I think AI is neat.
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”.
Keep seething, OpenAI’s LLMs will never achieve AGI that will replace people
Next you’ll tell me that the enemies that I face in video games arent real AI either!
Buddy, nobody ever said it would
Keep seething
Keep projecting
That was never the goal… You might as well say that a bowling ball will never be effectively used to play golf.
That was never the goal…
Most CEOs seem to not have got the memo…
I agree, but it’s so annoying when you work as IT and your non-IT boss thinks AI is the solution to every problem.
At my previous work I had to explain to my boss at least once a month why we can’t have AI diagnosing patients (at a dental clinic) or reading scans or proposing dental plans… It was maddening.
I find that these LLMs are great tools for a professional. So no, you still need the professional but it is handy if an ai would say, please check these places. A tool, not a replacemenrt.
You’ve just described most people…
P-Zombies, all of them. I happen to be the only one to actually exist. What are the odds, right? But it’s true.
It figures you’d say it, it’s probably your algorithm trying to mess up with my mind!
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.
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
I feel like our current “AIs” are like the Virtual Intelligences in Mass Effect. They can perform some tasks and hold a conversation, but they aren’t actually “aware”. We’re still far off from a true AI like the Geth or EDI.
I wish there was a term without “Intelligence” in it because LLMs aren’t intelligent.
“AI” is always reserved for the latest tech in this space, the previous gens are called what they are. LMMs will be what these are called after a new iteration is out.
I love reading Geth references in the wild.
I wish we called them VI’s. It was a good distinction in their ability.
Though honestly I think our AI is more advanced in conversation than a VI in ME.
This was the first thing that came to my mind as well and VI is such an apt term too. But since we live in the shittiest timeline Electronic Arts would probably have taken the Blizzard/Nintendo route too and patented the term.
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.
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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.
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 :)
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Alternatively we could call things what they are. You know, cause if we ever have actual AI we kind of need the term to be intact and not watered down by years of marketing bullshit or whatever else.
There are specific terms for what you’re talking about already. AI is all the ML algorithms that we are integrating into daily life, and AGI is human-level AI able to create it’s own subjective experience.
For General AI to work, we first need the computer to be able to communicate properly with humans, to understand them and to convey themselves in an understandable way.
LLM is just that. It is the first step towards General AI.
it is already a great tool for programmers. Which means programming anything, including new AI, will only go exponentially faster.
Why is this the first step and not any of the other things that have been around for years?
We have logic reasoning in the form of prolog, bots that are fun to play against in computer games, computers that can win in chess and go against the best players in the world, and computer vision is starting to be useful.
it is already a great tool for programmers. Which means programming anything, including new AI, will only go exponentially faster.
Yes to it being a tool. But right now all it can really do is bog standard stuff. Also have you read about that the use of Github Copilot seems to reduce the quality of code? This means we cannot yet rely on this type of technology. Again, it’s a limited tool and that is it. At least for now.
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.
I always argue that human learning does exactly the same. You just parrot and after some time you believe it’s your knowledge. Inventing new things is applying seen before mechanisms on different dataset.
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.
They also reason which is really wierd
ah, yes, prejudice
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 talking point
If an LLM is just regurgitating information in a learned pattern and therefore it isn’t real intelligence, I have really bad news for ~80% of people.
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.
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.
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.
AI can beat me in driving a car, and I have a degree.
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.
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.
Some systems clearly do that though or are you just talking about llms?
Just llms
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.
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 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
So… LLMS are… teenagers?
