Is my toddler a stochastic parrot?
newyorker.com
newyorker.com
Perhaps worth noting that it is possible to teach infants (often starting at around 9 months) sign language so that they can more easily signal their desires.
Some priority recommended words would probably be:
* hungry/more
* enough/all done (for when they're full)
* drink (perhaps both milk/formula and water† gestures)
See:
* https://babysignlanguage.com/chart/
* https://www.thebump.com/a/how-to-teach-baby-sign-language
These are not (AFAICT) 'special' symbols for babies, but the regular ASL gestures for the work in question. If you're not native English-speaking you'd look up the gestures in your specific region/language's sign language:
* https://en.wikipedia.org/wiki/List_of_sign_languages
* https://en.wikipedia.org/wiki/Sign_language
† Another handy trick I've run across: have different coloured containers for milk and water, and consistently put the same contents in each one. That way the infant learns to grab a particular colour depending on what they're feeling like.
I have friends who had much more success with it, but the value will largely depend on your child’s relative developmental strengths. A friend’s son with autism got literally years’ benefit out of the gestures before verbal speech caught up.
Could still useful: instead of shouting across the playground on whether they have to go potty you can simply make the gesture with minimal embarrassment. :)
Please don't blame this on the signs! This doesn't mean that he would have learned to speak earlier if not for the signs. I'd be glad that he could communicate proficiently at all.
were you always talking when you signed to them? maybe they thought it went together.
It took very minimal effort on our part, and was very rewarding for him; certainly a lot better than him crying with the hope that we could guess what he wanted. Definitely recommended for any new parents.
The best moment was when he was sitting on the floor, and looked up at his mom and made the "together" sign, it was heart melting.
I can't remember exact ages and timeframes-- that time of my life is "blurry". I wish I could remember all the gestures we used. (The only ones I can remember now are "milk", "apple", and "thank you".) As she became verbal she quickly transitioned away from them.
I wish I'd learnt sign language before having kids so I just already knew how to do it, it's so cool. Props to the Ms. Rachel videos for including so many signs.
At about the same age, I bought my son a mango lassi. He looked suspiciously at it, but took a sip. With a look of shocked delight he tilted it back, back, back, and emptied the cup!
Then he put it down, looked at me, and said, "Want more!"
I'm looking forward to kids out of the house. But there are some moments that I treasure.
It probably doesn't matter either way for babies, but fyi ASL isn't a sign version of English; it is its own language. In fact American Sign Language is more closely related to French Sign Language than to British Sign Language. The Australian and New Zealand Sign Languages are largely derived from British Sign Language, so there isn't really a correlation between English speaking regions and ASL. Canadians mostly use American Sign Language and French Canadian Sign Language.
Almost 20 years later, I still know all the signs for baby food ;)
For example, voluntary supination/pronation of the forearm is generally not something a 9month old can do. If you try and teach them a common sign for "enough/finished" (fist closed, thumb pointed out, then rotation of the forearm back and forth), or "done" and "more" in the parent link, they probably won't be able to do it properly. They can copy something close to that (thumb out and wobbling their hand around? good enough!) so you have to go with the flow.
There are quite a few signs like that actually, so try and think about how many muscles move together, and how controlled or complex that is. Simple stuff is good -- and doable.
One of my favourite memories of that was when we went to see the Vivid light show in Sydney and there was a contortionist on the street so we stopped to watch. I looked into the stroller and said "What do you think?" and she made the sign for "finished". So we moved on.
This is interesting. My son also did "index finger up" in response to "thumbs up" for the longest time. Why is the thumb so hard to manipulate? Late addition to the evolutionary sequence?
You can teach chatgpt too as well. It's like a toddler. A very articulate toddler:
https://chat.openai.com/share/40c94561-2505-4938-8331-7d10ae...
It makes mistakes as any human baby would. And as a parent you can correct it.
All this means is that learning an arbitrary sign language isn't a differentiator.
My favourite moment was in March, my daughter was about to turn 2 and wasn't speaking yet.
I asked her if she would like to hear some music.
She made the sign for dog.
I searched youtube for some songs about dogs and she shook her head.
She made the sign for tree.
I was like "dog, tree", she nodded. Hmmm...
I was searching for "dog tree music" when one of the pictures that came up was a christmas tree.
She pointed to that excitedly!
I was like "dog christmas tree music" ... it took me a second to realise that she wanted to listen to the Charlie Brown Christmas soundtrack that I had had playing off YouTube at Christmas 3 months previously!
I put that on again and we danced around to it.
I thought that was totally wild! It was the first time I remember her communicating a really sophisticated preference other than just wanting to eat/drink/help etc.
Settlers interacting with natives knew full well how complex their desires were – they lived side-by-side, traded, socialised, and learned from each other.[1] Any suggestion of a primitive native is self-comforting propaganda from the industrial complex that comes after the settlers.
[1]: Indians, Settlers, and Slaves in a Frontier Exchange Economy; Usner; Omohundro Institute; 2014.
ML is pretty long way from being able to make generalizations like that from…20 samples?
But I can never seem to find more information about it, and can't recall the documentary name for the life of me. Since you seem to have at least some familiarity with this, do you happen to know what I'm talking about?
-She learned some gestures of mine instead, which I didn't realize I was doing.
-Defaulted to speech as soon as possible because it was just easier.
I think it makes a big difference, both for us (parents, to have a clue what they want) and the kid (being understood, when they can’t speak).
Which BTW I don't think is a completely absurd comparison, see https://mattasher.substack.com/p/ais-killer-app
This article mentions excitement about neural networks overgeneralizing verb inflections, which human language learners also do. If neural networks lead to the discovery of new examples of human cognitive or perceptual errors or illusions, or to the discovery of new effective methods for learning, teaching, or psychotherapy, that could count as evidence that they're a good model of our actual minds.
How would they, except as tools for analyzing research rather than research models? They don't work like human brains, so while they might sometimes exhibit something that looks like similar behavior when viewed a certain way, other than already having observed the behavior in human beings, there’s no reason to expect something they do to reflect what human brains, and moreover there’s no reason to expect useful insights from the corresponding behavior, since there is no reason to expect that the behavior responds similarly outside of the conditions where it is observed in both systems, leaving all the insight on the brain to cone from the brain (or models that, unlike artificial neural nets, we know have structural and behavioral similarities with (some parts of) human brains that are useful.)
It's not a good measurement.
Sure, but who has had a good conversation with a clock?
> people even have satisfying emotional conversations with teddy bears.
No they don't. Kids know very well that they're pretending to have conversations. The people who actually hear teddy bears speak also know that that's not normal, and we all know that this is a cognitive distortion.
This is also true of those who form emotional attachments with current AI. The people developing romantic relationships with Replika etc. aren't engaging in healthy behaviour.
https://en.wikipedia.org/wiki/Hard_problem_of_consciousness
https://philosophy.stackexchange.com/questions/77517/what-is...
Its way more productive and way less woo-woo to understand that humans have a certain tendency towards comparison, and we tend to create things that reflect our current values and conceptions of ourselves. And that "technological progress" is not a straight line, but a labyrinthine route that traces societal conceptions and priorities.
The desire for the llm to be like us is probably more realistically our desire to be like the llm!
But then again, an apple is nothing like an orange, particularly if you want to make an apple pie.
The purpose of a comparison is important in helping to define its scope.
Neural networks are not based on how neurons work. They do not copy aspects of us. They call them neural networks because they are sort of conceptually like networks of neurons in the brain but they’re so different as to make false the statement that they are based on neurons.
"Behold! The Mechanical Leg! The first technology that actually copies aspects of our very selves! Think of what wonders of self-discovery it shall reveal!" :p
P.S.: "My god, it is stronger in compression rather than shear-stresses, how eerily similar to real legs! We're on to something here!"
I recommend reading Synaptic Organization of the Brain or getting into if you are brave, the primary literature on retinal processing of visual input.
But it is nonetheless the case that “neural networks” are not called that because they are based on the way neurons work.
https://www.ibm.com/topics/neural-networks#:~:text=Their%20n...
Comparing the brain to most complex machines in history wasn't a mistake, any more than refining laws of physics were. Successive approximations.
And we're no longer at the point where we're just comparing brain to most complex machines. We have information theory now. We figured out computation, in form independent of physical medium used. So we're really trying to determine the computational model behind the brain, and one of the ways to do it is to implement some computational models in whatever is most convenient (usually software running on silicon), and see if it's similar. Slowly but surely, we're mapping and matching computational aspects of the brain. LLMs are just one recent case where we got a spectacularly good match.
Not, apparently, the person I was replying to!
https://www.ibm.com/topics/neural-networks#:~:text=Their%20n....
(IMHO, Rosenblatt is an underappreciated genius; he had a working shallow computer vision hardware computer long before people even appreciated what an accomplishment that was. The hardware was fascinating- literally self-turning potentiometer knobs to update weights.
A theoretical such thing might be for us to look at, say, human arms and say "Well, this gripping thing is a cool piece of functionality. Let's build an artificial device that does this. But we don't have muscle contraction tech, so we'll put actuators in the gripping portion. All right, we've built an arm. It seems like if we place it in this position it minimizes mechanical wear when not in action and makes it unlikely for initial movement to create undesired results. I wonder if human arms+hands have the same behaviour. Ah, looks like not, but that would have been interesting if it were the case"
Essentially that's just the process of extracting substructure and then seeing if there is a homomorphism (smooshy type abuse here) between two structures as a way to detect yet hidden structure. Category theory is almost all this. I suppose the reason they find it annoying is that there are many mappings that are non-homomorphic and so these are the false cognates of concepts.
Still, I think the whole "An ANN is not a brain" thing is overdone. Of course not. A mechanical arm is not an arm, but they both have response curves, and one can consider a SLAM approach for the former and compare with the proprioceptive view of the latter. It just needs some squinting.
Anyway, considering your familiarity with R and his work, I think I'm not speaking to the uninitiated, but I thought it worth writing anyway.
Ultimately it's largely down to misapprehension of the difference between emulating a neuton and simulating a neuron, and defensiveness about an approximate model.
Most neuroscientists do not indulge in deep thought because it does not pay the bills. NIH does not push us toward “understanding cognition and consciousness”. They push us toward treatments for Alzheimer’s, schizophrenia, stroke, epilepsy, and addiction. We do not have time for the fun stuff until we are ready to retire, which is when many of us become terrible neurophilosophers, e.g., Eccles.
Both Humans and GPT are neural networks. Who cares that GPT doesn't have feathers or flap its wings? That's not the question to care bout. We are interested in whether GPT flies. You can sigh to Kingdom come and nothing will change that.
We've developed numerous different learning algorithms that are biologically plausible, but they all kinda work like backpropagation but worse, so we stuck with backpropagation. We've made more complicated neurons that better resemble biological neurons, but it is faster and works better if you just add extra simple neurons, so we do that instead. Spiking neural networks have connection patterns more similar to what you see in the brain, but they learn slower and are tougher to work with than regular layered neural networks, so we use layered neural networks instead.
The Wright brothers probably experimented with gluing feathers onto their gliders, but eventually decided it wasn’t worth the effort. Because that's not what is important.
https://cs.stanford.edu/people/eroberts/courses/soco/project....
Key word: "neurophysiologist"
I point this out because I think the idea of comparing ourselves to recent tech is more about using the technology as a metaphor for self, and it's worth incorporating the other ways we have done so historically for context.
[1]: https://online.ucpress.edu/SLA/article/2/4/542/83344/The-Bra...
Step B: turn it on its head "the human brain is nothing more than... <insert machine here.>"
It's a bit tautological.
The worry is that there's a Step C: Humans actually start to behave as simple as said machine.
They emulate the faculty, rather than biology.
A very large number, e.g., lots of implants and prosthetic devices for one fairly large class.
Consciousness is first a word and second a concept. And it’s a word that ChatGPT or Llama can use in an English sentence better than billions of humans worldwide. The software folks have made even more progress than sociologists, psychologists and neuroscientists to be able to create an artificial language cortex before we understand our biological mind comprehensively.
If you wait until conscious sentient AI is here to make your opinions known about the implications and correct policy decisions, you will already be too late to have an input. ChatGPT can already tell you a lot about itself (showing awareness) and will gladly walk you through its “thinking” if you ask politely. Given that it contains a huge amount of data about Homo sapiens and its ability to emulate intelligent conversation, you could even call it Sapient.
Having any kind of semantic argument over this is futile because a character AI that is hypnotized to think it is conscious, self-aware and sentient in its emulation of feelings and emotion would destroy most people in a semantics debate.
The field of philosophy is already ripe with ideas from hundreds of years ago that an artificial intelligence can use against people in debates of free will, self-determination and the nature of existence. This isn’t the battle to pick.
I’m not saying computers can’t have something like it, but it would be so fundamentally different as to be completely alien and unrelatable to humans, and thus (IMO) non-contiguous with human culture.
I wish we could keep this as an immutable truth, but give some sick girls and boys in Silicon Valley a few more years and they will make true creatures. No, I think that we should be honest to ourselves and stop searching for what make us special (other than our history, and being first, that is). It's okay to be self-interested and say "we want to remain in control, AIs should not be, no matter how much (or if) better they are than us."
Thank you for solving the riddle!
What AI lacks compared to humans, is what humans share in common with all of life, a motivation for survival.
As the creators of AI we need to be mindfully aware that we are the moral agents, we are the ones with motives, desires and goals, and we are responsible for making ethical decisions.
My bicycle can go down a hill much faster than my feet, but it doesn’t go anywhere without me because it doesn’t have anywhere it wants to go. I’m the one providing directions.
Regarding this line:
> ChatGPT can already tell you a lot about itself (showing awareness) and will gladly walk you through its “thinking” if you ask politely.
Is it actually walking you through its thinking? Or is it walking you through an imagined line of thinking?
Regardless, your main point still stands. That a program doesn't think the same way a human does, doesn't mean it isn't "thinking".
You can prompt an LLM model to provide reasoning first and an answer second and it becomes one and the same.
Worth keeping in mind that all of these points are orthogonal to the quality of reasoning, the bias, or the intentions of the system builders. And building something that emulates humans convincingly, you can expect it to emulate both the good and bad qualities naturally.
Because a human is measuring it unfairly.
The output without CoT is valid. It is syntactically valid. The observer is unhappy with the semantic validity, because the observer has seen syntactic validity and assumed that semantic validity is a given.
Like it would if the model was alive.
This is observer error, not model error.
Uh, wouldn't that apply equally to any other topic that has been argued extensively before, and to any tenable position on those topics? Like, I can make ChatGPT argue against your LLM sentience apologist-bot just as easily.
My point is that there are good reasons to believe that a hypothetical LLM that can pass a Turing test is a philosophical zombie, i.e. it can mimic human behavior but doesn't have an internal life, feelings, emotions, and isn't conscious. Whether that distinction is important is another question. LLMs provide evidence that consciousness may not be necessary to create sophisticated AIs that can pass or exceed human performance.
I'm curious how you know this. Certainly an LLM doesn't have human internal life, but to claim it has no internal life exceeds our state of knowledge on these topics. We simply lack a mechanistic model of qualia from which we can draw such conclusions.
Once you get that out of the way, sure, I guess it could be "alive" per the same loose definition that any electrical system can exist in an actuated state. It's software, though. I don't think it's profound or overly confident to say that we very clearly know these systems are inanimate and non-living.
>An LLM is math
Isn't everything math? Math is what we use to describe and model the nature of our reality.
>Those things aren't alive
We don't have a universal definition of alive, or at least I'm not aware of one that isn't purely philosophical or artistic. We therefore can't actually determine what is and isn't alive with much confidence.
>both the software and hardware used to facilitate it is artificial
Define artificial? We're made of exactly the same stuff. Complex chemical elements that were created from more basic ones in collapsing stars, arranged in a particular configuration with electricity flowing through it all and providing some kind of coordination. We're definitely a bit more "squishy", I'll give you that.
> Isn't everything math? Math is what we use to describe and model the nature of our reality.
Most things are measurable, yea. LLMs in particular rely on math to understand language. Outside of a probabilistic context, LLMs do not understand words or sentences. That's... not really how you or I think. It's not how we understand animals or even plants to behave. Probibalism in nature gets you killed; deviation and unique fitness is what keeps the Darwinian wheel rolling.
> We therefore can't actually determine what is and isn't alive with much confidence.
Our speculation does a good enough job, I'd say. Sand and rocks aren't alive. Carbon isn't alive unless it is dictated by biological autonomy. Silicon chips aren't "alive" outside the sense that copper wires and your wall outlet has a lived experience. Software has a "lifecycle", but that's more used to describe human patterns of development instead of software behavior.
When you roll all this up into one scenario, I see a pretty airtight argument that AI is not animate. If someone does prove that AI is a living organism, the implications would honestly be more dire outside of AI. It would overturn all modern understanding of software, which is why I figure it's not likely to be the case.
This is a much harder hypothetical, though. Assuming you get everything right, you'd have a pretty powerful simulation at-hand. I think what you're describing is the difference between an artificial lung and a real-life cloned sheep embryo, though. An iron lung is only alive in the sense that it simulates a complex biological process with technology. It's damn impressive and very capable, but living respiration looks and behaves differently. Just simulating the desired input and output won't give you de-facto biology.
A simulation tries to do what the original does though. For instance a GameBoy emulator actually runs code in the way the original device did, right?
Perhaps the test here is a bit like the Turing test. Could you tell whether the artificial device was being used, or the actual thing, apart from examining the device, of course:
- An artificial lung doesn't make people breathe in quite the same way I suspect.
- An artificial brain would give answers a bit like what ChatGPT can already do.
Then there's the concept of panpsychism.
Its readily apparent after a point that the previous poster is correct, and that these are simply tools.
They are impressive tools yes. But life they are not. At best, they are mimics.
I have longer comments in my history, you can read those if you must.
Besides, why wouldnt someone build things? This is HN.
A mimicry of fire is not fire because fire is a reaction between matter. A mimicry of intelligence may well be intelligence, because intelligence is just information processing.
We're not talking here about "life", but intelligence and "internal life", aka feelings, thoughts, etc.
Edit: as for the argument that "LLMs are simply tools", it has been quite easy for our ancestors to also dehumanize other people and treat them as tools. That was a core justification of slavery. This is simply not a convincing argument that LLMs lack an internal life. People fool themselves all of the time.
I am saying that LLMs DONT process information, not the way people are implying.
The best I can suggest is to please try building with an LLM. Its very hard to explain the nuance, and I am not the only one struggling with it.
[1] https://pub.towardsai.net/llms-and-memory-is-definitely-all-...
I'm also not even sure why you're talking about "alive" or "inanimate". From context it's clear we're talking about "internal life", meaning experience, feelings, thoughts, etc., not some ability to procreate, or even necessarily some continuity of existence.
So do you have any evidence of that existing, then? Because so far, it sounds like speculation.
LLMs do not feel or think or experience anything, in the same way that particle physics is not inherently sentient. LLM architectures are not designed to resemble life, or even understand human language for the most part. It's understandable that someone would look at written text and say "it's a sign of life!", but Markov Chains and LISP bots were no closer to achieving "internal life" or what have you.
Next, you'll play the "but how are us humans alive?" card. Humans not only qualify as animate life, but our brains are not artificial purpose-built tools. We are an imperfect computer, loaded with irreconcilable differences and improper conduct. We feel biological needs and immediate responses to stimuli. Our preconception of 'love' and 'hate' is not built on the words they're associated with, but the emotions they make us feel. Our lived experience is not limited to a preserved context, but rather our whole lives stretching across years of existence. Our thoughts are not dictated by what we write and say, and the way we present ourselves is not limited by the way we are finetuned.
Human life is self-evident and precious. AI life is seemingly non-existent, and based on dubious non-technical claims.
Panpsychists would like a word.
> but Markov Chains and LISP bots were no closer to achieving "internal life" or what have you.
I'm simply making clear that this is a statement of belief as opposed to a statement of fact. You believe that true life or true sentience has certain qualities that you believe LLMs are lacking, therefore you conclude that LLMs are not alive and not sentient. I'm simply saying that we don't know enough about sentience to turn such statements of belief into statements of fact (and I don't know what "life" is in such a context, so I won't touch that).
> but our brains are not artificial purpose-built tools
Again, artificiality has nothing to do with whether something is alive, intelligent or sentient. Furthermore evolution has indeed shaped our brain into a purpose-built tool optimized to procreate.
We do! We can look at rocks and very clearly define them as non-sentient. If you disagree with that, you are not deferring to the overwhelming wealth of evidence that can prove rocks are not similar to us. Animists and panpsychics be damned, you only need thermodynamics to prove that we are not the same.
It's fine if you want to argue the uncertainty angle. I vehemently disagree with it though, and firmly believe that we can extend simple logic to conclude that LLMs are software, and software is not alive. Adding noise and 4.21gb of trained weights does not move the needle on that, for me.
"Similarity to us" is not a comprehensive metric for sentience, that's the point.
> firmly believe that we can extend simple logic to conclude that LLMs are software
Yes, LLMs are software, so what? Whatever sentience is, it's an integral part of our information processing, and software does information processing. There is literally no reason to conclude that software cannot do the type of information processing that our brain does for sentience.
Even if we agree that panpsychism is incorrect, I still do not agree that we know enough about sentience to claim that software cannot be sentient. I do not claim that LLMs are sentient, but they certainly could be, and the fact that they seem very intelligent, more intelligent than most humans in some ways, is a big red flag.
Unless you believe in some form of dualism, this reproduction necessarily includes sentience. Therefore, software can be sentient.
That's what panpsychists believe. Regardless, this is a silly comparison. Show me a rock that can carry an intelligent conversation and then you might have a point.
That aside, we should be close to certain that LLMs don't have internal lives. That computer programs can mimic human behavior in some contexts is a tremendous discovery but it's no reason to assume an internal life. Conversely, many animals can't mimic human behavior but clearly have internal lives.
The capacity for an internal life evolved over a billion odd years. It has something to do with our bodies and if we want to understand it, we should focus on biology and neuroscience. A machine with an internal life would need hardware designed or evolved for that purpose and modern computers lack that.
In any case, the behaviour I'm referring to is clearly a proxy for complex change. A rock does not undergo complex changes, so unless you're a panpsychist, it's a pretty straightforward deduction that a rock doesn't have internal life.
> but it's no reason to assume an internal life.
I'm not asserting they have an internal life, the person I responded to was saying they have no internal life and I said they cannot rigorously justify such a claim.
> I'm not asserting they have an internal life, the person I responded to was saying they have no internal life and I said they cannot rigorously justify such a claim.
The only claim that I can "rigorously justify" concerning internal lives is that I have one. And the proof is subjective. Clearly this is not a discussion that admits the kind of rigor you're demanding...
We have good reason to believe that people and animals have internal lives whereas rocks and computers don't. That we can't be certain of this isn't a reason to believe the opposite. If your goal here is to insist that all statements about internal lives (other than "I have an internal life") can't be "rigorously justified," fine, I agree.
Behaviour can't tell you there isn't internal life, but complex behaviour sure is a big red flag that there might be internal life there.
> We have good reason to believe that people and animals have internal lives whereas rocks and computers don't.
Rocks yes, computer programs no we don't, that's the point. There is no reason to suppose an information processing system that can converse intelligently doesn't have some kind of internal experience, and you have presented not a single argument suggesting that we can draw such a conclusion. I presented a simple deduction for rocks, what is the corresponding deduction for computer programs?
I agree, behavior is evidence, but there are good reasons to suppose that, despite their behavior, computers lack internal lives. To try to make my argument more clearly:
I know I have an internal life (due to subjective evidence). My internal life is related to the structure of my body in an unknown way. I'm willing to grant that other humans and similar, evolved organisms also have internal lives because they are the same kind of evolved thing as me.
If someone wants to argue that a designed, thoroughly understood object (like a computer) has an internal life, they need to explain how the structure of the object gives rise to an internal life. Behavior isn't enough.
That's right, an unknown way. So we know the internal structures of computers and their programs, but we don't know the internal structure of humans and how that leads to sentience, but somehow this translates into having good reasons for thinking the relationships in computers are not isomorphic to or meaningfully related to the unknown structures that lead to human sentience? Don't you see the big blank there? This just does not follow and is exactly the first point I made in this thread.
All I've been pointing out this is that people making strong statements about LLMs lacking sentience are leaping to conclusions.
It absolutely follows. The evidence for consciousness is subjective human experience so it makes sense to assume that other humans and living things have consciousness (because living things are all related to each other).
Meanwhile it doesn't make sense to assume that a computer running some program is conscious just because it mimics human conversation. It isn't evolved life and doesn't have the same kinds of structures as evolved life.
It would be unbelievably weird if we reverse engineered consciousness without understanding how our bodies generate consciousness.
You changed the deduction I described thus moving the goalposts. I'm not going to argue a point I never made. Suffice it to say, the only people assuming anything about LLMs are the ones claiming they're not sentient.
> It would be unbelievably weird if we reverse engineered consciousness without understanding how our bodies generate consciousness
I find it weird that you think this would be weird. Most discoveries are like this. We mastered fire before we understood chemistry, we mastered flight before we understood how birds fly, we have a booming pharmaceutical industry and we still don't really know how most drugs work. Theory and formal understanding often follow discovery and practical applications, not the other way around.
Consciousness and/or sentience will be created before we understand it, and we will only retrospectively recognize that fact. We may have already created it in fact.
Human exceptionalism is a slow dying myth.
In a sense, yes! But to understand that you will first have to precisely define "pain". Good luck.
I think the whole point the article is making, though, is that overvaluing LLMs and their supposed intelligence because they excel at this one axis of cognition (if you’re spicy) or mashed-up language ability (if you’re a skeptic) doesn’t make sense when you consider children, who are not remotely capable of what LLMs can do, but are clearly cognizant and sentient. The whole point is that those people—whether they can write an essay or not, whether they can use the word “consciousness” or not—are still fundamentally alive because we share a grounded, lived, multi-sensory, social reality.
And ChatGPT does not, not in the same way. Anything that it expresses now is only a mimicry of that. And if it eventually does have its own experiences through embodied AI, I’d be interested to see what it produces from its own “life” so to speak, but LLMs do not have that.
However, it's a bit annoying that the focus of the AI anxiety is how AI is replacing us and the resolution is that we embrace our humanity. Fair enough, but at least to me the main focus in my AI anxiety is that it will take my job - honestly don't really care about it doing my shitty art.
An enlightened Humanity could solve this by separating the income from the job, but we live in a Malthusian Darwinian world where growth is paramount, "enough" does not exist, and we all have to justify and earn our living.
I really like programming to fix things. Even if I weren’t paid for it, even if I were to win the lottery, I would want to write software that solved problems for people. It is a nice way to spend my days, and I love feeling useful when it works.
I would be very bummed - perhaps existentially so - if there were no practical reason ever to write software again.
And I know the same is true for many artists, writers, lawyers, and so on.
I'm not too concerned about that being a reality in our lifetimes though.
And I think about that a lot.
Right now, we all own "robots" who spellcheck our words (editor) , research (librarian), play music (musican), send messages (courier), etc.
All these jobs are "lost", but at the same time, we wouldn't have had money to pay these many employees to live in our pocket.
I think it was clear about how language is not thought.
That leads to the intrinsic realization that our physical existence, our observance of reality is what is critical.
Also, AI taking jobs at scale is unlikely, the only place its going to do anything is low level spam and content generation.
For anything which has to be factual, its going to need humans.
I guess my real point is that I don't view experience or consciousness as something special or exceptional. My guess is that over time we come to understand how the brain works better and better but never find anything that definitively explains consciousness because it's totally subjective and immeasurable. We eventually produce computers complex enough to behave totally convincingly human, and they will claim to be conscious, maybe even be granted personhood, but we'll never actually know for sure if they experience the world, just like we don't know that other humans do.
Personally these sorts of things don't really matter to me- I don't really care if other people are conscious, and I don't think I could prove it either way- I just assume other people are conscious, and that we can make computers that are conscious.
And that's exactly what I'm pushing for: ML that passes every Turing-style test that we can come up with. Because, as they say "if you can't tell, does it really matter?"
The concept of consciousness is wildly more expansive and diverse than computation, rather than the other way around. A strict materialist account or "explanation" of consciousness seems to just end up a category error.
I take it as no surprise that a website devoted to the computers and software often insists that this is the only way to look at it, but there are entire philosophical movements that have developed fascinating accounts of consciousness that are far from strict materialism, nor are they "spiritual" or religious which is a common rejoinder by materialists, an example is the implications of Ludwig Wittgenstein's work from Philosophical Investigations and his analysis of language. And even in neuroscience there is far from complete agreement on the topic at all.
if there is no duality, then what else can it be?
> there are entire philosophical movements that have developed fascinating accounts of consciousness that are far from strict materialism
philosophers can bloviate all day. meanwhile the computer nerds built a machine that can engage in creative conversation, pass physician and bar exams, create art, music and code.
I'm not saying there's nothing to learn from philosophy, but gee you have to admit that philosophers come up a little short wrt practical applications of their "Philosophical Investigations"
Now: a chance encounter with someone of a different faith leads a citizen to respect the religious freedom of others in the realm of self-determination.
Future: a young hacker's formative experience leads to the idea that citizens should have the basic right to change out their device's recommendation engine with a random number generator at will.
Those future humans will still think of themselves as exceptional because the AI tools will have developed right alongside the current human-exceptionalist ideology.
Kinda like those old conservative couples I see in the South where the man is ostensibly the story teller and head of household. But if you listen long enough you notice his wife is whispering nearly every detail of importance to help him maintain coherence.
You've never actually seen this happen because it doesn't happen. This is not how real people interact, it's how the caricatures in your head interact.
But then I have no idea how she goes on to conclude:
> Human obsolescence is not here and never can be.
There's no fundamental reason why you can't put ChatGPT in the real world and give it a real life. The only things that probably will never be replaced by machines are things where our physical technology is likely never going to match biology - i.e. sex.
This said, there's an important difference between LLMs and humans: Humans have instincts. The most important ones seem to be the set of instincts concerning an immaterial will for life and a readiness to overcome deadly obstacles. In other words: LLMs don't have the primitive experience of what is life.
This might change in future. A startup might put neural networks into soft robots and then in a survival situation. Robots that don't emerge functioning are wiped, repaired and put back. In other words, they establish an evolutional situation. After careful enough iterations of curation they have things that "understand" life or at least better than current LLM instantiations.
EDIT: typo
To make LLM's more similar to humans, we'd need to hardwire them with a concept of 'self', and, importantly, the hardwired idea that their self was unique, special, and should survive. But I think the result would be terrible. Imagine a LLM that would be begging not to be switched off, or worse, trying to subtly manipulate its creators.
For a better explanation and responses to objections, consider the National Library of Thailand thought experiment: https://medium.com/@emilymenonbender/thought-experiment-in-t...
She quickly generalized; henceforth, both of them were "catten".
The fact that it's very unlikely for any of the current models to create something that even remotely resembles this article tells me we are very far away from AGI.
Every time I say to myself "AI is no big deal because it can't do X", some time later someone comes along and makes an AI that does X.
Over time, as the gaps in human knowledge get filled, the god "shrinks" - it becomes less expansive, less powerful, less directly involved in human affairs. The definition changes.
It's fascinating to watch the same thing happen with human exceptionalism – so many cries of "but AI can't do <thing that's rapidly approaching>". It's "human of the gaps", and those gaps are rapidly closing.
Every time computers outperform humans in some domain this is conflated with deep general progress, panicked projections of replacement and job losses, the end times and so on. People are way faster to engage in reductionism of human capacity and Terminator-esque fantasies than the opposite. Despite the fact that it never happens.
It's even reflected in our popular culture. I can barely recall a single work of science fiction in the last 30 years that would qualify as portraying human exceptionalism. AI doomerism, overestimation and fear of machines is the norm.
“but AI can’t write poetry”
“but AI can’t do real work”
Panic is probably not warranted. Too much incentive stacked against the truly apocalyptic scenarios. But yeah, a lot of jobs are probably going to shrink.
Say instead (for example) "It is important that I understand the differences between both the capabilities and internal mechanisms of AI and people, even if, over some period of time, the capabilities may appear to converge".
wget https://bellard.org/pi/pi2700e9/tpi-0.9-linux.tar.gz
tar xvf tpi-0.9-linux.tar.gz
time ./tpi -T 8 -o pi.txt 1M
real 0m0.176s
user 0m0.500s
sys 0m0.049sSimilarly LLMs can do things humans can't. No human has as much knowledge about the world as a typical LLM model. However, an LLM model can't generalize outside it's training set well (recent Deep Mind research). LLM doesn't have a memory outside it's input. LLM isn't Turing complete (has fixed amount of steps and always terminates).
We are building airplanes, not birds. That much is obvious.
It does to me as well, but reading the comments here, it doesn't seem that obvious to many people.
We already have an instance of emergent logic. Animals engage in logical reasoning. Corollary, humans and toddlers are not merely super-autocompletes or stochastic parrots.
It has nothing to do with "sensory embodiment" and/or "personal agency" arguments like in the article. Nor the clever solipsism and reductivism of "my mind is just random statistics of neural firing". It's about finding out what the models of computation actually are.
OK, time to train up our LLM.
Oops. Printing not yet invented. Books are scarce, and frequently not copied. We have almost no input data to give to our analog LLM.
Will it be of any use whatsoever? I think we can say with a high degree of confidence that it will not.
However, contrast a somewhat typical human from the same era. Will they suffer from any fundamental limitation compared to someone alive today? Will there be skills they cannot acquire? Languages they cannot learn? It's not quite so easy to be completely confident about the answer to this, but I think there's a strong case for "no".
The data dependence of LLMs compared to human (or ravens) makes a compelling argument that they are not operating in the same way (or, if they are, they have somehow reduced the data dependency in an incredibly dramatic way, such that it may still be reasonable to differentiate the mechanisms).
But the crux is that both kinds of "training" - almost a loaded term by now - are open areas of scientific research.
If one computational model accepts only assembly code, and another model accepts only C++, it tells us nothing about what is going on inside their black boxes. They could be doing computationally fundamentally equivalent things, or totally different things. We don't know till we look.
My point was really about neural networks in general. The argument is that neural networks include both humans and synthetic ones. The myopic focus on LLMs (because of mainstream media coverage) as they exist now is what limits such arguments.
Like so the Earth is alive and the universe would not be complete without you. Rest easy, little bird. One day you will spread the wings of your soul and the wind will do the rest.
That's a definitional argument, there is very little actual meaning there. The argument can't discriminate between a baby and a nuclear explosion destroying Manhattan.
This defines a transcendence of mortal concerns and ascending to eternity so it doesn't need to treat a baby and a bomb in a city differently. When we attempt to find peace with AI this is qn appropriate universe of discourse.
In fact I've observed (after working with a number of world-class ML researchers) that having children is the one thing that convinces ML people that learning is both much easier and much harder than what they do in computers.
Phonological development: https://en.wikipedia.org/wiki/Phonological_development
Imitation > Child development: https://en.wikipedia.org/wiki/Imitation#Child_development
https://news.ycombinator.com/item?id=33800104 :
> "The Everyday Parenting Toolkit: The Kazdin Method for Easy, Step-by-Step, Lasting Change for You and Your Child" https://www.google.com/search?kgmid=/g/11h7dr5mm6&hl=en-US&q...
> "Everyday Parenting: The ABCs of Child Rearing" (Kazdin, Yale,) https://www.coursera.org/learn/everyday-parenting
> Re: Effective praise and Validating parenting [and parroting]
I've got:
- Falsifiability - am I saying something false? Some products have an output checker, but it's a bolt on at this time for unacceptable output, which is not the same thing as falsifying.
- Agency - much ink already spilled
- Context Window size (too small, or need to summarize parts of the context to fit in context window, and know where to go back to for full context)
- Directed Self-Learning (I'm sure there are things like this that people may point out, but some kernel of self-learning isn't there yet)
Look: when a factory is outsourced to a different country, the people who worked there have no way to pass on their skills. In one generation the community knowledge is gone and if you move the factory back, you’re starting from scratch. Technology can be lost. It’s an emergent thing that comes from clusters of people doing the same thing and learning from each other.
If we stay on the path we are on: only hobbyists will be able to do these things, human effort will be unnecessary, and our minds will degrade and cease to be any use. Despite decades of Hollywood warning us of terminators and Hal 2000, the ai’s don’t need to attack us or try to kill us off to defeat us. They can destroy us just by trying to help.
A bit of an exaggeration. The paper reads like a long blog post to me.
That's an interesting insight that seems true for now. Are there attempts to create LLMs that "have a life"?
Some ~72 years ago in 1951, Claude Shannon released his "Prediction and Entropy of Printed English", an extremely fascinating read now.
The paper begins with a game. Claude pulls a book down from the shelf, concealing the title in the process. After selecting a passage at random, he challenges his wife, Mary to guess its contents letter by letter. The space between words will count as a twenty-seventh symbol in the set. If Mary fails to guess a letter correctly, Claude promises to supply the right one so that the game can continue.
In some cases, a corrected mistake allows her to fill in the remainder of the word; elsewhere a few letters unlock a phrase. All in all, she guesses 89 of 129 possible letters correctly—69 percent accuracy.
Discovery 1: It illustrated, in the first place, that a proficient speaker of a language possesses an “enormous” but implicit knowledge of the statistics of that language. Shannon would have us see that we make similar calculations regularly in everyday life—such as when we “fill in missing or incorrect letters in proof-reading” or “complete an unfinished phrase in conversation.” As we speak, read, and write, we are regularly engaged in predication games.
Discovery 2: Perhaps the most striking of all, Claude argues that that a complete text and the subsequent “reduced text” consisting of letters and dashes “actually…contain the same information” under certain conditions. How?? (Surely, the first line contains more information!).The answer depends on the peculiar notion about information that Shannon had hatched in his 1948 paper “A Mathematical Theory of Communication” (hereafter “MTC”), the founding charter of information theory.
He argues that transfer of a message's components, rather than its "meaning", should be the focus for the engineer. You ought to be agnostic about a message’s “meaning” (or “semantic aspects”). The message could be nonsense, and the engineer’s problem—to transfer its components faithfully—would be the same.
a highly predictable message contains less information than an unpredictable one. More information is at stake in (“villapleach, vollapluck”) than in (“Twinkle, twinkle”).
Does "Flinkle, fli- - - -" really contain less information than "Flinkle, flinkle" ?
Shannon concludes then that the complete text and the "reduced text" are equivalent in information content under certain conditions because predictable letters become redundant in information transfer.
Fueled by this, Claude then proposes an illuminating thought experiment: Imagine that Mary has a truly identical twin (call her “Martha”). If we supply Martha with the “reduced text,” she should be able to recreate the entirety of Chandler’s passage, since she possesses the same statistical knowledge of English as Mary. Martha would make Mary’s guesses in reverse.
Of course, Shannon admitted, there are no “mathematically identical twins” to be found, but and here's the reveal, “we do have mathematically identical computing machines.”
Those machines could be given a model for making informed predictions about letters, words, maybe larger phrases and messages. In one fell swoop, Shannon had demonstrated that language use has a statistical side, that languages are, in turn, predictable, and that computers too can play the prediction game.
I don't personally follow her all the way to some of those conclusions but the delivery was awesome
"""I believe that when the last ding-dong of doom has clanged and faded from the last worthless rock hanging tideless in the last red and dying evening, that even then there will still be one more sound: that of man's puny, inexhaustible, voice still talking! ...not simply because man alone among creatures has an inexhaustible voice, but because man has a soul, a spirit capable of compassion, sacrifice and endurance."""
This is partially reflected by later observations from training LLMs where we see that the performance of LLMs increases even on purely language tasks when adding extra modalities such as computer code or images, which, in a sense, bring the model closer to different aspects of reality; and we observe that adding tiny quantities of "experimental interaction" through RLHF can bring features that additional humongous amounts of pure surface form training data can't, and it certainly seems plausible that making a qualitative leap further would require some data from actual causal interaction with the real world (i.e. not replay of data based on "someone else's" actions but feedback from whatever action the model currently feels is the most "interesting" i.e. the outcome is uncertain to the model but with potential for surprise data), where relatively tiny amounts of such data can enable learning what large amounts of pure observations can't - just as the hypothetical octopus from the stochastic parrot paper thought experiment.
Beleive it or not, reality exists outside of anything you ever read in a whitepaper...
Imagine the contribution to GNP if we in practice, like Bokanovsky* (by Ford!) in fiction, could greatly reduce the 10-30 year lead time currently required to produce new employees...
Edit: * cf https://www.depauw.edu/sfs/notes/notes47/notes47.html
I firmly believe that LLMs are stochastic parrots and also that humans are too. To the point where I actually think even consciousness itself is a next-token predictor.
Where the industry is headed - multi-modal models. This really I think is the remaining frontier of LLM <> Human parity.
I also have a 15 month old son. It's totally obvious to me that he's definitely learning by repetition. But the sources of training data is much more high bandwidth than whatever we're training our LLMs on.
It's been a couple of years since GPT-3. It's time to abandon this notion of "stochastic parrot" as a derogatory. Anyone stuck in this mindset really is going to be hindered from making significant progress in developing utility from AI.
Almost every time I'm on hackernews I end up baffled by software engineers feeling entitled to have an unfunded opinion on scientific disciplines outside of their own field of expertise. I've literally never encountered that level of hubris from anyone else. It's always the software people!
Consciousness is far from being fully understood but having a body and sensorimotor interactions with the environment are already established as fundamental preconditions for cognition and in turn consciousness.
Margaret Wilsons paper from 2002 is a good read: https://link.springer.com/content/pdf/10.3758/BF03196322.pdf
peace
You'd think that software engineers would be a group that easily understands how making radical assumptions about implementation details when looking at nothing but an interface is generally misguided.
I'm not saying that there isn't a strong case to be made against LLMs being intelligent. It's pointing at the stochastic parrot as evidence enough in of itself that confuses me.
As a stochastic parrot I'm unable to do that.
Second of all, it wouldn't matter if individually they did or did not. The species does and now our species has developed consciousness. It's part of the package.
If you wanted a counterexample, you should look to plant life. There is some discussion on whether or not plant systems have a form on consciousness. But, then again, plants have bodies at the very least.
Does this make sense as a rebuttal to your reductio argument?
I don't think ie. Stephen Hawking was on "lower spectrum of consciousness" either.
But... a parrot has a body. And sure, you'll say "they don't literally mean parrot".. but it's a vague term when you unpack it, and people saying "we are stochastic parrots" are also making a pretty vague comment (they clearly don't mean literally). Anyone who has a small child and understands LLMs is shocked by how much similar they seem to be when it comes to producing output.
Instead, I work with the following idea: it seems not unlikely that we will, in the next decade or so, create non-embodied machine learning models which simply can't be told apart from a human (through a video chat-like interface). If you can do that, who really cares about whether it's conscious or not?
I don't really think philosophy of the mind is that important here; instead, we should treat this as an engineering problem where we assume brains are subjectively conscious, but that's not a metric we are aiming for.
There's an XKCD about this behavior[0]. The title is actually "Physicists", but I also have seen it on HN (especially with psychology).
Our sensory experience is the medium by which we learn about the external world. We learn of apples not because of the redness of the sensory experience, but because the pattern of red/not-red experience entails the shape of apples. Conscious experience provides the medium, modulations of which provide the information about features of the external world. It is analogous to how modulations of electromagnetic waves provides information about some distant information source. Understanding consciousness is an orthogonal matter to one's situatedness in the world, just like understanding electromagnetic waves is orthogonal to understanding the information source being modulated into them.
Does someone who is blind is not conscious then? How about when someone who is paralysed? or deaf? or someone with low IQ or mental illness?
Stephen Hawking was paralysed most of his life but was a smart guy. If he was not only paralysed but also blind and deaf he would be smart guy still.
I don't think AGI needs to have a body, sensorimotor interaction or even vision to be conscious. We need those for training - if you would be blind, paralysed, deaf from the beginning it would be hard for you learn anything and interact in any way.
Machines have 6th sense that humans don't have - kind of a telepathy where they can exchange tokens/thoughts with different machines or humans much faster than we humans can type or speak.
If we were attempting to put someone into some sort of Matrix like reality simulator but we lacked the technology to provide a perfect simulation what level of simulation would be 'good enough' that a human would consider it reality and be able to develop into something we could relate to?
If you gave someone the Helen Keller level of experience, but with reduced tactile sensation, how much could you reduce that touch sensation before they wouldn't be like us?
Have you tried VR before? You really don't need perfect simulation to be fooled. Good enough is already here, albeit for a short amount of time.
> There is a movement afoot in cognitive science to grant the body a central role in shaping the mind.
It is far from true to say "having a body and sensorimotor interactions with the environment are already established as fundamental preconditions for cognition and in turn consciousness"
It is a popular idea in some groups of people that study these questions. But there are many other similar groups of peopel studying these questions who do not agree with it, certainly not stated as strongly as you have put it here.
Also, a reminder that HN readership and its commentariat, while dominated by SWE, is not limited to them.
Like it somehow diminishes us. Reduces us to cogs and levers and such.
But I can't imagine how it could be otherwise, though. I'm still baffled by the existence of qualia, phenomenology, etc. Awareness. But bafflement on that front isn't a good reason to reject the possibility that the only thing that separates me from a computer is the level of complexity. Or the structure of the computation. Sometimes things are just weird.
I think you mean non-deterministic. The last century of physics was dominated by work showing how deterministic systems emerged from non-deterministic foundations. It seems that probability and statistics were the branches of maths behind everything. Who would have thought.
Of course there is randomness as well. So, yeah, I should clarify: We don't impose upon the world any kind of "uncaused cause", even if it feels like we do. Everything we think and do is a direct result of some other action. Sometimes that action can trace its lineage to a random particle decay. (Maybe—ultimately—all of them can?) Maybe we even have a source of True Randomness inherent in our minds. But even so, that doesn't lend any support to the common notion of our minds and consciousnesses as being somehow separate from the physical world or the chains of information that run through everything.
Yes, of course deep down there's unimaginable numbers of atoms randomly bumping around. But so far everything suggests that biological organisms do their damnedest to abstract that away and operate on a deterministic basis.
Think like how you keep changing -- gaining and losing cells, changing chemical balance, and on the whole it takes a lot of damage, even brain damage, to produce measurable results.
The only "real" evidence of qualia you have is your own running stream of it; you can try to carefully pour yourself, like a jug of water, into the body of another, and if you go carefully enough you may even succeed to carry that qualia-stream with you. But it's also possible that you pour too quickly, or that you get bumped in the middle of the pouring, and poof - the qualia is just gone. You're left with a p-zombie.
Or maybe not. Maybe it comes right back as soon as the transfer is done, like the final piece of a torrented movie. The important part is you won't know unless you try - and past success never guarantees future success. Maybe you just got lucky on the first pour.
For example, it is possible to write down a (natural/whole) number that completely describes the state of an LLM - its connections and weights etc - for example by simply taking the memory space and converting into a number as a very long sequence of 1s and 0s. The number will be very big, but still indescribably smaller than a number that could perfectly describe the state of a human brain - not least as that number is likely to lie on the real line, or beyond, even if it was calculable. See Cantor for a discussion on the various infinities and their respective cardinalities.
( See the relationship between information theory and Heisenberg's uncertainty principle that results in the law of paraconservation of information : http://www.av8n.com/physics/thermo/entropy-more.html#sec-pha... )
Humans definitely create new information. (Well, at least some humans do.)
Humans can observe new information, but that's obviously not that unique. We can reason about existing information, creating new hypotheses, but that is arguably a compression of existing information. When we act on them and observe the effects they become information, but that's not us creating the information (and LLMs can both act on the environment and have the effects fed back to their input to observe, so it's not really unique).
There is this whole field of art, but art is constantly going through a mental crisis whether anyone is creating anything new, or if it's all just derivations of what has come before. Same with dreams, which appear "novel" but might just be an artefact of our brain's process that compresses the experiences of the day.
More information: https://www.nature.com/articles/s41586-023-06600-9
you organize them into piles of ten
this created new information
LLM's are basically lossy compressors, they decrease information entropy.
Humans increase information entropy whenever we do anything.
(Some) information entropy is potentially very valuable, so in effect LLMs destroy value by design, while humans can potentially create value. From an economic point of view humans can never be replaced by an LLM.
Let's say we could somehow train an LLM on all written and spoken language from the western Roman civilization (Republic + Western Empire, up until 476 AD/CE, just so I don't muddy the experiment with near-modern timelines). Would it, without novel information from humans, ever be able to spit out a correct predecessor of modern science like atomic theory? What about steam power, would that be feasible since Romans were toying with it? How far back do we have to go on the tech tree for such an LLM be able to "discover" something novel or generate useful new information?
My thought is that the LLM would forever be "stuck" in the knowledge of the era it was trained in. Something in the complexity of human brains working together is what drives new information. We can continue training new LLMs with new information, and LLMs might be able to find new patterns in data that humans can't see and can augment our work, but the LLM's capability for novelty is stuck on a complexity treadmill, rooted in its training data.
I don't view this ability of humans as some magic consciousness, just a system so complex to us right now that we can't fully understand or re-create it. If we're stochastic parrots, we seem to be ones that are magnitudes more powerful and unpredictable than current LLMs, and maybe even constructed in a way that our current technology path can't hope to replicate.
If you took millions of genius-level immortal humans with all the same Roman data but had them sit in a blank, empty room with their hands tied and simply discuss philosophy for eternity, I'm certain that they would not be able ever spit out a correct predecessor of modern science like atomic theory. Perhaps they could spit out billions of theories including the atomic theory as well, but they would have no data to presume that the atomic theory is more relevant than any other. Extensive information processing can squeeze out every last ounce of knowledge from some data, but anything that isn't in that data can't be acquired by mere thinking about it. On the other hand, if you gave some "LLM++" the ability to toy around with reality and attempt all kinds of experiments to test various hypotheses, then I wouldn't assume that it would not be forever stuck in the knowledge of the era it was trained in.
Funny example - depending on how close the predecessor has to be, the answer is maybe: https://en.wikipedia.org/wiki/Atomism
I’m currently reading I Am A Strange Loop, a pretty extensive dive into the nature of consciousness. I’m reserving final judgment on how much I agree with the author, but I find it laughable to claim consciousness itself is on the same level as an LLM.
I agree with the first sentence but not with the second one. Consciousness most probably does not arise from just a next-token predictor. At least not from an architecture similar to current LLMs.
Both humans and LLMs basically learn to predict what happens next. However, LLMs only predict when we ask them. In contrast, humans predict something all the time. Even when we don't have any sensory input, our brain plays scenarios. Maybe consciousness arises because the result of our thinking is fed back as input. In that sense, we simulate a world that includes us acting and communicating in that world.
Also noteworthy, the human brain handles a variety of sensory information and it's output is not only language. LLMs are restricted to only language. But to me it seems like it's enough for consciousness if we can give it the self-referential property.
Pretty easy to do this exercise with an LLM. At least, easier than building an LLM in the first place. Leave it running, let it talk to itself, revisit partial memories, and explore noise. Really a duct-tape problem here more than anything.
I think this specific line shouts out that this is a typical tribalism comment. Once people identify themselves as a part of something, they start to translate the value of that something as their own worth. It's a cheap trick that even young kids play, but can LLM do this? No.
Some might say multi-modal this, train on that-thing, but it already takes tens of thousands of the most advanced hardware and gigawatts of energy to push around numbers to reach where it is. TBH, I don't see it going anywhere, considering ROI on research will decrease as we dig deeper into the same paradigm.
What I want to say is that today's LLM is certainly not the last stop of AI technology, but a lot of advocates tend to consider it as the final form of intelligence. It's certainly a case of extrapolation, and I don't think LLM can do that.
Everyone is basically talking out their ass when it comes to language and linguistics. That becomes incredibly obvious listening to Chomsky on chatGPT.
I was even so stupid to think Chomsky wasn't a fan of chatGPT because it somehow invalidated some of his language theories. Low and behold, no, Chomsky actually knows what he is talking about when it comes to linguistics.
The AIs will be creating masterwork art and literature personalized for each person better than anything Shakespeare ever wrote or Michaelangelo ever sculpted, but we'll console ourselves that at least we're _really_ conscious and they're just stochastic parrots.
I don't think it will. Art is truth and AI is a golem replicating humanity.