I think AI is neat.
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
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.
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.
LLMs are a step towards AI in the same sense that a big ladder is a step towards the moon.
I just love this meme-template… 😅
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…
People making content should immediately pivot to become the approvers, not the generators.
The world needs waaaay more editors, too…
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.
I know a few people who would fit that definition
Like almost all politicians?
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”.
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.
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.
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.
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those that look at ai in the present compared to a present day human. This seems to be the majority of people overall
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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.
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 :)
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.
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…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.
…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
Been destroyed for this opinion here. Not many practicioners here just laymen and mostly techbros in this field… But maybe I haven’t found the right node?
I’m into local diffusion models and open source llms only, not into the megacorp stuff
Have you ever considered you might be, you know, wrong?
No sorry you’re definitely 100% correct. You hold a well-reasoned, evidenced scientific opinion, you just haven’t found the right node yet.
Perhaps a mental gymnastics node would suit sir better? One without all us laymen and tech bros clogging up the place.
Or you could create your own instance populated by AIs where you can debate them about the origins of consciousness until androids dream of electric sheep?
Do you even understand my viewpoint?
Why only personal attacks and nothing else?
You obviously have hate issues, which is exactly why I have a problem with techbros explaining why llms suck.
They haven’t researched them or understood how they work.
It’s a fucking incredibly fast developing new science.
Nobody understands how it works.
It’s so silly to pretend to know how bad it works when people working with them daily discover new ways the technology surprises us. Idiotic to be pessimistic about such a field.
You obviously have hate issues
Says the person who starts chucking out insults the second they get downvoted.
From what I gather, anyone that disagrees with you is a tech bro with issues, which is quite pathetic to the point that it barely warrants a response but here goes…
I think I understand your viewpoint. You like playing around with AI models and have bought into the hype so much that you’ve completely failed to consider their limitations.
People do understand how they work; it’s clever mathematics. The tech is amazing and will no doubt bring numerous positive applications for humanity, but there’s no need to go around making outlandish claims like they understand or reason in the same way living beings do.
You consider intelligence to be nothing more than parroting which is, quite frankly, dangerous thinking and says a lot about your reductionist worldview.
You may redefine the word “understanding” and attribute it to an algorithm if you wish, but myself and others are allowed to disagree. No rigorous evidence currently exists that we can replicate any aspect of consciousness using a neural network alone.
You say pessimistic, I say realistic.
Haha it’s pure nonsense. Just do a little digging instead of doing the exact guesstimation I am talking about. You obviously don’t understand the field
Once again not offering any sort of valid retort, just claiming anyone that disagrees with you doesn’t understand the field.
I suggest you take a cursory look at how to argue in good faith, learn some maths and maybe look into how neural networks are developed. Then study some neuroscience and how much we comprehend the brain and maybe then we can resume the discussion.
You attack my viewpoint, but misunderstood it. I corrected you. Now you tell me I am wrong with my viewpoint (I am not btw) and start going down the idiotic path of bad faith conversation, while strawman arguing your own bad faith accusation, only because you are butthurt that you didn’t understand. Childish approach.
You don’t understand, because no expert currently understands these things completely. It’s pure nonsense defecation coming out of your mouth
You don’t really have one lol. You’ve read too many pop-sci articles from AI proponents and haven’t understood any of the underlying tech.
All your retorts boil down to copying my arguments because you seem to be incapable of original thought. Therefore it’s not surprising you believe neural networks are approaching sentience and consider imitation to be the same as intelligence.
You seem to think there’s something mystical about neural networks but there is not, just layers of complexity that are difficult for humans to unpick.
You argue like a religious zealot or Trump supporter because at this point it seems you don’t understand basic logic or how the scientific method works.
All the stuff on the dbzero instance is pro open source and pro piracy so fairly anti corpo and not tech illiterate
Thanks, I’ll join in
If anything people really need to start experimenting beyond talking to it like its human or in a few years we will end up with a huge ai-illiterate population.
I’ve had someone fight me stubbornly talking about local llms as “a overhyped downloadable chatbot app” and saying the people on fossai are just a bunch of ai worshipping fools.
I was like tell me you now absolutely nothing you are talking about by pretending to know everything.
But the thing is it’s really fun and exciting to work with, the open source community is extremely nice and helpful, one of the most non toxic fields I have dabbled in! It’s very fun to test parameters tools and write code chains to try different stuff and it’s come a long way, it’s rewarding too because you get really fun responses
Aren’t the open source LLMs still censored though? I read someone make an off-hand comment that one of the big ones (OLLAMA or something?) was censored past version 1 so you couldn’t ask it to tell you how to make meth?
I don’t wanna make meth but if OSS LLMs are being censored already it makes having a local one pretty fucking pointless, no? You may as well just use ChatGPT. Pray tell me your thoughts?
Depends who and how the model was made. Llama is a meta product and its genuinely really powerful (i wonder where zuckerberg gets all the data for it)
Because its powerful you see many people use it as a starting point to develop their own ai ideas and systems. But its not the only decent open source model and the innovation that work for one model often work for all others so it doesn’t matter in the end.
Every single model used now will be completely outdated and forgotten in a year or 2. Even gpt4 en geminni
Holy crap didnt expect him to admit it this soon:
Zuck Brags About How Much of Your Facebook, Instagram Posts Will Power His AI
Could be legal issues, if an llm tells you how to make meth but gets a step or two wrong and results in your death, might be a case for the family to sue.
But i also don’t know what all you mean when you say censorship.
But i also don’t know what all you mean when you say censorship.
It was literally just that. The commentor I saw said something like "it’s censored after ver 1 so don’t expect it to tell you how to cook meth.
But when I hear the word “censored” I think of all the stuff ChatGPT refuses to talk about. It won’t write jokes about protected groups and VAST swathes of stuff around it. Like asking it to define “fag-got” can make it cough and refuse even though it’s a British food-stuff.
Blocking anything sexual - so no romantic/erotica novel writing.
The latest complaint about ChatGPT is it’s laziness which I can’t help feeling is due to over-zealous censorship. Censorship doesn’t just block the specific things but entirely innocent things (see fag-got above).
Want help writing a book about Hilter beoing seduced by a Jewish woman and BDSM scenes? No chance. No talking about Hitler, sex, Jewish people or BDSM. That’s censorship.
I’m using these as examples - I’ve no real interest in these but I am affected by annoyances and having to reword requests because they’ve been mis-interpreted as touching on censored subjects.
Just take a look at r/ChatGPT and you’ll see endless posts by people complaining they triggered it’s censorship over asinine prompts.
Oh ok, then yea that’s a problem, any censorship that’s not directly related to liability issues should be nipped in the bud.
No there are many uncensored ones as well
Is that Summer from Rick and Morty?
AI: “Keep Summer safe”
So super informed OP, tell me how they work. technically, not CEO press release speak. explain the theory.
I’m not OP, and frankly I don’t really disagree with the characterization of ChatGPT as “fancy autocomplete”. But…
I’m still in the process of reading this cover-to-cover, but Chapter 12.2 of Deep Learning: Foundations and Concepts by Bishop and Bishop explains how natural language transformers work, and then has a short section about LLMs. All of this is in the context of a detailed explanation of the fundamentals of deep learning. The book cites the original papers from which it is derived, most of which are on ArXiv. There’s a nice copy on Library Genesis. It requires some multi-variable probability and statistics, and an assload of linear algebra, reviews of which are included.
So obviously when the CEO explains their product they’re going to say anything to make the public accept it. Therefore, their word should not be trusted. However, I think that when AI researchers talk simply about their work, they’re trying to shield people from the mathematical details. Fact of the matter is that behind even a basic AI is a shitload of complicated math.
At least from personal experience, people tend to get really aggressive when I try to explain math concepts to them. So they’re probably assuming based on their experience that you would be better served by some clumsy heuristic explanation.
IMO it is super important for tech-inclined people interested in making the world a better place to learn the fundamentals and limitations of machine learning (what we typically call “AI”) and bring their benefits to the common people. Clearly, these technologies are a boon for the wealthy and powerful, and like always, have been used to fuck over everyone else.
IMO, as it is, AI as a technology has inherent patterns that induce centralization of power, particularly with respect to the requirement of massive datasets, particularly for LLMs, and the requirement to understand mathematical fundamentals that only the wealthy can afford to go to school long enough to learn. However, I still think that we can leverage AI technologies for the common good, particularly by developing open-source alternatives, encouraging the use of open and ethically sourced datasets, and distributing the computing load so that people who can’t afford a fancy TPU can still use AI somehow.
I wrote all this because I think that people dismiss AI because it is “needlessly” complex and therefore bullshit. In my view, it is necessarily complex because of the transformative potential it has. If and only if you can spare the time, then I encourage you to learn about machine learning, particularly deep learning and LLMs.
That’s my point. OP doesn’t know the maths, has probably never implemented any sort of ML, and is smugly confident that people pointing out the flaws in a system generating one token at a time are just parroting some line.
These tools are excellent at manipulating text (factoring in the biases they have, I wouldn’t recommended trying to use one in a multinational corporation in internal communications for example, as they’ll clobber non euro derived culture) where the user controls both input and output.
Help me summarise my report, draft an abstract for my paper, remove jargon from my email, rewrite my email in the form of a numbered question list, analyse my tone here, write 5 similar versions of this action scene I drafted to help me refine it. All excellent.
Teach me something I don’t know (e.g. summarise article, answer question etc?) disaster!
They can summarize articles fairly well
No, they can summarise articles very convincingly! Big difference.
They have no model of what’s important, or truth. Most of the time they probably do ok but unless you go read the article you’ll never know if they left out something critical, hallucinated details, or inverted the truth or falsity of something.
That’s the problem, they’re not an intern they don’t have a human mind. They recognise patterns in articles and patterns in summaries, they non deterministically adjust the patterns in the article towards the patterns in summaries of articles. Do you see the problem? They produce stuff that looks very much like an article summary but do not summarise, there is no intent, no guarantee of truth, in fact no concern for truth at all except what incidentally falls out of the statistical probability wells.
That’s a good way of explaining it. I suppose you’re using a stricter definition of summary than I was.
I think it’s really important to keep in mind the separation between doing a task and producing something which looks like the output of a task when talking about these things. The reason being that their output is tremendously convincing regardless of its accuracy, and given that writing text is something we only see human minds do it’s so easy to ascribe intent behind the emission of the model that we have no reason to believe is there.
Amazingly it turns out that often merely producing something which looks like the output of a task apparently accidentally accomplishes the task on the way. I have no idea why merely predicting the next plausible word can mean that the model emits something similar to what I would write down if I tried to summarise an article! That’s fascinating! but because it isn’t actually setting out to do that there’s no guarantee it did that and if I don’t check the output will be indistinguishable to me because that’s what the models are built to do above all else.
So I think that’s why we to keep them in closed loops with person -> model -> person, and explaining why and intuiting if a particularly application is potentially dangerous or not is hard if we don’t maintain a clear separation between the different processes driving human vs llm text output.
You are so extremely outdated in your understanding, For one that attacks others for not implementing their own llm
They are so far beyond the point you are discussing atm. Look at autogen and memgpt approaches, the way agent networks can solve and develop way beyond that point we were years ago.
It really does not matter if you implement your own llm
Then stay out of the loop for half a year
It turned out that it’s quite useless to debate the parrot catchphrase, because all intelligence is parroting
It’s just not useful to pretend they only “guess” what a summary of an article is
They don’t. It’s not how they work and you should know that if you made one
Yeah this is basically how I think of them too. It’s fascinating that it works.
Fact of the matter is that behind even a basic AI is a shitload of complicated math.
Depending on how simple something can be to be considered an AI, the math is surprisingly simple compared to what an average person might expect. The theory behind it took a good amount of effort to develop, but to make something like a basic image categorizer (eg. optical character recognition) you really just need some matrix multiplication and calculating derivatives-- non-math-major college math type stuff.
Come on… It’s not impressive to just not be aware of where the bar is for most people. No, it’s not complex math but you are debating people that read headlines only and then go fully into imagination of what it says
you really just need some matrix multiplication and calculating derivatives-- non-math-major college math type stuff.
Well sure you don’t need a math degree for that, but most people really need to put some time into those topics. I.e., that kind of math is complex enough to constitute a barrier to entry into the field, particularly people with no free time to self-study or money for school.
Said differently: matrix math and basic calculus is hard, just not for you and I.
Point taken
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.
They also reason which is really wierd
ah, yes, prejudice
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.
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.
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.
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
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.
