I think AI is neat.
I’ve know people in my life who put less mental effort into living their lives than LLMs put into crafting the most convincing lies you’ve ever read
They’re predicting the next word without any concept of right or wrong, there is no intelligence there. And it shows the second they start hallucinating.
…yeah dude. Hence artificial intelligence.
There aren’t any cherries in artificial cherry flavoring either 🤷♀️ and nobody is claiming there is
I have a silly little model I made for creating Vogoon poetry. One of the models is fed from Shakespeare. The system works by predicting the next letter rather than the next word (and whitespace is just another letter as far as it’s concerned). Here’s one from the Shakespeare generation:
KING RICHARD II:
Exetery in thine eyes spoke of aid.
Burkey, good my lord, good morrow now: my mother’s said
This is silly nonsense, of course, and for its purpose, that’s fine. That being said, as far as I can tell, “Exetery” is not an English word. Not even one of those made-up English words that Shakespeare created all the time. It’s certainly not in the training dataset. However, it does sound like it might be something Shakespeare pulled out of his ass and expected his audience to understand through context, and that’s interesting.
Wow, sounds amazing, big probs to you! Are you planning on releasing the model? Would be interested tbh :D
Nothing special about it, really. I only followed this TensorFlow tutorial:
https://www.tensorflow.org/text/tutorials/text_generation
The Shakespeare dataset is on there. I also have another mode that uses entries from the Joyce Kilmer Memorial Bad Poetry Contest, and also some of the works of William Topaz McGonagall (who is basically the Tommy Wiseau of 19th century English poetry). The code is the same between them, however.
Nice, thx
They are a bit like you’d take just the creative writing center of a human brain. So they are like one part of a human mind without sentience or understanding or long term memory. Just the creative part, even though they are mediocre at being creative atm. But it’s shocking because we kind of expected that to be the last part of human minds to be able to be replicated.
Put enough of these “parts” of a human mind together and you might get a proper sentient mind sooner than later.
…or you might not.
It’s fun to think about but we don’t understand the brain enough to extrapolate AIs in their current form to sentience. Even your mention of “parts” of the mind are not clearly defined.
There are so many potential hidden variables. Sometimes I think people need reminding that the brain is the most complex thing in the universe, we don’t full understand it yet and neural networks are just loosely based on the structure of neurons, not an exact replica.
True it’s speculation. But before GPT3 I never imagined AI achieving creativity. No idea how you would do it and I would have said it’s a hard problem or like magic, and poof now it’s a reality. A huge leap in quality driven just by quantity of data and computing. Which was shocking that it’s “so simple” at least in this case.
So that should tell us something. We don’t understand the brain but maybe there isn’t much to understand. The biocomputing hardware is relatively clear how it works and it’s all made out of the same stuff. So it stands to reason that the other parts or function of a brain might also be replicated in similar ways.
Or maybe not. Or we might need a completely different way to organize and train other functions of a mind. Or it might take a much larger increase in speed and memory.
You say maybe there’s not much to understand about the brain but I entirely disagree, it’s the most complex object in the known universe and we haven’t discovered all of it’s secrets yet.
Generating pictures from a vast database of training material is nowhere near comparable.
Ok, again I’m just speculating so I’m not trying to argue. But it’s possible that there are no “mysteries of the brain”, that it’s just irreducible complexity. That it’s just due to the functionality of the synapses and the organization of the number of connections and weights in the brain? Then the brain is like a computer you put a program in. The magic happens with how it’s organized.
And yeah we don’t know how that exactly works for the human brain, but maybe it’s fundamentally unknowable. Maybe there is never going to be a language to describe human consciousness because it’s entirely born out of the complexity of a shit ton of simple things and there is no “rhyme or reason” if you try to understand it. Maybe the closest we get are the models psychology creates.
Then there is fundamentally no difference between painting based on a “vast database of training material” in a human mind and a computer AI. Currently AI generated images is a bit limited in creativity and it’s mediocre but it’s there.
Then it would logically follow that all the other functions of a human brain are similarly “possible” if we train it right and add enough computing power and memory. Without ever knowing the secrets of the human brain. I’d expect the truth somewhere in the middle of those two perspectives.
Another argument in favor of this would be that the human brain evolved through evolution, through random change that was filtered (at least if you do not believe in intelligent design). That means there is no clever organizational structure or something underlying the brain. Just change, test, filter, reproduce. The worst, most complex spaghetti code in the universe. Code written by a moron that can’t be understood. But that means it should also be reproducible by similar means.
Possible, yes. It’s also entirely possible there’s interactions we are yet to discover.
I wouldn’t claim it’s unknowable. Just that there’s little evidence so far to suggest any form of sentience could arise from current machine learning models.
That hypothesis is not verifiable at present as we don’t know the ins and outs of how consciousness arises.
Then it would logically follow that all the other functions of a human brain are similarly “possible” if we train it right and add enough computing power and memory. Without ever knowing the secrets of the human brain. I’d expect the truth somewhere in the middle of those two perspectives.
Lots of things are possible, we use the scientific method to test them not speculative logical arguments.
Functions of the brain
These would need to be defined.
But that means it should also be reproducible by similar means.
Can’t be sure of this… For example, what if quantum interactions are involved in brain activity? How does the grey matter in the brain affect the functioning of neurons? How do the heart/gut affect things? Do cells which aren’t neurons provide any input? Does some aspect of consciousness arise from the very material the brain is made of?
As far as I know all the above are open questions and I’m sure there are many more. But the point is we can’t suggest there is actually rudimentary consciousness in neural networks until we have pinned it down in living things first.
Exactly. Im not saying its not impressive or even not useful, but one should understand the limitation. For example you can’t reason with an llm in a sense that you could convince it of your reasoning. It will only respond how most people in the used dataset would have responded (obiously simplified)
You repeat your point but there already was agreement that this is how ai is now.
I fear you may have glanced over the second part where he states that once we simulated other parts of the brain things start to look different very quickly.
There do seem to be 2 kind of opinions on ai.
-
those that look at ai in the present compared to a present day human. This seems to be the majority of people overall
-
those that look at ai like a statistic, where it was in the past, what improved it and project within reason how it will start to look soon enough. This is the majority of people that work in the ai industry.
For me a present day is simply practice for what is yet to come. Because if we dont nuke ourselves back to the stone age. Something, currently undefinable, is coming.
What i fear is AI being used with malicious intent. Corporations that use it for collecting data for example. Or governments just putting everyone in jail that they are told by an ai
I’d expect governments to use it to craft public relation strategies. An extension of what they do now by hiring the smartest sociopaths on the planet. Not sure if this would work but I think so. Basically you train an AI on previous messaging and results from polls or voting. And then you train it to suggest strategies to maximize for support for X. A kind of dumbification of the masses. Of course it’s only going to get shittier from there on out.
I didn’t, I just focused on how it is today. I think it can become very big and threatening but also helpful, but that’s just pure speculation at this point :)
-
The way I’ve come to understand it is that LLMs are intelligent in the same way your subconscious is intelligent.
It works off of kneejerk “this feels right” logic, that’s why images look like dreams, realistic until you examine further.
We all have a kneejerk responses to situations and questions, but the difference is we filter that through our conscious mind, to apply long-term thinking and our own choices into the mix.
LLMs just keep getting better at the “this feels right” stage, which is why completely novel or niche situations can still trip it up; because it hasn’t developed enough “reflexes” for that problem yet.
LLMs are intelligent in the same way books are intelligent. What makes LLMs really cool is that instead of searching at the book or page granularity, it searches at the word granularity. It’s not thinking, but all the thinking was done for it already by humans who encoded their intelligence into words. It’s still incredibly powerful, at it’s best it could make it so no task ever needs to be performed by a human twice which would have immense efficiency gains for anything information based.
ah, yes, prejudice
They also reason which is really wierd
Ok, but so do most humans? So few people actually have true understanding in topics. They parrot the parroting that they have been told throughout their lives. This only gets worse as you move into more technical topics. Ask someone why it is cold in winter and you will be lucky if they say it is because the days are shorter than in summer. That is the most rudimentary “correct” way to answer that question and it is still an incorrect parroting of something they have been told.
Ask yourself, what do you actually understand? How many topics could you be asked “why?” on repeatedly and actually be able to answer more than 4 or 5 times. I know I have a few. I also know what I am not able to do that with.
I feel that knowing what you don’t know is the key here.
An LLM doesn’t know what it doesn’t know, and that’s where what it spouts can be dangerous.
Of course there’s a lot of actual people that applies to as well. And sadly they’re often in positions of power.
There are more than a couple research agents in development
We need something that can real time fact check without error that would fuck twitter up lol
Few people truly understand what understanding means at all, i got teacher in college that seriously thinked that you should not understand content of lessons but simply remember it to the letter
I am so glad I had one that was the opposite. I discussed practical applications of the subject material after class with him and at the end of the semester he gave me a B+ even though I only got a C by score because I actually grasped the material better than anyone else in the class, even if I was not able to evaluate it as well on the tests.
I’m glad for you) out teacher liked to offer discussion only to shoot us down when we tried to understand something, i was like duh that’s what teachers are for, to help us understand, if teachers don’t do that, then it’s the same as watching YouTube lectures
Jokes on them. I don’t even calculate when I need to parrot. I am beyond such lowly needs.
I don’t think actual parroting is the problem. The problem is they don’t understand a word outside of how it is organized. They can’t be told to do simple logic because they don’t have a simple understanding of each word in their vocabulary. They can only reorganize things to varying degrees.
Some systems clearly do that though or are you just talking about llms?
Just llms
It’s like saying bro, this mouse can’t even type text if I don’t use an on screen keyboard
It doesn’t need to understand the words to perform logic because the logic was already performed by humans who encoded their knowledge into words. It’s not reasoning, but the reasoning was already done by humans. It’s not perfect of course since it’s still based on probability, but the fact that it can pull the correct sequence of words to exhibit logic is incredibly powerful. The main hard part of working with LLMs is that they break randomly, so harnessing their power will be a matter of programming in multiple levels of safe guards.
https://en.m.wikipedia.org/wiki/Chinese_room
I think they’re wrong, as it happens, but that’s the argument.
I guess, I just am looking at from an end user vantage point. I’m not saying the model cant understand the words its using. I just don’t think it currently understands that specific words refer to real life objects and there are laws of physics that apply to those specific objects and how they interact with each other.
Like saying there is a guy that exists and is a historical figure means that information is independently verified by physical objects that exist in the world.
In some ways, you are correct. It is coming though. The psychological/neurological word you are searching for is “conceptualization”. The AI models lack the ability to abstract the text they know into the abstract ideas of the objects, at least in the same way humans do. Technically the ability to say “show me a chair” and it returns images of a chair, then following up with “show me things related to the last thing you showed me” and it shows couches, butts, tables, etc. is a conceptual abstraction of a sort. The issue comes when you ask “why are those things related to the first thing?” It is coming, but it will be a little while before it is able to describe the abstraction it just did, but it is capable of the first stage at least.
This is only one type of intelligence and LLMs are already better at humans at regurgitating facts. But I think people really underestimate how smart the average human is. We are incredible problem solvers, and AI can’t even match us in something as simple as driving a car.
AI can beat me in driving a car, and I have a degree.
Lol @ driving a car being simple. That is one of the more complex sensory somatic tasks that humans do. You have to calculate the rate of all vehicles in front of you, assess for collision probabilities, monitor for non-vehicle obstructions (like people, animals, etc.), adjust the accelerator to maintain your own velocity while terrain changes, be alert to any functional changes in your vehicle and be ready to adapt to them, maintain a running inventory of laws which apply to you at the given time and be sure to follow them. Hell, that is not even an exhaustive list for a sunny day under the best conditions. Driving is fucking complicated. We have all just formed strong and deeply connected pathways in our somatosensory and motor cortexes to automate most of the tasks. You might say it is a very well-trained neural network with hundreds to thousands of hours spent refining and perfecting the responses.
The issue that AI has right now is that we are only running 1 to 3 sub-AIs to optimize and calculate results. Once that number goes up, they will be capable of a lot more. For instance: one AI for finding similarities, one for categorizing them, one for mapping them into a use case hierarchy to determine when certain use cases apply, one to analyze structure, one to apply human kineodynamics to the structure and a final one to analyze for effectiveness of the kineodynamic use cases when done by a human. This would be a structure that could be presented an object and told that humans use it and the AI brain could be able to piece together possible uses for the tool and describe them back to the presenter with instructions on how to do so.
I think AI is the single most powerful tool we’ve ever invented and it is now and will continue completely changing the world. But you’ll get nothing but hate and “iTs Not aCtuaLly AI” replies here on Lemmy.
Umm penicillin? anaesthetic? the Haber process? the transistor? the microscope? steel?
I get it, the models are new and a bit exciting but GPT wont make it so you can survive surgery, or make rocks take the jobs of computers.
Very true and valid. Tho, devils advocate for a moment, AI is great at discovering new ways to survive surgery and other cool stuff. Of course it uses the existing scientific discoveries to do that, but still. It could be the tool to find the next biggest thing on the penicillin, anaesthesia, haber process, transistor, microscope, steel list which is pretty cool.
Is it? This seems like a big citation needed moment.
Have LLMs been used to make big strides? I know some trials are going on aiding doctors in diagnosis and stuff but computer vision algorithms have been doing that for ages (shit contrast dyes, pcr, and blood analysis also do that really) but they come with their own risks and we haven’t seen like widespread unknown illnesses being discovered or anything. Is the tech actually doing anything useful atm or is it all still hype?
We’ve had algorithms help find new drugs and stuff, or plot out synthetic routes for novel compounds; We can run DFT simulations to help determine if we should try make a material. These things have been helpful but not revolutionary, I’m not sure why LLMs would be? I actually worry they’ll hamper scientific progress by aiding fraud (unreproducible results are already a fucking massive problem) or extremely convincingly lying or omitting something if trying to use one to help in a literature review.
Why do you think LLMs will revolutionise science?
He said AI, not LLMs specifically.
This seems like splitting hairs agi doesn’t exist so that can’t be what they mean. AI applies to everything from pathing algorithms for library robots to computer vision and none of those seem to apply.
The context of this post is LLMs and their applications
The comment you replied to first said “AI”, not “LLMs”. And he even told you himself that he didn’t mean LLMs.
I’m not saying he’s right, though, because afaik AI hasn’t made any noteworthy progress made in medical science. (Although a quick skim through Google suggests there has been). I’m just saying that’s clearly not what he said.
I thought they was saying they didn’t mean llms will aid science not that llms wasn’t the topic. Ambiguous in reread.
AI isn’t well defined which is what I was highlighting with mentions of computer vision etc, that falls into AI and it isn’t really meaningfully different from other diagnostic tools. If people mean agi then they should say that, but it hasn’t even been established it’s likely possible let alone that we’re close.
There are already many other intelligences on the planet and not many are very useful outside of niches. Even if we make a general intelligence it’s entirely possible we won’t be able to surpass fish level let alone human for example. and even then it’s not clear that intelligence is the primary barrier in anything, which was what I was trying to point out in my science held back post.
There are so many ifs AGI is a Venus is cloudy -> dinosaurs discussion, you can project anything you like on it but it’s all just fantasy.
why do you think LLMs will revolutionise science
Idk it probably won’t. That wasn’t exactly what I was saying, but I’m also not an expert in any scientific field so that’s my bad for unintentionally contributing to the hype by implying AI is more capable than it currently is or has the potential to be
Fair enough, I used to be scientist (a very bad one that never amounted to anything) and my perspective has been that the major barriers to progress are:
- We’ve just got all the low hangingfruit
- Science education isn’t available to many people, perspectives are quite limited consequently.
- power structures are exploitative and ossified, driving away many people
- industry has too much influence, there isn’t much appetite to fund blue sky projects without obvious short term money earning applications
- patents slow progress
- publish or perish incentivises excessive volumes of publication, fraud, and splitting discoveries into multiple papers which increases burden on researchers to stay current
- nobody wants to pay scientists, bright people end up elsewhere
Reminds me of this meme I saw somewhere around here the other week
I love that this is clearly from a computer science course.
It’s from when we used generic algorithms
Well, college is already dry enough as it is, you gotta appreciate it when your instructor has a sense of humor.
How do you know for sure your brain is not doing exactly the same thing? Hell, being autistic, many social interactions are just me trying to guess what will get me approval without any understanding lol.
Also really fitting that Photon chose this for a placeholder right now:
I once ran an LLM locally using Kobold AI. Said thing has an option to show the alternative tokens for each token it puts out, and what their probably for being chosen was. Seeing this shattered the illusion that these things are really intelligent for me. There’s at least one more thing we need to figure out before we can build an AI that is actually intelligent.
It’s cool what statistics can do, though.
That’s actually pretty neat. I tried Kobold AI a few months ago but the novelty wore off quickly. You made me curious, I’m going to check out that option once I get home. Is it just a toggleable opyiont option or do you have to mess with some hidden settings?
It’s been about a year since I saw the probabilities. I took another look at it just now, and while I can find the toggle in the settings, I can’t find the context menu where the probabilities are shown.
Just as I was about to give up, it somehow worked: https://imgchest.com/p/9p4ne9m9m4n I didn’t really do anything different this time around, so no idea why it didn’t work at first.
Unfortunately the majority of people are idiots who just do this in real life, parroting populous ideology without understanding anything more than the proper catchphrase du jour. And there are many employed professionals who are paid to read a script, or output mundane marketing content, or any “content”. And for that, LLMs are great.
It’s the elevator operator of technology as applied to creative writers. Instead of “hey intern, write the next article about 25 things these idiots need to buy and make sure 90% of them are from our sponsors” it goes to AI. The writer was never going to purchase a few different types of each product category, blindly test them and write a real article. They are just shilling crap they are paid to shill making it look “organic” because many humans are too stupid to not know it’s a giant paid for ad.
The reason it’s dangerous is because there are a significant number of jobs and people out there that do exactly that. Which can be replaced…
This post isn’t true, LLMs do have an understanding of things.
SELF-RAG: Improving the Factual Accuracy of Large Language Models through Self-Reflection
Chess-GPT’s Internal World Model
POKÉLLMON: A Human-Parity Agent for Pokémon Battle with Large Language Models
Whilst everything you linked is great research which demonstrates the vast capabilities of LLMs, none of it demonstrates understanding as most humans know it.
This argument always boils down to one’s definition of the word “understanding”. For me that word implies a degree of consciousness, for others, apparently not.
To quote GPT-4:
LLMs do not truly understand the meaning, context, or implications of the language they generate or process. They are more like sophisticated parrots that mimic human language, rather than intelligent agents that comprehend and communicate with humans. LLMs are impressive and useful tools, but they are not substitutes for human understanding.
When people say that the model “understands”, it means just that, not that it is human, and not that it does so exactly humans do. Judging its capabilities by how close it’s mimicking humans is pointless, just like judging a boat by how well it can do the breast stroke. The value lies in its performance and output, not in imitating human cognition.
Understanding is a human concept so attributing it to an algorithm is strange.
It can be done by taking a very shallow definition of the word but then we’re just entering a debate about semantics.
Understanding is a human concept so attributing it to an algorithm is strange.
Yes sorry probably shouldn’t have used the word “human”. It’s a concept that we apply to living things that experience the world.
Animals certainly understand things but it’s a sliding scale where we use human understanding as the benchmark.
My point stands though, to attribute it to an algorithm is strange.
I’m starting to wonder about you though.
You are moving goal posts
“understanding” can be given only when you reach like old age as a human and if you meditated in a cave
That’s my definition for it
No one is moving goalposts, there is just a deeper meaning behind the word “understanding” than perhaps you recognise.
The concept of understanding is poorly defined which is where the confusion arises, but it is definitely not a direct synonym for pattern matching.
People making content should immediately pivot to become the approvers, not the generators.
The world needs waaaay more editors, too…
I fully back your sentiment OP; you understand as much about the world as any LLM out there and don’t let anyone suggest otherwise.
Signed, a “contrarian”.
I know a few people who would fit that definition
Like almost all politicians?
People learn the same way, we do things that bring us satisfaction and get us approval.
We use words to describe our thoughts and understanding. LLMs order words by following algorithms that predict what the user wants to hear. It doesn’t understand the meaning or implications of the words it’s returning.
It can tell you the definition of an apple, or how many people eat apples, or whatever apple data it was trained on, but it has no thoughts of it’s own about apples.
That’s the point that OOP was making. People confuse ordering words with understanding. It has no understanding about anything. It’s a large language model - it’s not capable of independent thought.
I think that the question of what “understanding” is will become important soon, if not already. Most people don’t really understand as much as you might think we do, an apple for example has properties like flavor, texture, appearance, weight and firmness it also is related to other things like trees and is in categories like food or fruit. A model can store the relationship of apple to other things and the properties of apples, the model could probably be given “personal preferences” like a preferred flavor profile and texture profile and use this to estimate if apples would be preferred by the preferences and give reasonings for it.
Unique thought is hard to define and there is probably a way to have a computer do something similar enough to be indistinguishable, probably not through simple LLMs. Maybe using a LLM as a way to convert internal “ideas” to external words and external words to internal “ideas” to be processed logically probably using massive amounts of reference materials, simulation, computer algebra, music theory, internal hypervisors or some combination of other models.
Knowing that LLMs are just “parroting” is one of the first steps to implementing them in safe, effective ways where they can actually provide value.
I think a better way to view it is that it’s a search engine that works on the word level of granularity. When library indexing systems were invented they allowed us to look up knowledge at the book level. Search engines allowed look ups at the document level. LLMs allow lookups at the word level, meaning all previously transcribed human knowledge can be synthesized into a response. That’s huge, and where it becomes extra huge is that it can also pull on programming knowledge allowing it to meta program and perform complex tasks accurately. You can also hook them up with external APIs so they can do more tasks. What we have is basically a program that can write itself based on the entire corpus of human knowledge, and that will have a tremendous impact.
LLMs definitely provide value its just debatable whether they’re real AI or not. I believe they’re going to be shoved in a round hole regardless.
The next step is to understand much more and not get stuck on the most popular semantic trap
Then you can begin your journey man
There are so, so many llm chains that do way more than parrot. It’s just the last popular catchphrase.
Very tiring to keep explaining that because just shallow research can make you understand more than it’s a parrot comment. We are all parrots. It’s extremely irrelevant to the ai safety and usefulness debates
Most llm implementations use frameworks to just develop different understandings, and it’s shit, but it’s just not true that they only parrot known things they have internal worlds especially when looking at agent networks
I just love this meme-template… 😅
