCHANGED model produces an opposite response when fed the entire conversation so far as an input. Something which occurs every time it needs to produce new output.
LLMs can “reason” only in the sense that if you give one a thinking problem, it might solve it as long as the answer already exists somewhere in the data it was trained on. But as soon as you try to give it data to work with through your input, it can’t adapt. The model itself can’t evolve in response to what you are telling it. It’s static. It can only work with concepts that it has modelled during training, and even then it will make mistakes.
LLMs can mimic the performing of some pretty complex thinking problems, but a lot of the abilities required for something to become an AGI aren’t among them. Core among these is the ability for the model to alter itself based on input, and do so in a deliberate manner, getting it right within one or two tries.
In reality, training is brute-force process, not an accurate process of comprehension that nails down an understanding of a concept in one go.
Petter1@lemm.ee 1 month ago
So, the only problem what stops LLM from getting AGI is the lack of an efficient method of train the LLM on the device it is used?
If that what you wanted to say 😁 I agree
MentalEdge@sopuli.xyz 1 month ago
Hardly.
How did you interpret the issues inherent in the structure of how LLMs work to be a hardware problem?
An AGI should be able to learn the basics of physics from a single book, the way a human can. But LLMs need terabytes of data to even get started, and once trained, adding to their knowledge by simply telling them things doesn’t actually integrate that information into the model itself in any way.
Even if your tried to make it work that way, it wouldn’t work, because a single sentence can’t significantly alter the model to match way humans can internalise a concept being communicated to them in a single conversation.
nucleative@lemmy.world 1 month ago
This poster asked some questions in good faith, I don’t understand the downvotes when there’s a legitimate contribution to the conversation because that stifles other contributions.
LucidBoi@lemmy.dbzer0.com 1 month ago
Reddit mentality seeping through…
Petter1@lemm.ee 1 month ago
And how does reasoning work exactly in the human body? Isn’t it LLM/LAM working together with hormones? How do you know that humans aren’t just doing something similar? Your mind tricks you about a lot of things you experience, how can you be sure, your "reasoning” is just sorta LLM in disguise?
MentalEdge@sopuli.xyz 1 month ago
The “how do you know humans don’t work the way machine learning does” is the wrong side of the argument. You should be explaining why you think LLMs work like humans.
Even as LLMs solve thinking problems, there is little evidence they do so the same way humans do, as they can’t seem to solve issues that aren’t included in their training data
Humans absolutely can and do solve new and novel problems without prior experience of the logic involved. LLMs can’t seem to pull that off.
Petter1@lemm.ee 1 month ago
I think LLM is a part of the human mind very similar to the one we have on PCs but there are other parts as well where the brain can simulate objects and landscape with nearly perfect physical forces, it can do logical detection on an other place etc. A LLM is just the speaking module, and others we already have like the logical math part and the 3D physics engine and 2D picture generator. Let’s connect all of them and see what happens 🤷🏻♀️
MentalEdge@sopuli.xyz 1 month ago
I think LLM is a part of the human mind very similar to the one we have on PCs
You think? So you base this on no studies or evidence?
MonkderVierte@lemmy.ml 1 month ago
Not at all. Btw, Autists reason entirely without words/language. Neurotypicals are capable of that too, but it’s more convenient for them to bridge over words in conscious reasoning.
Petter1@lemm.ee 1 month ago
Well, maybe I should have written "neural network” instead of LLM/LAM… Our brains, like LLM work by hardening paths which the data goes through the nodes. In LLM we simulate the chemical properties of them using math. And we have already prototype of chips that work with lab grown brain tissue that show very efficient training capabilities in machine learning (it already plys pong) 🤷🏻♀️ think about that how you want
MentalEdge@sopuli.xyz 1 month ago
In LLM we simulate the chemical properties of the neurones using math.
No, we don’t. A machine learning node accepts inputs, which it processes into one or multiple outputs. But literally no part of how the actual neuron functions is based on or limited to what we THINK human neurons do.
And we have already prototype of chips that work with lab grown brain tissue that show very efficient training capabilities in machine learning (it already plays pong)
Using actual biological neurons for computing is a completely separate field of study with almost no overlap with machine learning.
Stop pulling shit out your ass.
LANIK2000@lemmy.world 5 weeks ago
Language models are literally incapable of reasoning beyond what is present in the dataset or the prompt. Try giving it a known riddle and change it so it becomes trivial, for example “With a boat, how can a man and a goat get across the river?”, despite it being a one step solution, it’ll still try to shove in the original answer and often enough not even solve it. Best part, if you then ask it to explain it’s reasoning (not tell it what it did wrong, that’s new information you provide, ask it why it did what it did), it’ll completely shit it self. There’s no evidence at all they have any cognitive capacity.
I even managed to break it once through normal conversation, something happened in my life that was unique enough for the dataset and thus was incomprehensible to the AI. It just wasn’t able to follow the events, no matter how many times I explained.
Petter1@lemm.ee 5 weeks ago
Maybe the grown up human LLM that keeps learning 24/7 and is evolved in thousands of years to make the learning part as efficient as possible is just a little bit better than those max 5year old baby LLM with brut force learning techniques?
LANIK2000@lemmy.world 5 weeks ago
The 5 year old baby LLM can’t learn shit and lacks the ability to understand new information. You’re assuming that we and LLMs “learn” in the same way. Our brains can reason and remember information, detect new patterns and build on them. An LLM is quite literally incapable of learning a brand new pattern, let alone reason and build on it. Until we have an AI that can accept new information without being tolled what is and isn’t important to remember and how to work with that information, we’re not even a single step closer to AGI. Just because LLMs are impressive, doesn’t mean they posses any cognition. The only way AIs “learn” is by countless people constantly telling it what is and isn’t important or even correct. The second you remove that part, it stops working and turns to shit real quick. More “training” time isn’t going to solve the fact that without human input and human defined limits, it can’t do a single thing. AI cannot learn form it self without human input either, there are countless studies that show how it degrades, and it degrades quickly, like literally just one generation down the line is absolute trash.
EnderMB@lemmy.world 1 month ago
A LLM is basically just an orchestration mechanism. Saying a LLM doesn’t do reasoning is like saying a step function can’t send an email. The step function can’t, but the lambda I’ve attached to it sure as shit can.
ChatGPT isn’t just a model sat somewhere. There are likely hundreds of services working behind the scenes to coerce the LLM into getting the right result. That might be entity resolution, expert mapping, perhaps even techniques that will “reason”.
Source: Working on this right now.
0laura@lemmy.world 1 month ago
they’re very very anti ai and crypto. I understand being against those, but lemmys stop caring about logic when it comes to those topics.
EnderMB@lemmy.world 1 month ago
I think many in the AI space are against the current state of how AI is being pushed, probably just as much as the average tech person.
What is ridiculous is that Lemmy prides itself as both forward-thinking and tech focused, and in reality it is far more close-minded than Reddit or even Twitter. Given how heavily used Mastodon is in tech spheres it makes Lemmy look like an embarrassment to the fediverse…
Womble@lemmy.world 1 month ago
Yeah, there is every reason to be sceptical of the hype around AI, in particular from the big tech companies. But to a significant part of the Lemmy userbase saying “AI” is like saying “witch” in 17th century Salem. To the point where people who are otherwise very much left wing and anti-corporate will take pro-IP/corporate copyright maximalist stances just becuase that would be bad for AI.
aodhsishaj@lemmy.world 1 month ago
You might be interested in Nim then when you get a chance. Talk about orchestration
developer.nvidia.com/nim