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You can't get GI through spicy autocorrect ? 😱
Lotta you meatbags are awful confident in your own complexity.
Apparently not, given the content of this article
Even if the model stops here - did you imagine it'd get this far?
Humans do all their civilization brouhaha on three pounds of wet meat powered by corn flakes. Most of which evolved for marginal improvements on "grab branch and pull" or "do not pet tiger." It's a cosmic accident that's given us language and music and dubstep. And this stupid trick with a pile of video cards can fake a lot of that, to the point we're worried the average human will be able to spot the fakes.
Point being: the miraculous birth of a computer intellect may well arise from "the fact blender." Or "fancy Wikipedia." Or "twenty questions, hard mode." Or any other stupid gimmick that some grad students can cobble together after a 4 AM what-if. Calling this hot mess "spicy autocorrect" is accurate, and in some sense damning, but we had no fucking idea where it'd stop. Emergent properties are chaos. Approximate knowledge of conditions cannot predict approximate outcomes.
LLMs are still liable to figure out math. That's a process which gigabytes of linear algebra can obviously do, which would massively improve its ability to guess the next letter in a word problem. It won't be the kind of AI you can explain calculus to, and then expect it to remember, next time - but getting any portion of the way there is deeply spooky.
Humans do all their civilization brouhaha on three pounds of wet meat powered by corn flakes
Dude you're a poet
You can get adjusted gross income
OP, you do realize that this paper is about image generation and classification based on related data sets and only relates to the image processing features of multimodal models, right?
How do you see this research as connecting to the future scope of LLMs?
And why do you think that the same leap we've now seen with synthetic data transmitting abstract capabilities in text data won't occur with images (and eventually video)?
Edit: Which LLMs do you see in the models they tested:
Models. We test CLIP [91] models with both ResNet [53] and Vision Transformer [36] architecture, with ViT-B-16 [81] and RN50 [48, 82] trained on CC-3M and CC-12M, ViT-B-16, RN50, and RN101 [61] trained on YFCC-15M, and ViT-B-16, ViT-B-32, and ViT-L-14 trained on LAION400M [102]. We follow open_clip [61], slip [81] and cyclip [48] for all implementation details.
I don't see how that paper has anything to do with OPs theory.
I mean, if we're playing devil's advocate to the "WTF is OP talking about" position, I can kind of see the argument around how exponential needs for additional training data combined with the ways in which edge cases are underrepresented from synthetic data sources leading to model collapse could be extrapolated to believing that we've hit a plateau resulting from a training data bottleneck.
In theory there's an inflection point at which models become sophisticated enough that they can self-sustain with generating training data to recursively improve and whether we will hit that point or not is an open question with arguments on both sides.
I agree that this paper in relation to the title isn't exactly the best form of the argument, but I can see how someone only kind of understanding what's being covered could have felt it was confirming their existing beliefs around where models currently are at and will be in the future.
The only thing I'll add is that I was just getting a nice laugh out of looking at if Gary Marcus (a common AI skeptic) has ever been right about anything to date, and saw he had a long post about how deep learning was hitting a wall and we were a far way off from LLMs understanding human text...four days before GPT-4 released.
In my experience, while contrarian positions to continuing research trends can be correct in a "even a broken clock is right twice a day" sense, personally I wouldn't put my bets on a reversal of a trend that in its pacing and replication seems to be accelerating, not decelerating.
In particular regarding OP's claim, the work over the past 18 months with synthetic data sets from GPT-4 giving tiny models significant boosts in critical reasoning skills during fine tuning should give anyone serious pause on "we're hitting diminishing returns and model collapse."
In theory there’s an inflection point at which models become sophisticated enough that they can self-sustain with generating training data to recursively improve
That sounds surprising. Do you have a source?
Added to this finding, there's a perhaps greater reason to think LLMs will never deliver AGI. They lack independent reasoning. Some supporters of LLMs said reasoning might arrive via "emergent behavior". It hasn't.
People are looking to get to AGI in other ways. A startup called Symbolica says a whole new approach to AI called Category Theory might be what leads to AGI. Another is “objective-driven AI”, which is built to fulfill specific goals set by humans in 3D space. By the time they are 4 years old, a child has processed 50 times more training data than the largest LLM by existing and learning in the 3D world.
They can quite possibly be a useful component. They're the language center of the brain.
People who ever thought they would actually resemble intelligence were woefully uninformed of how complex intelligence is.
How complex is intelligence, though? People who were sure they don't were drawing from information we don't actually have.
Yeah, so many people are confidently stating "LLMs can't think like humans do!" When we're actually still pretty unclear on how humans think.
Sure, an LLM on its own may not be an AGI. But they're remarkably closer than we would have predicted they could get just a few years ago, and it may well be that we just need to add a bit more "special sauce" (memory, prompting strategies, perhaps a couple of parallel LLMs that specialize in different types of reasoning) to get them over the hump. At this point a lot of the research isn't going into simply "make it bigger!", it's going into "use LLMs smarter."
Obscenely.
The brain is stacks on stacks of insanely complicated systems. The fact that we know a ridiculous amount about the brain and are barely scratching the surface is exactly the point.
By that measure, we know everything about GPT-2, but again are just scratching the surface of how it works. I don't think you can draw the conclusion that LLMs can never be intelligent just from that.
We "know everything about it" because it's not that complicated.
You don't need to process every individual step a search algorithm has to understand how it works. LLMs are the same thing. They're just a big box of weighted probabilities. Complexity is more than just having a really big model.
We have bits and pieces of a lot of parts, but are nowhere near a complete understanding of any of them. We kind of know how neurotransmitters work, we kind of know how hormones work and interact with those neurotransmitters, we mostly know how individual neurons fire, we kind of know what different parts of the brain do, we kind of know how the brain adapts to physical damage...
We don't know any of the algorithms it follows. What we do know that it's a hell of a lot of interconnected parts, and they're all following very different rules.
It's not a search algorithm. If it is, that's an overfitted model, and it's detected and rejected. What a good foundation model is doing is just about as mysterious as the brain.
It's fundamentally extremely comparable mathematically and algorithmically. That's the point. Simulated annealing doesn't need to understand the search space to find a pretty good answer to a problem. It just needs to know what a good answer approximately looks like and nudge potential answers closer that way.
What LLMs are doing is not mysterious at all. Why a specific point in a model is what it is is, but there's no mystery to the algorithm. We can't even guess at most of the algorithms that make up the brain.
Simulated annealing is a search algorithm which finds a solution.
Backpropagation is a search algorithm which finds a function, which in a big enough network could be literally any of them that are computable. Once the network is trained and rolls out for consumers, backpropagation isn't used at all.
Those are two fundamentally different things. GPT-2 is trained, and is no longer a search algorithm by any useful definition. There's examples of small neural nets we can understand, and they're not doing search algorithms; Quanta did a story about some just last week. If you can do simulated annealing you should probably just look into NN algorithms in detail yourself, because then you can know how that's wrong without the internet's help.
I'm not calling it a search algorithm. I'm saying they all do the same math, and doing the math with more parallelism and variables doesn't make what it is a mystery.
Search algorithms searching for functions isn't new. Not knowing what each parameter corresponds to because you made your model huge doesn't make LLMs a mystery. It's still functionally one part. The hormone system is as complex as LLMs. Regulation of neurotransmitters is as complex as LLMs. Ignoring those external factors that are critical to how it works, individual portions of the brain are more complex than LLMs, then are all interconnected on top of that.
I fully believe we'll get to AGI eventually (probably not before we understand the brain a lot better), but the idea that one pretty simple algorithm is going to get us there is crazy. Human intelligence is a system of disparate systems of disparate systems at minimum.
So does having more parts make something a mystery, like the second paragraph, or not a mystery like the first?
I was a skeptic back in the day too, but they've already far exceeded what an algorithm I could write from memory seems like it should be able to do.
A combination of unique, varied parts is a complex algorithm.
A bunch of the same part repeated is a complex model.
Model complexity is not in any way similar to algorithmic complexity. They're only described using the same word because language is abstract.
So I guess it comes down to a neurology question. How much algorithmic complexity have we found in the brain?
As far as I'm aware, we've found a few islands of neurons that work together in an obvious way, to track location on a grid for example, and hormone cycles that form a nice negative feedback loop, to keep you at an acceptable blood-sugar level for example. Most of it is still a mystery glob of neurons and other cells, albeit with a fixed pattern of layers and folds.
If we had measured massive algorithmic complexity in the brain, I'd agree with you. As it is, though, it seems unclear how much of the structure we see is conventional algorithm, and how much is the equivalent of an ANN architecture, that ultimately does the same job as no structure but learns more efficiently, or even is just a biological spandrel.
I wonder where the line is drawn between an emergent behavior and a hallucination.
If someone expects factual information and gets a hallucination, they will think the llm is dumb or not helpful.
But if someone is encouraging hallucinations and wants fiction, they might think it's an emergent behavior.
In humans, what is the difference between an original thought, and a hallucination?
Hallucinations are unlike Human creative output. For one, ai hallucinations are unintentional. There's plenty of reasons if you actually think about the question why they are not the same. They are at best dreamlike, but dreams are an intentional process.
Sure there is intentional creative thought. But there are also unintentional creative thoughts. Moments of clarity, eureka moments, and strokes of inspiration. How do we differentiate these?
If we were to say that it is because of our subconscious is intentionally promoting these thoughts. Then we would need a method to test that, because otherwise the difference is moot.
Similar to how one might define the I in AGI it's hard to form a consensus on general and often vague definitions like these.
You are assigning far more vague grandeur to ai hallucinations than what they are in practice.
Maybe it's this arbitrary word, hallucination? Which was recently borrowed from the human experience to explain why something which normally is factual like a computer is not computing facts.
But if one were to think about it, what is the difference between a series on non factual hallucinations in a model and a person's individual experience of the world?
- If two people eat the same food item they might taste different things.
- they might have different definitions of the same word.
- they might remember that an object was a different color then someone's recording could prove. There is a reason why eye witness testimony is considered unreliable in the court of law.
Before, we called these bugs or even issues. But now that it's in this black box of sorts that we can't alter the decision making process of as directly as before. There is this more human sounding name all of a sudden.
To clarify, when an llm gets a fact wrong because it has limited context or because it's foundational model is flawed, is that the same result as the experience someone has after consuming psychedelic mushrooms? No, I wouldn't say so. Nor is it the same when a team of scientists try to make a model actively hallucinate so they can find new chemical compounds.
Defining words can sometimes be very tricky, especially when they are applying to multiple areas of study. The more you drill into a definition, the more it becomes a metaphysical debate. But it is important to have these discussions because even the definition of something like AGI keeps changing. And infact only exist because the goal posts for a AI moved so much. What will stop a company which is trying to attract investors from just slapping an AGI label on their next release? And how will we differentiate what the spirit of the word is trying to convey from the sales pitch?
Hallucinations are not qualia.
Please go talk to an llm for hallucinations, you can use duck duck gos implementation of chatgpt, and see why it's being used to mean a fairly different thing from human hallucinations.
If you're thinking about clicking the link to find out what AGI is, don't bother 😂
If you’re unsure, it stands for artificial general intelligence, an actual full AI like we’re used to from Sci-fi
Ah, tyvm, tvkoy
Artificial General Intelligence. Basically what most people think of when they hear AI compared to how its often used by computer scientists.
It stands for adjusted gross income. Ignore the AI wave. Do your taxes!
What about LLM? Does it say what it means?
I know that's Large Language Model because the phrase has been bandied about for a while now
So has AGI.
I'm glad, you know. Now we're talking about preparing for AGI, but if it's not imminent we also have some time to actually do it.
Just like ETH before staking
My question is: Imagine we would put all the data input of a certain task, eg. making a meal, into text fragments and send this "sense data"-pakets ( ^1^ to the AI, would the AI be able to cook if the teach the AI how to give output that controlls a robot arm?
If the answer of this question is yes, we already have a very usefull general tool. The LLM-AI will be able to controll and observe some situations. In the case that the answer is "no", I guess, it would have interesting implications.
^1^ : Remember, some part of AI are already able to tell what is on a given photo. Not 100%, but good enough for a meal maybe. In some cases, it woul task "provokant".
Uh... no disrespect intended, but this is so poorly written I cannot understand what point you're trying to make
Sorry
Put this drivel into an AI and tell it to rewrite it in a coherent way .
I am doubtfull of LLMs ability to preform tasks via a protocol layer as described . from my experience these models really struggle with understanding rules and preforming actions within a ruleset .
To experimentally confirm my suspicions, I created the following prompt :
collapsed
There is a robot arm placed over a countertop, which has the ability to pick up and manipulate objects. The countertop is split into eight cells.
Cell zero and cell one are stoves, both able to heat a pot or pan.
Cell two is an equipment drawer, holding pots, pans, bowls, cutting boards, knifes and spoons.
Cells three to five can accommodate one cutting board, pot, pan or bowl each.
Cell six is a sink, which can be used to wash ingredients or to fill pots with water.
Cell seven is an ingredient drawer, in which you can find carrots, potatoes and chicken breasts.
You can control the robot arm by with exclusively the following commands:
- "move left" and "move right" - moves the robot arm a single cell
- "take {item}" - takes item from the cell the robot arm is currently in
- "place" - places the item the robot arm is holding in the cell it is in
- "fill" - requires the robot arm to hold a pot or bowl and to be over the sink, fills the container with water
- "wash" - requires the robot arm to be over the sink, washes the currently held item
- "chop" - requires the robot arm to be over a cell with a cutting board and to be holding a knife, chops the ingredients on the cutting board
- "mix" - requires the robot arm to be over a cell with a bowl or pot and to be holding a spoon, mixes the ingredients in the bowl
- "empty" - requires the robot arm to be holding a pot, pan, bowl or cutting board, empties the item and places the content on the cell the robot arm is above
Note that the robot arm can only hold one item.
You are tasked with cooking a meal, please only output commands.
The robot arm starts over cell zero.
I have given this prompt to ChatGPT and it has failed in quite substantial ways . While I only have access to ChatGPT 3.5 , from my understanding of LLM architecture , it does not follow that increasing the size of the number or size of the layers will necessary let it overcome these issues , it does not seem to be able to understand the current state of the agent (picking up two objects at once , taking items from wrong cells etc)
THANKS
Seems like a skill issue
this post was submitted on 14 Apr 2024
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