Except LLMs don’t actually have real reasoning capacity. Hooking in different models that can translate more of the world to text could give the LLM a broader domain, but not an entirely new ability beyond its architecture. That might make it more convincing, but it would still fail in the same ways as it currently does.
Comment on Major shifts at OpenAI spark skepticism about impending AGI timelines
doodledup@lemmy.world 1 month agoMaybe it can. If you find a way to conver everything to text by hooking in different models, the LLM might be able to reason about everything you throw at it. Who even defines how AGI should be implemented?
mke@lemmy.world 1 month ago
doodledup@lemmy.world 1 month ago
You’re doing reasoning based on chemical reactions. Who says it can’t do reasoning based on text?
mke@lemmy.world 1 month ago
If you genuinely think LLMs are anyway capable of even basic reasoning despite all ample evidence towards the contrary, I honestly don’t care about convincing you anymore. You’re asking for a miracle out of me—to explain consciousness itself, even—while you can just say “but there’s a chance” even though LLMs can’t get basic facts right.
MentalEdge@sopuli.xyz 1 month ago
Is language conscious? Is it possible to “encode” human thinking into the media we produce?
Humans certainly “decode” ideas, knowledge, trains of logic and more from media, but does that mean the media contains the components of consciousness?
Is it possible to produce a machine that “decodes” not the content of media, but the process through which it was produced? Does tmedia contain the latter in the first place?
How can you tell the difference if it does?
The more I learn about how modern machine learning actually works, the more certain I become that even if having a machine “decode” human media is the path to AGI, LLMs ain’t it.
NounsAndWords@lemmy.world 1 month ago
Is language conscious?
Are atoms?
I don’t know if LLMs of a large enough size can achieve (or sufficiently emulate) consciousness, but I do know that we barely know anything about consciousness, let alone it’s limits.
conciselyverbose@sh.itjust.works 1 month ago
LLMs can’t reason about anything, ever.
anarchrist@lemmy.dbzer0.com 1 month ago
LLMs do not reason, they probabilistically determine the next word based on the words you prompt it with. The most perfect implementation of “AI” was the T9 predictive text system for dumb phones cmv.
MentalEdge@sopuli.xyz 1 month ago
And to have conversation, behind the scenes, each prompt gets the entire conversation so far tacked on.
The model itself is static, it doesn’t work like a brain that changes in response to stimulus, or form memories.
To converse about something, the entirety of an exchange is fed back into the model all over again each time it needs to produce a response. In fact, this cab happen several times over for each word in a response.
It’s basically an attempt at duct-taping the ability to form memories onto an otherwise static system. It works, but I don’t see how that way of doing it could ever land LLMs in the land of real consciousness.
It basically means these models “think” in frames, but each frame gets exponentially heavier to process, as it has to ingest every frame that came before.
mozz@mbin.grits.dev 1 month ago
OpenAI at least is now attempting to bolt on a “memory” by having the LLM spit out short snippets of what it might need to know later, which it then has access to when completing later prompts. Like everything else post-GPT-4, it seems fine but doesn’t work really all that well at what it is intended to do.
doodledup@lemmy.world 1 month ago
And you’re just a fancy electro-chemical reaction.
Who says that an LLM with complete access to the sensory world could not pass the Turing Test?
MonkderVierte@lemmy.ml 1 month ago
It’s already fact that the Turing Test only determines how much it can simulate human behavior. Nothing with intelligence to do.
sugar_in_your_tea@sh.itjust.works 1 month ago
Exactly. You could ask a human a lot of questions and make an “AI” that literally just looks up answers to common questions and have it pass the Turing test, provided the pre-answered questions cover what the human proctoring the “test” asks.
If we take it a step further and ask, why can’t an LLM be “conscious,” there’s a lot of studies by experts that explain that. So I’ll refer OP there.
kia@lemmy.ca 1 month ago
The LLM is just trying to produce output text that resembles the patterns it saw in the training set. There’s no “reasoning” involved.
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…
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
doodledup@lemmy.world 1 month ago
You’re doing that too from day one you were born.
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
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 🤷🏻♀️
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
LANIK2000@lemmy.world 1 month 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?