[HN Gopher] Why Mastering Language Is So Difficult for AI
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Why Mastering Language Is So Difficult for AI
Author : TeacherTortoise
Score : 54 points
Date : 2022-10-07 16:43 UTC (6 hours ago)
(HTM) web link (undark.org)
(TXT) w3m dump (undark.org)
| noobermin wrote:
| Thankfully someone is pushing against the hype, good for him.
| Thank god.
|
| It really frustrates me when people, particularly people on here
| specifically respond to criticism of NNs and the like by saying
| nonsense like "but YOUR brain is a neural net!" as if all your
| brain is but mimickry after all and there's nothing else. There
| is this need to reduce all the mind's complexity down to a model
| that you seem to understand, just because you understand it,
| which seems like such a lazy, reductionist way to think.
| Reductionism in general has value but it tends to narrow the
| mind.
|
| I feel like Gary Marcus is right, you don't just need the
| mimickry even if it plays a role, you do need "symbolic"
| understanding somewhere, and that makes it easier to determine
| whether Trump is president vs. Biden at the moment, as they talk
| about in the article. You do need concepts (symbols) deep down
| somewhere, it seems so bizarre to think you don't because no one
| even in their own lives lives that way or thinks that way. I mean
| literally right now, me reading and writing comments on hacker
| news is all about ideas and concepts, symbols in the
| philosophical sense. I feel like even if it started as mimickry
| when I was an infant, eventually the mimickry takes a life of its
| own, I figure out _concepts_ themselves, and I no longer even
| work in mere mimickry, I work with symbols.
|
| It reminds me of Baudrillard's concept (unintended pun, I think)
| of simulacra, it's not that simulacra are just representations of
| the real and are thus fake, like money represents some amount of
| food or gold or something and so money is fake. Money, as we all
| know, is in fact very real! Our daily lives are shaped around it,
| they shape our reality, things like loans, credit, bank accounts,
| stock markets eventually become things we care about. Money is
| therefore hardly fake. Eventually the simulacra develop a reality
| of their own, in fact, they become hyperreal.
|
| Ideas are the same, and any real AI will need them.
| everly wrote:
| Oh, he _loves_ to push against the hype alright. To his credit,
| he's very smart - I think he sold an AI startup to Uber several
| years ago. And I agree with a lot of his criticisms and general
| AI philosophy.
|
| Still, it's impossible for me to shake the feeling that he just
| likes to hear himself talk and doesn't want to see progress in
| AI.
|
| I think I trust him more than Sam Altman though, not that
| anyone's being asked to make that judgement.
| ripe wrote:
| > Still, it's impossible for me to shake the feeling that
| [Gary Marcus] just likes to hear himself talk and doesn't
| want to see progress in AI.
|
| He says he wants to see progress in AI. He wants people to
| get their heads out of the neural network cul-de-sac and use
| other tools, too. His quote:
|
| "Imagine a world in which iron makers shouted "iron," and
| carbon lovers shouted "carbon," and nobody ever thought to
| combine the two [to make steel]; that's much of what the
| history of modern artificial intelligence is like." [1]
|
| An adjustment will happen when the limitations of these
| purely statistical methods play out and burn a few self-
| driving car investors. Unfortunately, it will also probably
| taint the entire field of AI and start another AI winter for
| a decade or so.
|
| [1] https://nautil.us/deep-learning-is-hitting-a-wall-238440/
| everly wrote:
| Thanks, that's a worthy addition (and good example of what
| I mean about agreeing with his philosophy). I was probably
| a bit too uncharitable in saying he doesn't want to see
| progress.
| lvxferre wrote:
| [taunt]Most things that I hear about natural language processing
| boil down to a bunch of codemonkeying assumers trying to
| oversimplify linguistic phenomena that they are completely
| ignorant about, and yet trying to sell it as if it was the next
| best thing after sliced bread.[/taunt]
|
| Anyway. There are three things that NLP is notoriously bad at:
|
| 1. Using world knowledge to interpret utterances in a given
| context. The article itself provides a neat example of that with
| United-Statian presidents.
|
| 2. Contextualisation that goes beyond the text that the utterance
| is found in.
|
| 3. Assessing the pragmatic purpose of an utterance.
|
| But of course, "linguistics is useless for NLP!", the codemonkeys
| say.
| Der_Einzige wrote:
| No, it unironically IS useless. You're welcome to bark up the
| symbolic tree along with Chomsky, Gary Marcus, and others who
| are about to fade into obscurity.
|
| Or you can embrace the bitter lesson, which is that symbolic
| techniques are considered harmful, like the future winners of
| the AI race have:
| http://www.incompleteideas.net/IncIdeas/BitterLesson.html
| sbierwagen wrote:
| Saved you a click: it's an interview with Gary Marcus.
| CharlesW wrote:
| Saved you another click if you also have never heard of this
| person: https://en.wikipedia.org/wiki/Gary_Marcus
| wpietri wrote:
| It's nice to see some coverage of AI that is realistic about the
| issues.
| freeopinion wrote:
| Mastering language is difficult for humans. I don't think most
| people realize how often they just guess at meanings, and how
| often they are wrong. Especially in spoken language.
|
| Constantly asking for clarification is exhausting and gets you
| labeled as pedantic and "difficult". So most people don't do it.
| I'd guess that a fairly high percentage of human communication
| results in a misunderstanding. This is largely masked by human
| inaction or ability to disguise their lack of understanding. The
| AIs have no ego and no incentive to hide their confusion.
| kelseyfrog wrote:
| Replace GPT-3 with Bob, language model with person, and deep
| learning with learning, and you arrive at the conclusion that
| we're not able to determine whether people actually understand
| the world around them. Any utterance is indistinguishable from a
| sufficiently advanced computation model which simply produces the
| next token of text. Which is to say that the essential
| discriminating characteristic isn't the fact that GPT-3 runs on a
| computer, it's that it doesn't have a basis of reality rooted in
| the present.
|
| The article correctly recognizes this, but then fails to actually
| make any meaningful contributions. Say the model was in fact
| conditioned on the basis of a present reality, would that change
| the interviewee's mind? The fact that it's difficult to say
| simply hints that the interviewer didn't really do that great of
| a job drilling down to the interesting questions.
| beezlebroxxxxxx wrote:
| I think implying that GPT-3 and a human learn a language in the
| same way is an error. AI "learning" is very rigid; while human
| learning is often a complicated mishmash of behaviour, social
| feedback, reasoning, and imagination.
|
| > conditioned on the basis of a present reality
|
| If we grant this hypothetical, we might find what it "says"
| fundamentally alien to us --- we might not even know it is
| saying anything at all.
| Tijdreiziger wrote:
| Neural networks are modeled after the neurons in human
| brains, so by definition, the way NNs learn approximates the
| way humans learn.
| kelseyfrog wrote:
| How does the brain do backprop?
| Der_Einzige wrote:
| So, that's pretty wrong.
|
| Spiking neural networks are possibly closer to being
| biologically analogies, but they kind of suck to train and
| don't have good performance.
|
| It takes a lot of computer "neurons" to simulate a single
| human neuron, and we can't simulate it perfectly in any
| case.
| yyyk wrote:
| Neural networks were modeled after _a model_ of the neurons
| in human brains, a model which we know by now is inaccurate
| and misses a lot of details.
| Jensson wrote:
| > Replace GPT-3 with Bob, language model with person, and deep
| learning with learning, and you arrive at the conclusion that
| we're not able to determine whether people actually understand
| the world around them.
|
| Sure we can, there are diseases which greatly hampers your
| ability to understand the world but doesn't hamper your verbal
| abilities at all, Williams syndrome for example. They are great
| at generating nonsense stories that doesn't make sense. GPT-3
| is like that but much worse.
|
| https://en.wikipedia.org/wiki/Williams_syndrome
| chongli wrote:
| For a particularly striking example of the opposite problem,
| see people with Wernicke's Aphasia [1]. Here's an example [2]
| of a man who had a stroke that left him with the disorder. He
| can speak fluently but his speech is meaningless.
|
| Byron can understand what's happening in the world around him
| and can communicate it through body language (just see the
| warmth in his eyes and his smile) but when he speaks
| everything comes out in a stream of nonsense.
|
| If we tried to apply a language-model-centric view of
| intelligence we would rate Byron is unintelligent. That would
| clearly be a mistake, as anyone can plainly see that he is
| intelligent.
|
| [1] https://en.wikipedia.org/wiki/Receptive_aphasia
|
| [2] https://www.youtube.com/watch?v=3oef68YabD0
| kelseyfrog wrote:
| Right, so there is a characteristic other than computational
| substrate that allows us to differentiate between systems
| that have this ability and those that don't.
|
| Say we apply the diagnostic criteria of William's syndrome to
| language models and deduce that they meet the criteria for
| diagnosis, then the diagnostic itself ie: the test results
| are the grounding for the conclusion not the computational
| substrate. In that way the discussion surrounding the
| computation substrate is a red herring or at the very least a
| unclear and roundabout way of grouping of the characteristic
| we're actually interesting in.
| mistermann wrote:
| > Replace GPT-3 with Bob, language model with person, and deep
| learning with learning, and you arrive at the conclusion that
| _we 're not able to determine whether people actually
| understand the world around them_.
|
| Wouldn't the fact that people's facts do not match up prove
| that they do not?
| hinkley wrote:
| I recall reading some advice to parents not to freak out if
| they discover their child lying at an early age. The sentiment
| was that lying is built on a mental model of another person,
| and therefore requires some substantial development of
| emotional intelligence to even make the attempt.
|
| There's some romanticism in common between magicians, con
| artists, and fraudsters about how unclearly 'normal people'
| perceive the world so perhaps there's some truth to what you
| say. And the perpetual problem with artists and visionaries is
| that they see the world before everyone else does. For the one
| group they see things as they are now while everyone else seems
| to see things as they were 2, 5 years ago. For the other they
| see what the world could be, by exercising the tools near at
| hand.
|
| One wonders then how an AI that actually understand things
| would be seen. As a trickster? A misunderstood intellectual? A
| madman?
| benlivengood wrote:
| Language is a communication method evolved by intelligent beings,
| not a (primary) constituent of intelligence. From neurology it's
| pretty clear that the basic architecture of human minds is
| functional interconnected neural networks and not symbolic
| processing. My belief is that world-modeling and prediction is
| the vast majority of what intelligence is, which is quite close
| to what the LLMs are doing. World models can be in many
| representations (symbolic, logic gates, neural networks) but what
| matters is how accurate they are with respect to reality, and how
| well the model state is mapped from sensory input and back into
| real-world outputs. Symbolic human language relies on each
| person's internal world model and is learned by interacting with
| other humans who share a common language and similar enough world
| models, not the other way around (learning the world model as an
| aspect of the language itself). Children learn which language
| inputs and outputs are beneficial and enjoyable to them using
| their native intelligence and can strengthen their world model
| with questions and answers that inform their model without having
| to directly experience what they are asking about.
|
| People who don't believe the LLMs have a world model are wrong
| because they are mistaking a physically weak world model for no
| world model. GPT-3 doesn't understand physics well enough to
| embed models of the referents of language into a unified model
| that has accurate gravity and motion dynamics, so it maintains a
| much more dreamlike model where objects exist in scenes and have
| relationships to each other but those relationships are governed
| by literary relationships instead of physical ones and so
| contradictions and superpositions and causality violations are
| allowed in the model. As multimodal transformers like Gato get
| trained on more combined language and sensory input their world
| models will become much more physically and causally accurate
| which will be reflected in their accuracy on NLP tasks.
| ripe wrote:
| You might be right that these "multimodal" transformers, by
| integrating additional data from non-text sources, would be
| more capable than GPT-3. But I don't think that invalidates
| Gary Marcus's point.
|
| The word "model" is another of those words that Minsky called
| "suitcase words"--- they can be used to mean many things. I
| don't think Marcus is saying that that LLMs have "no model",
| just that they don't have a model of the type that a symbolic
| system could have. He gives many examples of deductions that a
| symbolic AI system can easily do, which GPT-3 is simply
| incapable of.
| benlivengood wrote:
| To hint at what I fundamentally mean by model I'd be
| interested to see a symbolic model of vision. E.g. take 512
| _512_ 3 numbers and give them names and then follow some
| rules to arrive at "cat" or "dog". Image recognition, I
| think, is demonstrably not symbolic. Likewise most
| transformations from the real world to model state are not
| symbolic. Within model state, symbolism may have uses but
| Church-Turing claims that it isn't _necessary_.
|
| It seems clear to me that if the CLIP-like part of Imagen or
| Stable Diffusion can take an image made of pixels and yield
| "cat" and similarly take "dog" and produce a 3D neural
| radiance field that we can light just like any other 3D model
| and recognize as a dog then there must be an accurate and
| useful model of both how vision works and what dogs look like
| and the English relationship between those two things inside
| the machine.
|
| I also wish Gary Marcus was replying to Google's Minerva
| paper instead of GPT-3. The ability to answer multi-step
| symbolic problems is basically here.
| https://ai.googleblog.com/2022/06/minerva-solving-
| quantitati...
|
| I'd also note that decades of attempts at automated theorem
| proving with symbolic systems haven't yielded similarly
| impressive results. Now we have deep learning models helping
| with proof search.
| hinkley wrote:
| Are you saying that GPT-3 doesn't grasp Object Permanence?
| fspeech wrote:
| Well put on what language is. Language encodes the delta of
| worlds. There are way more unsaid than what is said explicitly.
| noobermin wrote:
| Reading things like this on hacker news and twitter reminds me
| a lot like particle physicists unwilling to grapple with lack
| of evidence for SUSY. Every time someone points it out, these
| guys merely recite the holy texts (describe the model in gross
| detail with more magic words as if the criticism is because the
| critic don't understand it well enough), as if that addresses
| the critique at all. They then eventually gesture at the energy
| horizon, suggesting that "with more _s_ [0] you'll eventually
| see my particles."
|
| It's feels a bit similar here. There are clear limitations to
| AI by just NNs, namely they like Stable Diffusion must be
| trained on so much data, far more than any single artist will
| ever see, and they still fall short sometimes. With NLP it
| seems a little more obvious because the limitations seem to lag
| actual speech understood by humans still, even with the massive
| dataset again beyond what any single human will ever digest.
| There are clear issues here, merely reciting to critics what
| your model of intelligence is doesn't address the shortcomings
| these systems have which any literate person can perceive. And
| gesturing at the data horizon (you need even _more_ data??) is
| not convincing either.
|
| Apparently this cat (the interviewed author) has some baggage,
| but the things he's talking about (symbols) have already been
| studied and used by philosophers and linguists long before AI
| or computers were a thing. It helps to build upon what other
| researchers have already done, I don't know why NN researchers
| don't seem to map what they are doing to existing research on
| intelligence in other areas, namely cognitive science. Do NN
| researchers have any contact with linguists or cognitive
| scientists generally?
|
| Again, being a little ignorant of his baggage, I do think a
| hybrid approach sounds like it would be promising. Symbolic AI
| failed in the 70s, NN is getting a bit of the way there now and
| is pretty useful but misses a few things, why go all in on just
| one type of approach?
|
| [0] _s_ is basically the center of mass energy, what particle
| physicists mean when they say the "energy" of an interaction
| (collision)
| xwolfi wrote:
| You say far more than any artist but I m not so sure. When we
| spend 20 years looking at stuff before doing one passably
| interesting work of art (bar absolute outlying geniuses),
| it's not a small time nor a small training cost. It may feel
| easy for us but our brain never stop ingesting information
| for decades before being original enough.
| Der_Einzige wrote:
| "Every time I fire a linguist, the performance of my speech
| recognizer goes up"
| hackinthebochs wrote:
| Yes, and the history of philosophy is replete with examples
| where the learned opinion on a subject stymied progress.
| Respect for one's intellectual forebearers is good, but being
| constrained by them has proven to be detrimental in areas
| where the learned opinion is largely speculative. The
| progress made by deep learning is so unlikely anything that
| came before, trying to shoehorn old methods and ontologies
| into the new paradigm would only harm progress.
| 317070 wrote:
| This NN breakthrough in language is only about 2 years old
| now [0].
|
| People are working on your symbolic logic hybrid approach,
| but not much interesting has happened yet. This is a bit
| expected, since the short amount of time, and the fast
| scaling these language models have done in those last two
| years. It's been hard to outscale them with any other
| approach, let alone by the particular one of symbolic logic.
|
| I think the main discripancy with particle physics, is that
| every iteration of bigger language models are also
| significantly better, no diminishing returns in sight.
|
| Better for concrete applications as well. There is therefore
| no need to claim these models are scaled for scientific
| reasons in the search of AI, the accountants are happy as it
| is.
|
| [0] https://arxiv.org/abs/2005.14165
| gojomo wrote:
| While language seemed difficult for AI for a while, it now seems
| to be falling quite rqpidly to recent techniques.
|
| (It reminds me a little of the early years of aviation. In 1901
| the US Navy's chief engineer called manned flight a "vain
| fantasy", & in 1903 the New York Times suggested flying machines
| would be possible in "one million to 10 million years". But it
| was accomplished later in 1903, with rapid improvement after
| that. Things that seem impossible now can become humdrum in a few
| decades.)
|
| What are the current best results from Marcus, or others using
| his preferred approaches?
| pmontra wrote:
| TL;DR "A parrot's not a bad metaphor, because we don't think
| parrots actually understand what they're talking about. And GPT-3
| certainly does not understand what it's talking about."
|
| BTW a parrot knows a big deal about the world. Not much about
| human language but still more about language than GPT-3. Of
| course a 737 doesn't know how to fly but it still flies people
| around the world. Landing on a branch, not much.
| Gibbon1 wrote:
| Grandmother mentioned her aunt had a parrot. One time she and
| her aunt are hanging out. There was a lineman up on the pole in
| front of the house and the bird was curiously watching him.
| After a bit a young lady walks by and the bird chuckles and
| gives out a loud wolf whistle. And the lady then begins to cuss
| out the lineman up on the pole. The bird giggled and then
| giggled off and on for the rest of the afternoon.
|
| GPT-3 is closer to a talking toaster than a Parrot.
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