Communicate the concept of "green" to me in text.
The sound of a dog barking, a motor turning over, a sonic boom, or the experience of a Doppler shift. Beethoven's symphony.
Sour. Sweet. What does "mint" taste like? Shame. Merit. Learn facial and object recognition via text.
Vertigo.
Tell a boxer how to box by reading?
Hand eye coordination, bodies in 3 dimensional space.
Look, I love text, maybe even more than yourself. But all these things imbibe, structure and influence or text, but are not contained in them.
To make substantial inroads to something that looks like human esque AI, text is not enough. The division of these fields are artificial and based on our current limited tech and the specialisation of our researchers, faculties and limitations.
When we read, we play back memories, visions, sounds, feelings, etc, and inherent ideas gained through experience of ourselves as physical bodies in space.
Strong AI, at least to be vaguely recognised as such, must work with algorithms and machinery that understand these things, but which then works at that next level of abstraction to combine them into proper human type concepts.
Of course, there is the question about why we would want to create a human like AI, it's my contention that human like AI isn't actually what many of us would want, but that's another topic...
I won't touch the qualia aspect, but everything necessary to flawlessly pretend to understand the color green, the sound of a dog's bark, or the experience of hearing a sonic boom can be represented with text. As an existence proof, you could encode an adult human as a serial stream of characters.
But if you must pretend to be sighted and hearing, there are many descriptions of green, of dogs barking, of motors, etc, scattered through the many books written in English (and other languages.)
Are these descriptions perfect? Maybe not. But they are sufficient to mimic or communicate with humans through text. It's sufficient to beat a Turing test, to answer questions intelligently, to write books and novels, and political arguments, etc. If that's not AGI, I don't know what is.
I don't propose that a human could lose all of their senses and still be able to communicate. But I do believe computers could do so, if they are designed to do that. Humans are not designed to work lacking those senses.
Now we are just speculating. We believe a computer might be able to understand things for which it doesn't have the sense - but that is speculation and totally untested, and certainly can no longer be justified by using human minds as an example.
That seems hard to believe.
Even if this were a true statement, it's still the case that it might not be enough. There is a class of functions that are simply not learnable without some prerequisite knowledge. This is directly analogous to a one-time pad in crypto. It is entirely possible that the function 'language' is in this class of unlearnable functions. While it may be the case that certain varieties of intelligence are learnable tabula rasa from a powerful neural net, the surface form of human natural language (the part your recommending measuring) may simply not have enough information in it to decode the whole picture. It is entirely possible that you need to supply some of your own information as well to the picture, in a specific manner so as to act as a kind of decryption key. A record needs a record player, even if you can make similar sounds with cassettes and CDs.
And so, I'm willing to bet that you simply cannot, using raw, uninformed statistical techniques, predict what word a human would say next. You need to understand more of the underlying structure of humans first.
I will agree, however, that the success towards the Hutter Prize is a valuable demonstration of AI progress. Simply because I believe that maximal compression and the kind of intelligence I'm talking about are one and the same thing. You need to offload as much of the semantic weight of the corpus into the encryption algorithm as you can. That means building a very complex model of natural language. And if you accept the premise that this model is not simply learnable by observing the surface form, then that means building Strong AI
I don't understand how this could possibly be the case. We can already make great progress on language understanding with simple methods like word2vec, or perhaps even markov chains. There are tons of statistical patterns in text that can be learned by computers.
If it turns out that the easiest, or even only means of doing this is by emulating the human brain, then it is entirely possible that we inherit a whole new set of constraints and dependencies such that world-simulation and an emobdied mind are required to make such a system learn. If this turns out not to be the case, that there's some underlying principle of language we can emulate (the classic "airplanes don't fly like birds" argument) then it may be the case that text is enough. But that's in the presence of a new assumption, that our system came pre-equipped to learn language, and didn't manufacture an understanding from whole cloth. That the model weights were pre-initialized to specific values.
I don't think it's so weird to imagine that natural language really doesn't convey a ton of explicit information on its own. Sure, there's some there, enough that our current AI attempts can solve little corners of the bigger problem. But is it so strange to imagine that the machinery of the human brain takes lossy, low-information language and expands, extrapolates, and interprets it so heavily so as to make it orders of magnitude more complex than the lossy, narrow channel through which it was conveyed? That the only reason we're capable of learning language and understanding eachother (the times we _do_ understand eachother) is because we all come pre-equipped with the same decryption hardware?
1) They appear to have crafted the skeleton of a grammar as it is with their nodes, super nodes, and slot collocations. This is directly analogous to something like an Xbar grammar, and is not learned by the system; therefore, if anything, it's strengthening a Chomskian position; the system is learning how a certain set of signals satisfy its extant constraints.
2) The don't appear to go beyond generative grammar, which already seems largely solvable by other ML methods, and is a subset of the problem "language". Correct me if I'm wrong here, it's a very long paper and I may have missed something.
There's also the related phenomenon of "subtext" -- the idea that some language has a different meaning than what's said. For instance, when I ask about whether a signature line on a form is required, and the other person says, "Yes, it's required. However you think best to get the signature." There's a subtext there of, "This signature won't actually be checked, so don't worry about it."
Of course we have no idea how the Voynich manuscript is encrypted (which would make the assumptions of word2vec wrong), or if it even has any meaning at all. And it's an incredibly small dataset compared to modern text corpuses, so there is probably significant uncertainty and overfitting. And other problems like inconsistent spellings, many errors in transcriptions, etc. But in principle this is a good strategy.
>how would a person do if they were locked in a room with lots of books written in a language unknown to them?
If you spent all day reading them, for years, and you somehow didn't get bored and kept at it, eventually you would start to see the patterns. You would learn how "slithy toves" are related to "brillig", even if you have no idea how that would translate to English. Study it long enough, and you may even be able to produce text in that language, indistinguishable from the real text. You may be able to predict the next word in a sentence, and identify mistakes, etc. Perhaps carry out a conversation in that language.
And I think eventually you would understand what the words mean, by comparing the patterns to those found in English. Once you have guesses for translations of just a few words, you can translate the rest. Because you know the relationships between words, and so knowing one word constrains the possibilities of what the other words can be.
If the translation it produces is nonsense, the words you guessed must have been wrong, and you can try again with other words. Eventually you will find a translation that isn't nonsense, and there you go. This would be very difficult for humans, because the number of hypotheses to test is so large, and analyzing text takes forever. Computers can do it at lightspeed though.
More generally, has any attempt been made to identify the meanings of words in any sufficiently large corpus of text in a known foreign language (for example, Finnish), without being provided with a translation into English, and then compare the identified meanings to the actual meanings, as a first step towards translation?
Doing this without any translated words at all, would be more difficult. But I believe possible. It's actually a project I want to try in the near future.
This statement is false. There is a well known thought experiment called Mary’s Room the gist of which is that knowing all conceivable scientific knowledge about how humans perceive color is still not a substitute for being a human and perceiving the color red: https://philosophynow.org/issues/99/What_Did_Mary_Know
The experience of seeing red is an example of what is called “qualia”.
In Google AI systems that identify cats, birds, etc it is reasonable to imagine AI technology evolving towards systems that can discuss those objects at the level of a typical person. However with an AI based on text only there is no possibility of that. It would be like discussing color with a blind person or sound with a deaf person.
In any case, at some level everything is symbols. A video is just a bunch of 1's and 0's, as is text, and everything else. A being raised on only text input would have qualia just like a being raised on video input. It would just be different qualia.
It may sure look indistinguishable, but on the inside it just wouldn't be the same.
If you assert that a person can understand everything there is to know about the color red and then still not understand what it is like to see red, you have either contradicted yourself or assumed dualism.
Also, it's a thought experiment. Some people will claim the answer to that question is no, she learned nothing. Others will claim that she did. It's that thing she learned beyond the physical that theoretically cannot be conveyed by science, or even possibly by language.
Word2vec is definitely an impressive algorithm. But at the end of the day, it's just a tool that cranks out a fine-grained clustering of words based on (a proxy measure for) contextual similarity (or rather: an embedding in a high-dimensional space, which implicitly allows the words to be more easily clustered). And yes, some additive relations between words, when the signal is strong enough.
But to say that it's "learning" the "meaning" of these words is really quite a stretch.
I remember that when I first read A Clockwork Orange, it took me a while but I finally started to understand the meanings of those words/phrases (though I may not have every encountered them before.) It did feel like my brain was re-wiring itself to a new language. It'd be interesting to see how some type of language AI would treat these works.
https://en.wikipedia.org/wiki/Jabberwocky
edited to add there's a wiki article on the language of A Clockwork Orange, Nadsat: https://en.wikipedia.org/wiki/Nadsat
Gender was just an example. There are lots of semantic information learned by word2vec, and the vectors have shown to be useful in text classification and other uses. It can learn subtle stuff, like the relationship between countries, celebrities, etc. All that information is contained in a few hundred dimensions, which is tiny compared to the neurons in the brain.
You say, as many people do, that the operation "king - man + woman = queen" indicates that it understands that the relation between "king" and "queen" is the relation between "man" and "woman". But there is something much simpler going on.
What you're asking it for, in particular, is to find the vector represented by "king - man + woman", and find the vector Q with the highest dot product with to this synthetic vector, out of a restricted vocabulary.
The dot product is distributive, so distribute it: you want to find the maximum value of (king * Q) - (man * Q) + (woman * Q).
So you want to find a vector that is like "king" and "woman", and not like "man", and is part of the extremely limited vocabulary that you use for the traditional word2vec analogy evaluation, but not one of the three words you used in the question. (All of these constraints are relevant.) Big surprise, the word that fits the bill is "queen".
(I am not the first to do this analysis, but I've heard it from enough people that I don't know who to credit.)
It's cool that you can use sums of similarities between words to get the right answer to some selected analogy problems. Really, it is. It's a great thing to show to people who wouldn't otherwise understand why we care so much about similarities between words.
But this is not the same thing as "solving analogies" or "understanding relationships". It's a trick where you make a system so good at recognizing similarities that it doesn't have to solve analogies or understand relationships to solve the very easy analogy questions we give it.
There's also the relationship it has with the world.
If literally the only thing you know is the relationship between words, but you have a perfect knowledge of the relationship between words, you'll quickly determine that "Day" and "Night" are both acceptable answers, and have no means of determining which is the right one. At the very minimum, you need a clock, and an understanding of the temporal nature of your training set, to get the right one.
What do you see? What do you smell? What do you hear? What does the landscape look like? What memory does this bring up?
These are messages that the language is communicating. If an AI can't understand at least some of the content of the message then can it compose one effectively? I'm not certain it can understand the meaning from words alone, but we can certainly try.
And then there is the fundamentally dynamic aspect of language, which strengthens the need for a rich understanding of the world that words describe and convey.
That's a bit overstated. I think maybe you mean to say "all of the information you'd need to be an effective citizen of our world is contained in text"? Or something similar? I think even that is too strong a claim, but it's at least understandable.
As stated the assertion doesn't make any sense. There is more information in a glass of milk than could be stored on all of the computers on earth, and Heisenberg showed that it's impossible to even record all of the information about a single particle.
Certainly there is no textual information available about my grandfather's eyes, but that information is accessible in the world for those who'll look. You seem to be underestimating the quantity of information you absorbed as a baby, just by reacting robotically to the people and events around you and absorbing the relationships between percepts and feelings.
If the much-vaunted "general intelligence" consists in both vague and precise causal reasoning and optimal control with respect to objects in the real world, then no, it obviously cannot be done with mere language. At least one sensor and one effector will be needed to train an ML/AI model to perform those tasks.
The internet is so large and so comprehensive (especially if you include digitized books and papers, e.g. libgen or google books) that I doubt any important information that can be learned through video data, can't be obtained through text data.
Uhhhh... there's this thing called entropy. A corpus of video data is vastly higher in entropy content than text data, and probably a good deal more tightly correlated too, making it much easier to learn from.
Remember, for a human, the whole point of speech and text is to function as a robust, efficient code (in the information-theoretic sense) for activating generative models we already have in our heads when we acquire the words. This is why we have such a hard time with very abstract concepts like mathematical ones: the causal-role concepts (easy to theorize how to encode those as generative models) are difficult to acquire from nothing but the usage statistics of the words and symbols, in contrast to "concrete", sense-grounded concepts, which have large amounts of high-dimensional data to fuel the Blessing of Abstraction.
Nevermind, I should probably just get someone to let me into a PhD program so I can publish this stuff. If only they'd consider the first paper novel enough already!
I don't know if humans have generative models in their heads. There are people who have no ability to form mental images, and they function fine. Regardless, an AI should be able to get around that by learning our common patterns. It need not mimic our internal states, only our external behavior.
No, I meant a pair of specific information-theoretical quantities I've been studying.
>And yes videos are highly redundant. But I don't see how that's any kind of advantage.
Representations are easier to learn for highly-correlated data. Paper forthcoming, but conceptually so obvious that quantifying it is (apparently) non-novel.
>I don't know if humans have generative models in their heads.
The best available neuroscience and computational cognitive science says we do.
>Regardless, an AI should be able to get around that by learning our common patterns. It need not mimic our internal states, only our external behavior.
Our external behavior is determined by the internal states, insofar as those internal states are functions which map sensory (including proprioceptive and interoceptive) statistics to distributions over actions. If you want your robots to function in society, at least well enough to take it over and kill everyone, they need a good sense of context and good representations for structured information, behavior, and goals. Further, most of the context to our external behavior is nonverbal. You know how it's difficult to detect sarcasm over the internet? That's because you're trying to serialize a tightly correlated high-dimensional data-stream into a much lower-dimensional representation, and losing some of the variance (thus, some of the information content) along the way. Humans know enough about natural speech that we can usually, mostly reconstruct the intended meaning, but even then, we've had to develop a separate art of good writing to make our written representations conducive to easy reconstruction of actual speech.
Deep learning can't do this stuff right now. Objectively speaking, it's kinda primitive, actually. OTOH, realizing the implications of our best theories about neuroscience and information theory as good computational theories for cognitive science and ML/AI is going to take a while!
I know what you are saying, but I don't think it's true.
Imagine a hypothetical language that is so compressed, so non-redundant, so little correlated, that it's indistinguishable from random noise. Learning this language may seem an impossible task. But in fact it's very easy to produce text in this language. Just produce random noise! As stated, that's indistinguishable from real text in this language.
Real language, of course, has tons of statistical patterns, and is definitely not random. But I don't see how it is harder to learn than, say more redundant audio recording of the same words, or a video recording of the person speaking them. That extra information is irrelevant and will just be discarded by any smart algorithm anyway.
>The best available neuroscience and computational cognitive science says we do.
Most people do. As I said some people don't, and they function fine. See this: http://www.bbc.com/news/health-34039054
>Our external behavior is determined by the internal states, insofar as those internal states are functions which map sensory (including proprioceptive and interoceptive) statistics to distributions over actions. If you want your robots to function in society, at least well enough to take it over and kill everyone, they need a good sense of context and good representations for structured information, behavior, and goals.
Robots are never going to have exactly the same internal states and experience as humans. They could be very, very different, in structure, to the human brain. Being exactly like humans isn't the goal. Mimicking humans is an interesting diversion, but it's not necessary, or the goal in and of itself.
And you may be right that a robot without vision would be disadvantaged. I think that's mostly anthropomorphism, imagining how disadvantaged blind humans are (and in fact even blind humans can function better than most people expect.) But even if it's true, my point is that sight is not strictly necessary for intelligence.
In fact I think vision may even be a disadvantage. So much of the brain is devoted to visual processing. While text, and even language itself, are hacks that evolution created relatively recently. A brain built purely for language could be much more efficient at it than we can probably imagine. Ditching vision could save a huge amount of processing power and space.
Reading your post, you actually seem quite confused.
>Imagine a hypothetical language that is so compressed, so non-redundant, so little correlated, that it's indistinguishable from random noise. Learning this language may seem an impossible task.
Well yes, learning a class of strings in which each digit of every finite prefix is statistically independent from each other digit, is very hard, bordering on impossible (or at least, impossible to do better than uniform-random guessing).
>But in fact it's very easy to produce text in this language. Just produce random noise!
But that isn't the learning problem being posed! You are not being asked to learn `P(string | language)` (which is, in fact, the uniform distribution over arbitrary-length strings), but `P(language | string1, string2, ..., stringn)`, which by the way you've posed the problem factorizes into `P(language| character1) x P(language|character2) x ... x P(language|characterm)`. If the actual strings are sampled from a uniform distribution over arbitrary-length strings, then we have two possibilities:
1) The prior is over a class of languages some of which are not optimally compressed, and which thus do not render each character (or even each string) conditionally independent. In this case, the posterior will favor languages that do render each character conditionally independent, but we won't be able to tell apart one such hypothesis from another. We've learned very little.
2) The prior is over a class of languages all of which yield strings full of conditionally-independent noise: no hypothesis can compress the data. In this case, the evidence-probability and the likelihood cancel, and our posterior over languages equals our prior (we've learned nothing).
>Real language, of course, has tons of statistical patterns, and is definitely not random. But I don't see how it is harder to learn than, say more redundant audio recording of the same words, or a video recording of the person speaking them. That extra information is irrelevant and will just be discarded by any smart algorithm anyway.
Noooo. Compression does not work that way. Compression works by finding informative patterns in data, not by throwing them away. If your goal is to learn the structure in the data, you want the structure to be more redundant rather than less.
I'm telling you, once the paper is submitted, I can send you a copy and just show you the equations and inequalities demonstrating this fact.
>Most people do. As I said some people don't, and they function fine. See this: http://www.bbc.com/news/health-34039054
Differences in sensorimotor cortex function that leave the brain unable to perform top-down offline simulation with a high subjective-sensory precision don't invalidate the broad theory that cortical microcircuits are generative models (in particular, hierarchical ones, possibly just large hierarchies in which the individual nodes are very simple distributions).
http://www.fil.ion.ucl.ac.uk/~karl/The%20free-energy%20princ...
>Robots are never going to have exactly the same internal states and experience as humans. They could be very, very different, in structure, to the human brain.
Duh. However, if we want them to work, they probably have to run on free-energy minimization somehow. There is more necessity at work here than connectionism believes in, but that's a fault in connectionism.
>Being exactly like humans isn't the goal. Mimicking humans is an interesting diversion, but it's not necessary, or the goal in and of itself.
I didn't say that a working robot's representations had to exactly match those of humans. In fact, doing so would be downright inefficient, since robots would have completely different embodiments to work with, and thus be posed different inference problems in both perception and action. The fact that they would be, necessarily, inference problems is the shared fact.
>And you may be right that a robot without vision would be disadvantaged. I think that's mostly anthropomorphism, imagining how disadvantaged blind humans are (and in fact even blind humans can function better than most people expect.) But even if it's true, my point is that sight is not strictly necessary for intelligence.
Sight isn't. Some kind of high-dimensional sense-data is.
>In fact I think vision may even be a disadvantage. So much of the brain is devoted to visual processing. While text, and even language itself, are hacks that evolution created relatively recently. A brain built purely for language could be much more efficient at it than we can probably imagine. Ditching vision could save a huge amount of processing power and space.
That's putting the cart before the horse. Language is, again, an efficient but redundant (ie: robust against noise) code for the models (ie: knowledge, intuitive theories, as you like) the brain already wields. You can take the linguistic usage statistics of a word and construct a causal-role concept for them in the absence of a verbal definition or sensory grounding for the word, which is arguably what children do when we read a word before anyone has taught it to us, but doing so will only work well when the concepts' definitions are themselves mostly ungrounded and abstract.
So purely linguistic processing would work fairly well for, say, some of mathematics, but not so much for more empirical fields like social interaction, ballistic-missile targeting, and the proper phrasing of demands made to world leaders in exchange for not blowing up the human race.
Hold on. Let's say the goal is passing a Turing test. I think that's sufficient to demonstrate general intelligence and do useful work. In that case, all that is required is mimicry. All you need to know is P(string), and you can produce text indistinguishable from a human.
>Noooo. Compression does not work that way. Compression works by finding informative patterns in data, not by throwing them away. If your goal is to learn the structure in the data, you want the structure to be more redundant rather than less.
Ok lets say I convert English words to smaller huffman codes. This should be even easier for a neural network to learn, because it can spend less effort trying to figure out spelling. Of course some encodings might make it harder for a neural net to learn, since NNs make some assumptions about how the input should be structured, but in theory it doesn't matter.
>Some kind of high-dimensional sense-data is [necessary]... purely linguistic processing would work fairly well for, say, some of mathematics, but not so much for more empirical fields
These are some really strong assertions that I just don't buy, and I don't think you've backed up at all.
Humans have produced more than enough language for a sufficiently smart algorithm to construct a world model from it. Any fact you can imagine is contained somewhere in the vast corpus of all English text. English contains a huge amount of patterns that give massive hints to the meaning. E.g. that kings are male, or that males shave their face and females typically don't, or that cars are associated with roads, which is a type of transportation, etc.
Even very crude models can learn these things. Even very crude models can produce nearly sensible dialogue from movie scripts. Models with millions of times fewer nodes than the human brain. It's amazing this is possible at all. Of course a full AGI should be able to do a thousand times better and completely understand English.
Trying to model video data first is wasted processing power. It's setting the field back. Really smart researchers spend so much time eeking out 0.01% better benchmark on MNIST/imagenet/whatever, with entirely domain specific, non general methods. So much effort is put into machine vision, when Language is so much more interesting and useful, and closer to general intelligence. Convnets, et al., are a dead end, at least for AGI.
I'd need to see the math for this: how will the Huffman codes preserve a semantic bijection with the original English while throwing out the spellings as noise? It seems like if you're throwing out information, rather than moving it into prior knowledge (bias-variance tradeoff, remember?), you shouldn't be able to biject your learned representation to the original input.
Also, spelling isn't all noise. It's also morphology, verb conjugation, etc.
>Humans have produced more than enough language for a sufficiently smart algorithm to construct a world model from it.
Then why haven't you done it?
>Any fact you can imagine is contained somewhere in the vast corpus of all English text.
Well no. Almost any known fact I can imagine, plus vast reams of utter bullshit, can be reconstructed by coupling some body of text somewhere to some human brain in the world. When you start trying to take the human (especially the human's five exteroceptive senses and continuum of emotions and such) out of the picture, you're chucking out much of the available information.
There's damn well a reason children have to learn to speak, understand, read, and write, and then have to turn those abilities into useful compounded learning in school -- rather than just deducing the world from language.
>Even very crude models can learn these things. Even very crude models can produce nearly sensible dialogue from movie scripts.
Which doesn't do a damn thing to teach the models how to shave, how to tell kings from queens by sight, or how to avoid getting hit by a car when crossing the street.
>Models with millions of times fewer nodes than the human brain. It's amazing this is possible at all.
The number of nodes isn't the important thing in the first place! It's what they do that's actually important, and by that standard, today's neural nets are primitive as hell:
* Still utterly reliant on supervised learning and gradient descent.
* Still subject to vanishing gradient problems when we try to make them larger without imposing very tight regularizations/very informed priors (ie: convolutional layers instead of fully-connected ones).
* Still can't reason about compositional, productive representations.
* Still can't represent causality or counterfactual reasoning well or at all.
>Trying to model video data first is wasted processing power. It's setting the field back. Really smart researchers spend so much time eeking out 0.01% better benchmark on MNIST/imagenet/whatever, with entirely domain specific, non general methods. So much effort is put into machine vision, when Language is so much more interesting and useful, and closer to general intelligence. Convnets, et al., are a dead end, at least for AGI.
Well, what do you expect to happen when people believe in "full AGI" far more than they believe in basic statistics or neuroscience?
I don't think anything like that exists today, or ever will exist. And in fact you are making an even stronger claim than that. Not just that vision will be helpful, but absolutely necessary.
If I said "That is one huge fan", could you predict what my next word will be? It would depend a lot on context and the ability to reason within a complex world model. Depending on whether I had gone to a concert or to a wind tunnel, your distribution over guesses would alter. If I had gone whale watching you might even suspect I made a typo. Changing huge to large would lead to major to no adjustments, depending on each guess.
So while I agree an AI could emerge from text alone, it would have to be very sophisticated to do this.
doesn't that depend on the syntax of a language? my guess is that it would not work for a language that has a more flexible word order than English; so it wouldn't quite work for such languages as Russian, Turkish and Finnish (and other languages).
But I do believe it is possible, someday. Probably within our lifetime.
Edit - To be more clear I also agree AGI could be possible with text as the only input. I just think we need a new paradigm. Ostensibly AGI is meant to mimic human intelligence (minus the pitfalls), so IMO, the best approach will be that which mimics the underlying processes of human intelligence - not just the results. Traditional statistical NLP methods will probably have some role in this final system, but not the heart of it, as far as mimicking intelligence by mimicking intelligence's underlying processes goes.
It's learning a meaning, not the meaning. It's just a probabilistic model for the occurrence of a word based on the words that surround it. This should not serve as a base for the rest of your claims.
Anyway -- the improvements gained by multi-modal systems essentially disprove your thesis. Which is a good news! We're making progress.
Maybe we can do that with just words, but humans certainly don't. Words are related to concepts first, then we figure out the meaning of the rest.
Look at animal kingdom some animals are walking about 5 minutes of being born and running in hours.
How about when you include multimedia recordings? Or give the machine a camera and wheels?
Look at word2vec. By using simple dimensionality reduction on the frequency that words that occur near to each other in news articles, it can learn really interesting things about the meaning of words. The famous example is the vector "king" minus the vector for "man" plus "woman" equals "queen". It's learning the meaning of words, and the relationships between them.
Recurrent NNs, using similar techniques, but with much more complexity, can learn to predict the next word in a sentence very well. They can learn to write responses that are almost indistinguishable from humans. And it's incredible this works at all, given RNNs have only a few thousand neurons at most, and a few days of training, compared to humans' billions of neurons trained over a lifetime.
All of the information of our world is contained in text. Humans have produced billions of books, papers, articles, and internet comments. Billions of times more information than any human could read in their entire lifetime. Any information you can imagine is contained in text somewhere. I don't think it's necessary for AIs to be able to see, or interact with the world in any way.
If you can predict the word a human would say next, with enough accuracy, then you could also produce answers indistinguishable from theirs. Meaning you could pass the Turing test, and perform any language task they could do just as well. So language prediction alone may be sufficient for AGI.
This is the theory behind the Hutter Prize, which proposes that predicting (compressing) wikipedia's text is a measure of AI progress. The Hutter Prize isn't perfect (it's only a sample of wikipedia, which is very small compared to all the text humans have produced), but the idea is solid.