> Compared to humans, LLMs have effectively unbounded training data. They are trained on billions of text examples covering countless topics, styles, and domains. Their exposure is far broader and more uniform than any human's, and not filtered through lived experience or survival needs.
I think it's the other way round: humans have effectively unbounded training data. We can count exactly how much text any given model saw during training. We know exactly how many images or video frames were used to train it, and so on. Can we count the amount of input humans receive?
I can look at my coffee mug from any angle I want, I can feel it in my hands, I can sniff it, lick it and fiddle with it as much as I want. What happens if I move it away from me? Can I turn it this way, can I lift it up? What does it feel like to drink from this cup? What does it feel like when someone else drinks from my cup? The LLM has no idea because it doesn't have access to sensory data and it can't manipulate real-life objects (yet).
A big challenge is that the LLM cannot selectively sample it's training set. You don't forget what a coffee cup looks like just because you only drank water for a week. LLMs on the other hand will catastrophically forget anything in their training set when the training set does not have a uniform distribution of samples in each batch.
This is a fair criticism we should've addressed. There's actually a nice study on this: Vong et al. (https://www.science.org/doi/10.1126/science.adi1374) hooked up a camera to a baby's head so it would get all the input data a baby gets. A model trained on this data learned some things babies do (eg word-object mappings), but not everything. However, this model couldn't actively manipulate the world in the way that a baby does and I think this is a big reason why humans can learn so quickly and efficiently.
That said, LLMs are still trained on significantly more data pretty much no matter how you look at it. E.g. a blind child might hear 10-15 million words by age 6 vs. trillions for LLMs.
While an LLM is trained on trillions of tokens to acquire its capabilities, it does not actively retain or recall the vast majority of it, and often enough is not able to make deductive reasoning either (e.g. X owns Y does not necessarily translate to Y belongs to X).
The acquired knowledge is a lot less uniform than you’re proposing and in fact is full of gaps a human would never make. And more critically, it is not able to peer into all of its vast knowledge at once, so with every prompt what you get is closer to an “instance of a human” than “all of humanity” as you might think of LLMs.
(I train and dissect LLMs for a living and for fun)
I think you are proposing something that's orthogonal to the OP's point.
They mentioned the training data is much higher for an LLM, LLM's recall not being uniform was never in question.
No one expects compression to be without loss when you scale below knowledge entropy that exists in your training set.
I am not saying LLMs do simple compression but just pointing a mathematical certainity.
(And I think you don't need to be an expert in creating LLMs to understand them, albeit I think a lot of people here have experience with it aswell so I find the additional emphasis on it moot).
The way I understood OP’s point is that because LLMs have been trained on the entirety of humanity’s knowledge (exemplified by the internet), then surely they know as much as the entirety of humanity. A cursory use of an LLM shows this is obviously not true, but I am also raising the point that LLMs are only summoning a limited subset of that knowledge at a time when answering any given prompt, bringing them closer to a human polymath than an omniscient entity, and larger LLMs only seem to improve on the “depth” of that polymath knowledge rather than the breadth of it.
Again just my impression from exposure to many LLMs at various states of training (my last sentence was not an appeal to expertise)
I don’t think the amount of data is essential here. The human genome is only around 750 MB, much less than current LLMs, and likely only a small fraction of it determines human intelligence. On the other hand, current LLMs contain immense amounts of factual knowledge that a human newborn carries zero information about.
Intelligence likely doesn’t require that much data, and it may be more a question of evolutionary chance. After all, human intelligence is largely (if not exclusively) the result of natural selection from random mutations, with a generation count that’s likely smaller than the number of training iterations of LLMs. We haven’t found a way yet to artificially develop a digital equivalent effectively, and the way we are training neural networks might actually be a dead end here.
Humans ship with all the priors evolution has managed to cram into them. LLMs have to rediscover all of it from scratch just by looking at an awful lot of data.
Sure, but LLMs are trying to build the algorithms of the human mind backwards, converge on similar functionality based on just some of the inputs and outputs. This isn't an efficient or a lossless process.
The fact that they can pull it off to this extent was a very surprising finding.
There's only so much information content you can get from a mug though.
We get a lot of high quality data that's relatively the same. We run the same routines every day, doing more or less the same things, which makes us extremely reliable at what we do but not very worldly.
LLMs get the opposite: sparse, relatively low quality, low modality data that's extremely varied, so they have a much wider breadth of knowledge but they're pretty fragile in comparison since they get relatively little experience on each topic and usually no chance to affirm learning with RL.
It’s unlikely sensory data contributes to intelligence in human beings. Blind people take in far, far less sensory data than sighted people, and yet are no less intelligent. Think of Helen Keller - she was deafblind from an early age, and yet was far more intelligent than the average person. If your hypothesis is correct, and development of human intelligence is primarily driven by sensory data, how do you reconcile this with our observations of people with sensory impairments?
Blind people tend to have less spatial intelligence though, like significantly more. Not very nice to say like that, and of course they often develop heightened intelligence in other areas, but we do consider human-level spatial reasoning a very important goal in AI.
People with sensory impairments from birth may be restricted in certain areas, on account of the sensory impairment, but are no less generally cognitively capable than the average person.
> but are no less generally cognitively capable than the average person
I think this would depend entirely on how the sensory impairment came about, since most genetic problems are not isolated, but carry a bunch of other related problems (all of which can impact intelligence).
Lose your eye sight in an accident? I would grant there is likely no difference on average.
Otherwise, the null hypothesis is that intelligence (and a whole host of other problems) are likely worse, on average.
> It’s unlikely sensory data contributes to intelligence in human beings.
This is clearly untrue. All information a human ever receives is through sensory data. Unless your position is that the intelligence of a brain that was grown in a vat with no inputs would be equivalent to that of a normal person.
Now, does rotating a coffee mug and feeling its weight, seeing it from different angles, etc. improve intelligence? Actually, still yes, if your intelligence test happens to include questions like “is this a picture of a mug” or “which of these objects is closest in weight to a mug”.
>Unless your position is that the intelligence of a brain that was grown in a vat with no inputs would be equivalent to that of a normal person.
Entirely possible - we just don’t know. The closest thing we have to a real world case study is Helen Keller and other people with significant sensory impairments, who are demonstrably unimpaired in a general cognitive sense, and in many cases more cognitively capable than the average unimpaired person.
I think you are trying to argue for a very abstract notion of intelligence that is divorced from any practical measurement. I don’t know how else to interpret your claim that inputs are divorced from intelligence (and that we don’t know if the brain in a jar is intelligent).
This seems like a very philosophical standpoint, rather than practical. And I guess that’s fine, but I feel like the implication is that if an LLM is in some way intelligent, then it was exactly as intelligent before training. So we are talking about “potential intelligence“? Does a stack of GPU’s have “intelligence”?
Intelligence isn’t rigorously defined or measurable, so any conversation about the nature of intelligence will be inherently philosophical. Like it or not, intelligence just is an abstract concept.
I’m trying to illustrate that the constraints that apply to LLMs don’t necessarily apply to humans. I don’t believe human intelligence is reliant upon sensory input.
It can’t be both. If intelligence is this abstract and philosophical then the claims about inputs not being relevant for human intelligence are meaningless. It’s equally meaningless to say that constraints on LLM intelligence don’t apply to human intelligence. In the absence of a meaningful definition of intelligence, these statements are not grounded in anything.
The term cannot mean something measurable or concrete when it’s convenient, but be vague and indefinable when it’s not.
LLMs are language models. We interact with them using language, all of that, but also only that. That doesn't mean that they have "common sense", context, same motivations, agency, or even reasoning like us.
But as we interact with other people using mostly language, and since the start of internet a lot of those interactions happen in way similar to how we interact with AI, the difference is not so obvious. We are falling into the Turing test in this, mostly because that test is more about language than about intelligence.
What matters is what happen in the outside. We don't know what happen in our inside (or the inside of others, at least), we know the language and how it is used, event the meanings don't have to be the same as long as it is consistent. And you get that by construction. Does that mean intelligence, self consciousness, soul or whatever? We only know that it walk like a duck and quacks like a duck.
But have you considered that humans really really want to feel like they're unique and special and exceptional?
If it walks like a duck and quacks like a duck, then it's not anything remotely resembling a duck, and is just a bag of statistics doing surface level duck imitation. According to... ducks, mostly.
If your takeaway from the LLM breakthrough is "abstract thinking is actually quite mundane", then at least you're heading in the right direction. Some people are straight up in denial.
You have no idea what abstract thinking actually is but you are convinced the illusion presented by an LLM is it. Your ontology is confused but I doubt you are going to figure out why b/c that would require some abstract thinking which you're convinced is no more special than matrix arithmetic.
If I wanted worthless pseudo-philosophical drivel, I'd ask GPT-2 for some. Otherwise? Functional similarity at this degree is more than good enough for me.
By now, I am fully convinced that this denial is "AI effect" in action. Which, in turn, is nothing but cope and seethe driven by human desire to remain Very Special.
I feel like the interface in this case has caused us to fool ourselves into thinking there's more there than there is.
Before 2022 (most of history), if you had a long seemingly sensible conversation with something, you could safely assume this other party was a real thinking human mind.
it's like a duck call.
edit, i want to add because this is neural net that's trained to output sensible text, language isn't just the interface.
unlike a website there's no separation between anything, with LLM's the back and front end are all one blob.
edit2: seems I have upset the ducks that think the duck call is a real duck .
> LLMs process information very differently. They look at everything in parallel, all at once, and can use the whole context in one shot. Their “memory” is stored across billions of tiny weights, and they retrieve information by matching patterns, not by searching through memories like we do. Researchers have shown that LLMs automatically learn specific little algorithms (like copying patterns or doing simple lookups), all powered by huge matrix multiplications running in parallel rather than slow, step-by-step reasoning.
I think this is incorrect on two accounts: Yes, transformers and individual layers are parallel, but the entire network is not. On a first level, it's obviously sequential over generated tokens - but even generation of a single token is sequential in the number of layers that the information travels through.
Both those constraints are comparable to the way humans think I believe. (The human brain doesn't have neatly organized layers, but it does have "pathways" where certain brain regions project into other brain regions)
I like "ghosts" as a simple metaphor for what you chat with when you chat with AI. Usually we chat with Casper the Friendly Ghost, but there are a lot of other ghosts that can be conjured up.
Some people are obsessed with chatting with ghosts. It seems like a rational adult couldn't be seriously harmed by chatting with a ghost, but there are news reports showing that some people get possessed.
I think there might be a slight bias in this blog article in favor of their product/service. Their human verification service probably needs AI to have less humanness.
But as we saw over the course of recent months or years, AI outputs are becoming more indistinguishable for human output.
Our main argument is that outputs will become increasingly indistinguishable, but the processes won't. E.g. in 5 years if you watch an AI book a flight it will do it in a very non-human way, even if it gets the same flight you yourself would book.
If the observable behavior (output) becomes indistinguishable (which I’m doubtful of), what does it matter that the internal process is different? Surely only to the extent that the behavior still exhibits differences after all?
> in 5 years if you watch an AI book a flight it will do it in a very non-human way
I would bet completely against this, models are becoming more human-like, not less, over time.
What's more likely to change (that would cause a difference) is the work itself changing to adapt to areas where models are already super-human, such as being able to read entire novels in seconds with full attention.
One thing I don't understand in these conversations is why we're treating LLMs as if they are completely interchangeable with chatbots/assistants.
A car is not just an engine, it's a drivetrain, a transmission, wheels, steering, all of which affect the end-product and its usability. LLMs are no different, and focusing on alignment without even addressing all the scaffolding that intermediates the exchange between the user and the LLM in an assistant use case seems disingenuous.
Unfortunately a lot of the hype around LLMs is that their capability is humanness, specifically that they are (much) cheaper humans for replacing your expensive and annoying current humans.
I think that's the first time the C suite is so interested in having their employees using a tool regardless of the result. It likes prescribing that you have to send an email twice a day using outlook. And make sure to use attachment both time.
Language is not humanness either; it is a disembodied artifact of our extended cognition, it is a way of transferring the contents of our consciousness to others or to ourselves over time. This is precisely what LLMs piggyback on and therefore are exceedingly good at simulating, which is why the accuracy of "is this human" tools are stuck at %60-70's (%50 is a coin flip), and are going to be bounded for a foreseeable future.
And I am sorry to be negative but there is so much bad cognitive science in this article that I couldn't take the product seriously.
> LLMs can be scaled almost arbitrarily in ways biological brains cannot: more parameters, more training compute, more depth.
- Capacity of raw compute is irrelevant without mentioning the complexity of computation task at hand. LLM's can scale - not infinitely - but they solve for O(n^2) tasks. It is also amiss to think human compute = a singular human's head. Language itself is both a tool and protocol of distributed compute among humans. You borrow a lot of your symbolic preprocessing from culture! Like said, this is exactly what LLM's piggyback on.
> We are constantly hit with a large, continuous stream of sensory input, but we cannot process or store more than a very small part of it.
- This is called relevance, and we are so frigging good at it! The fact that machine has to deal with a lot more unprioritized data in a relatively flat O(n^2) problem formulation is a shortcoming, not a feature. Visual cortex is such an opinionated accelerator of processing all that massive data that only the relevant bits need to make to your consciousness. And this architecture was trained for hundreds of millions of years, over trillions of experiment arms - that were in parallel experimenting on everything else too.
> Humans often have to act quickly. Deliberation is slow, so many decisions rely on fast, heuristic processing. In many situations (danger, social interaction, physical movement), waiting for more evidence simply isn't an option.
- Again a lot of this equivocates conscious processing to entire cognition. Anyone who plays sports or music knows to respect the implicit, embodied cognition that goes on to achieve complex motor tasks. We are yet to see a non-massively-fast-forwarded household robot do a mundane kitchen cleaning task, and go play table tennis with the same motor "cortex". Motor planning and articulation is a fantastically complex computation; just because it doesn't make it to our consciousness or instrumented exclusively through language doesn't mean it is not.
> Human thinking works in a slow, step-by-step way. We pay attention to only a few things at a time, and our memory is limited.
- Thinking, Fast and Slow by Kahneman is a fantastic way of getting into how much more complex the mechanism is.
The key point here is as limited in their recall, how good humans are at relevance, because it matters, because it is existential. Therefore when you are using a tool to extend your recall, it is important to see its limitations. Google search having indexed billions of pages is not a feature if it can't bring the top results well. If it gets the capability to sell me whatever it brought up was relevant, that still doesn't mean the results are actually relevant. And this is exactly the degradation of relevance we are seeing in our culture.
I don't care if the language terminal is a human or a machine, if the human was convinced by the low relevance crap of the machine it just a legitimacy laundering scheme. Therefore this is not a tech problem, it is a problem of culture; we need to be simultaneously cultivating epistemic humility, including quitting the Cartesian tyranny of worshipping explicit verbal cognition that is assumed to be locked up in a brain; we have to accept that we are also embodied and social beings that depend on a lot of distributed compute to solve for agency.
> Compared to humans, LLMs have effectively unbounded training data. They are trained on billions of text examples covering countless topics, styles, and domains. Their exposure is far broader and more uniform than any human's, and not filtered through lived experience or survival needs.
I think it's the other way round: humans have effectively unbounded training data. We can count exactly how much text any given model saw during training. We know exactly how many images or video frames were used to train it, and so on. Can we count the amount of input humans receive?
I can look at my coffee mug from any angle I want, I can feel it in my hands, I can sniff it, lick it and fiddle with it as much as I want. What happens if I move it away from me? Can I turn it this way, can I lift it up? What does it feel like to drink from this cup? What does it feel like when someone else drinks from my cup? The LLM has no idea because it doesn't have access to sensory data and it can't manipulate real-life objects (yet).
A big challenge is that the LLM cannot selectively sample it's training set. You don't forget what a coffee cup looks like just because you only drank water for a week. LLMs on the other hand will catastrophically forget anything in their training set when the training set does not have a uniform distribution of samples in each batch.
This is a fair criticism we should've addressed. There's actually a nice study on this: Vong et al. (https://www.science.org/doi/10.1126/science.adi1374) hooked up a camera to a baby's head so it would get all the input data a baby gets. A model trained on this data learned some things babies do (eg word-object mappings), but not everything. However, this model couldn't actively manipulate the world in the way that a baby does and I think this is a big reason why humans can learn so quickly and efficiently.
That said, LLMs are still trained on significantly more data pretty much no matter how you look at it. E.g. a blind child might hear 10-15 million words by age 6 vs. trillions for LLMs.
> hooked up a camera to a baby's head so it would get all the input data a baby gets.
A camera hooked up to the baby's head is absolutely not getting all the input data the baby gets. It's not even getting most of it.
While an LLM is trained on trillions of tokens to acquire its capabilities, it does not actively retain or recall the vast majority of it, and often enough is not able to make deductive reasoning either (e.g. X owns Y does not necessarily translate to Y belongs to X).
The acquired knowledge is a lot less uniform than you’re proposing and in fact is full of gaps a human would never make. And more critically, it is not able to peer into all of its vast knowledge at once, so with every prompt what you get is closer to an “instance of a human” than “all of humanity” as you might think of LLMs.
(I train and dissect LLMs for a living and for fun)
I think you are proposing something that's orthogonal to the OP's point.
They mentioned the training data is much higher for an LLM, LLM's recall not being uniform was never in question.
No one expects compression to be without loss when you scale below knowledge entropy that exists in your training set.
I am not saying LLMs do simple compression but just pointing a mathematical certainity.
(And I think you don't need to be an expert in creating LLMs to understand them, albeit I think a lot of people here have experience with it aswell so I find the additional emphasis on it moot).
The way I understood OP’s point is that because LLMs have been trained on the entirety of humanity’s knowledge (exemplified by the internet), then surely they know as much as the entirety of humanity. A cursory use of an LLM shows this is obviously not true, but I am also raising the point that LLMs are only summoning a limited subset of that knowledge at a time when answering any given prompt, bringing them closer to a human polymath than an omniscient entity, and larger LLMs only seem to improve on the “depth” of that polymath knowledge rather than the breadth of it.
Again just my impression from exposure to many LLMs at various states of training (my last sentence was not an appeal to expertise)
Not only that, but humans also have access to all of the "training data" of hundreds of millions of years of evolution baked into our brains.
I don’t think the amount of data is essential here. The human genome is only around 750 MB, much less than current LLMs, and likely only a small fraction of it determines human intelligence. On the other hand, current LLMs contain immense amounts of factual knowledge that a human newborn carries zero information about.
Intelligence likely doesn’t require that much data, and it may be more a question of evolutionary chance. After all, human intelligence is largely (if not exclusively) the result of natural selection from random mutations, with a generation count that’s likely smaller than the number of training iterations of LLMs. We haven’t found a way yet to artificially develop a digital equivalent effectively, and the way we are training neural networks might actually be a dead end here.
That just says "low Kolmogorov complexity". All the priors humans ship with can be represented as a relatively compact algorithm.
Which gives us no information on computational complexity of running that algorithm, or on what it does exactly. Only that it's small.
LLMs don't get that algorithm, so they have to discover certain things the hard way.
Which must be doing some heavy lifting.
Humans ship with all the priors evolution has managed to cram into them. LLMs have to rediscover all of it from scratch just by looking at an awful lot of data.
OTOH, all that data is built on patterns that evolved from many years of evolution, so I think the LLM benefits from that evolution also.
Sure, but LLMs are trying to build the algorithms of the human mind backwards, converge on similar functionality based on just some of the inputs and outputs. This isn't an efficient or a lossless process.
The fact that they can pull it off to this extent was a very surprising finding.
There's only so much information content you can get from a mug though.
We get a lot of high quality data that's relatively the same. We run the same routines every day, doing more or less the same things, which makes us extremely reliable at what we do but not very worldly.
LLMs get the opposite: sparse, relatively low quality, low modality data that's extremely varied, so they have a much wider breadth of knowledge but they're pretty fragile in comparison since they get relatively little experience on each topic and usually no chance to affirm learning with RL.
It’s unlikely sensory data contributes to intelligence in human beings. Blind people take in far, far less sensory data than sighted people, and yet are no less intelligent. Think of Helen Keller - she was deafblind from an early age, and yet was far more intelligent than the average person. If your hypothesis is correct, and development of human intelligence is primarily driven by sensory data, how do you reconcile this with our observations of people with sensory impairments?
Blind people tend to have less spatial intelligence though, like significantly more. Not very nice to say like that, and of course they often develop heightened intelligence in other areas, but we do consider human-level spatial reasoning a very important goal in AI.
People with sensory impairments from birth may be restricted in certain areas, on account of the sensory impairment, but are no less generally cognitively capable than the average person.
> but are no less generally cognitively capable than the average person
I think this would depend entirely on how the sensory impairment came about, since most genetic problems are not isolated, but carry a bunch of other related problems (all of which can impact intelligence).
Lose your eye sight in an accident? I would grant there is likely no difference on average.
Otherwise, the null hypothesis is that intelligence (and a whole host of other problems) are likely worse, on average.
> It’s unlikely sensory data contributes to intelligence in human beings.
This is clearly untrue. All information a human ever receives is through sensory data. Unless your position is that the intelligence of a brain that was grown in a vat with no inputs would be equivalent to that of a normal person.
Now, does rotating a coffee mug and feeling its weight, seeing it from different angles, etc. improve intelligence? Actually, still yes, if your intelligence test happens to include questions like “is this a picture of a mug” or “which of these objects is closest in weight to a mug”.
>Unless your position is that the intelligence of a brain that was grown in a vat with no inputs would be equivalent to that of a normal person.
Entirely possible - we just don’t know. The closest thing we have to a real world case study is Helen Keller and other people with significant sensory impairments, who are demonstrably unimpaired in a general cognitive sense, and in many cases more cognitively capable than the average unimpaired person.
I think you are trying to argue for a very abstract notion of intelligence that is divorced from any practical measurement. I don’t know how else to interpret your claim that inputs are divorced from intelligence (and that we don’t know if the brain in a jar is intelligent).
This seems like a very philosophical standpoint, rather than practical. And I guess that’s fine, but I feel like the implication is that if an LLM is in some way intelligent, then it was exactly as intelligent before training. So we are talking about “potential intelligence“? Does a stack of GPU’s have “intelligence”?
Intelligence isn’t rigorously defined or measurable, so any conversation about the nature of intelligence will be inherently philosophical. Like it or not, intelligence just is an abstract concept.
I’m trying to illustrate that the constraints that apply to LLMs don’t necessarily apply to humans. I don’t believe human intelligence is reliant upon sensory input.
It can’t be both. If intelligence is this abstract and philosophical then the claims about inputs not being relevant for human intelligence are meaningless. It’s equally meaningless to say that constraints on LLM intelligence don’t apply to human intelligence. In the absence of a meaningful definition of intelligence, these statements are not grounded in anything.
The term cannot mean something measurable or concrete when it’s convenient, but be vague and indefinable when it’s not.
LLMs are language models. We interact with them using language, all of that, but also only that. That doesn't mean that they have "common sense", context, same motivations, agency, or even reasoning like us.
But as we interact with other people using mostly language, and since the start of internet a lot of those interactions happen in way similar to how we interact with AI, the difference is not so obvious. We are falling into the Turing test in this, mostly because that test is more about language than about intelligence.
> But as we interact with other people using mostly language,
Didn't they used to say that 90% of communication is non-verbal?
Look, that was a while ago, when people met IRL. So maybe not as true today.
"Language" is just the interface. What happens on the inside of LLMs is a lot weirder than that.
What matters is what happen in the outside. We don't know what happen in our inside (or the inside of others, at least), we know the language and how it is used, event the meanings don't have to be the same as long as it is consistent. And you get that by construction. Does that mean intelligence, self consciousness, soul or whatever? We only know that it walk like a duck and quacks like a duck.
But have you considered that humans really really want to feel like they're unique and special and exceptional?
If it walks like a duck and quacks like a duck, then it's not anything remotely resembling a duck, and is just a bag of statistics doing surface level duck imitation. According to... ducks, mostly.
Which arithmetic operation in an LLM is weird?
The fact that you can represent abstract thinking as a big old bag of matrix math sure is.
So it's not weird, it's actually very mundane.
If your takeaway from the LLM breakthrough is "abstract thinking is actually quite mundane", then at least you're heading in the right direction. Some people are straight up in denial.
You have no idea what abstract thinking actually is but you are convinced the illusion presented by an LLM is it. Your ontology is confused but I doubt you are going to figure out why b/c that would require some abstract thinking which you're convinced is no more special than matrix arithmetic.
If I wanted worthless pseudo-philosophical drivel, I'd ask GPT-2 for some. Otherwise? Functional similarity at this degree is more than good enough for me.
By now, I am fully convinced that this denial is "AI effect" in action. Which, in turn, is nothing but cope and seethe driven by human desire to remain Very Special.
Which matrix arithmetic did you perform to find that gold nugget of insight?
"Weirder" does not mean "more complex" or "more human-like".
And?
I feel like the interface in this case has caused us to fool ourselves into thinking there's more there than there is.
Before 2022 (most of history), if you had a long seemingly sensible conversation with something, you could safely assume this other party was a real thinking human mind.
it's like a duck call.
edit, i want to add because this is neural net that's trained to output sensible text, language isn't just the interface.
unlike a website there's no separation between anything, with LLM's the back and front end are all one blob.
edit2: seems I have upset the ducks that think the duck call is a real duck .
> LLMs process information very differently. They look at everything in parallel, all at once, and can use the whole context in one shot. Their “memory” is stored across billions of tiny weights, and they retrieve information by matching patterns, not by searching through memories like we do. Researchers have shown that LLMs automatically learn specific little algorithms (like copying patterns or doing simple lookups), all powered by huge matrix multiplications running in parallel rather than slow, step-by-step reasoning.
I think this is incorrect on two accounts: Yes, transformers and individual layers are parallel, but the entire network is not. On a first level, it's obviously sequential over generated tokens - but even generation of a single token is sequential in the number of layers that the information travels through.
Both those constraints are comparable to the way humans think I believe. (The human brain doesn't have neatly organized layers, but it does have "pathways" where certain brain regions project into other brain regions)
I like "ghosts" as a simple metaphor for what you chat with when you chat with AI. Usually we chat with Casper the Friendly Ghost, but there are a lot of other ghosts that can be conjured up.
Some people are obsessed with chatting with ghosts. It seems like a rational adult couldn't be seriously harmed by chatting with a ghost, but there are news reports showing that some people get possessed.
It's a better metaphor than parrots, anyway.
more:
https://karpathy.bearblog.dev/animals-vs-ghosts/
I think there might be a slight bias in this blog article in favor of their product/service. Their human verification service probably needs AI to have less humanness.
But as we saw over the course of recent months or years, AI outputs are becoming more indistinguishable for human output.
Our main argument is that outputs will become increasingly indistinguishable, but the processes won't. E.g. in 5 years if you watch an AI book a flight it will do it in a very non-human way, even if it gets the same flight you yourself would book.
If the observable behavior (output) becomes indistinguishable (which I’m doubtful of), what does it matter that the internal process is different? Surely only to the extent that the behavior still exhibits differences after all?
> in 5 years if you watch an AI book a flight it will do it in a very non-human way
I would bet completely against this, models are becoming more human-like, not less, over time.
What's more likely to change (that would cause a difference) is the work itself changing to adapt to areas where models are already super-human, such as being able to read entire novels in seconds with full attention.
One thing I don't understand in these conversations is why we're treating LLMs as if they are completely interchangeable with chatbots/assistants.
A car is not just an engine, it's a drivetrain, a transmission, wheels, steering, all of which affect the end-product and its usability. LLMs are no different, and focusing on alignment without even addressing all the scaffolding that intermediates the exchange between the user and the LLM in an assistant use case seems disingenuous.
Unfortunately a lot of the hype around LLMs is that their capability is humanness, specifically that they are (much) cheaper humans for replacing your expensive and annoying current humans.
> that they are (much) cheaper humans
This is literally their inhumanness.
What I think you mean to say is that AI is promoted as fungible with humans at a lower price point.
I think that's the first time the C suite is so interested in having their employees using a tool regardless of the result. It likes prescribing that you have to send an email twice a day using outlook. And make sure to use attachment both time.
Language is not humanness either; it is a disembodied artifact of our extended cognition, it is a way of transferring the contents of our consciousness to others or to ourselves over time. This is precisely what LLMs piggyback on and therefore are exceedingly good at simulating, which is why the accuracy of "is this human" tools are stuck at %60-70's (%50 is a coin flip), and are going to be bounded for a foreseeable future.
And I am sorry to be negative but there is so much bad cognitive science in this article that I couldn't take the product seriously.
> LLMs can be scaled almost arbitrarily in ways biological brains cannot: more parameters, more training compute, more depth.
- Capacity of raw compute is irrelevant without mentioning the complexity of computation task at hand. LLM's can scale - not infinitely - but they solve for O(n^2) tasks. It is also amiss to think human compute = a singular human's head. Language itself is both a tool and protocol of distributed compute among humans. You borrow a lot of your symbolic preprocessing from culture! Like said, this is exactly what LLM's piggyback on.
> We are constantly hit with a large, continuous stream of sensory input, but we cannot process or store more than a very small part of it.
- This is called relevance, and we are so frigging good at it! The fact that machine has to deal with a lot more unprioritized data in a relatively flat O(n^2) problem formulation is a shortcoming, not a feature. Visual cortex is such an opinionated accelerator of processing all that massive data that only the relevant bits need to make to your consciousness. And this architecture was trained for hundreds of millions of years, over trillions of experiment arms - that were in parallel experimenting on everything else too.
> Humans often have to act quickly. Deliberation is slow, so many decisions rely on fast, heuristic processing. In many situations (danger, social interaction, physical movement), waiting for more evidence simply isn't an option.
- Again a lot of this equivocates conscious processing to entire cognition. Anyone who plays sports or music knows to respect the implicit, embodied cognition that goes on to achieve complex motor tasks. We are yet to see a non-massively-fast-forwarded household robot do a mundane kitchen cleaning task, and go play table tennis with the same motor "cortex". Motor planning and articulation is a fantastically complex computation; just because it doesn't make it to our consciousness or instrumented exclusively through language doesn't mean it is not.
> Human thinking works in a slow, step-by-step way. We pay attention to only a few things at a time, and our memory is limited.
- Thinking, Fast and Slow by Kahneman is a fantastic way of getting into how much more complex the mechanism is.
The key point here is as limited in their recall, how good humans are at relevance, because it matters, because it is existential. Therefore when you are using a tool to extend your recall, it is important to see its limitations. Google search having indexed billions of pages is not a feature if it can't bring the top results well. If it gets the capability to sell me whatever it brought up was relevant, that still doesn't mean the results are actually relevant. And this is exactly the degradation of relevance we are seeing in our culture.
I don't care if the language terminal is a human or a machine, if the human was convinced by the low relevance crap of the machine it just a legitimacy laundering scheme. Therefore this is not a tech problem, it is a problem of culture; we need to be simultaneously cultivating epistemic humility, including quitting the Cartesian tyranny of worshipping explicit verbal cognition that is assumed to be locked up in a brain; we have to accept that we are also embodied and social beings that depend on a lot of distributed compute to solve for agency.