Maybe AI hype won't ever solve natural language understanding, but it'll keep some cash and careers going.
Machine learning won't solve natural language understanding
141–150 of 193 posts
Re: Machine learning won't solve natural language understanding
#142Earlier quoted context omitted.
The two tasks are interconnected. The reasoning flows both ways.
Noise and lack of specificity are completely different problems, and the article concerns itself with the latter
I was replying specifically to the line in the comment I quoted, not the article as a whole.
Re: Machine learning won't solve natural language understanding
#143This person is wrong and doesn't understand how brains produce and interact with language, but other people have already said that, so what is the straw that this person is grasping at?
There is a representational / symbolic divide in much of AI. Unfortunately some people think symbolic processes are somehow more "true" or "real" than representational processes. This leads them to say things like "A full understanding of an utterance or a question requires understanding the one and only one thought that a speaker is trying to convey." As if there were such a thing in a jello-like mass of briefly sparking cells.
But symbols are super useful, you're processing them now to understand my thoughts. They are so useful that you see them constantly in human innovations. Math, logic, art, literature ... everywhere! So what is a more useful way to understand the relationship between our gooey protoplasm and the beauty of say the pythagorean theorem? It's the same way to understand the relationship between analog and digital computation. The latter is built on the former.
Your digital (symbolic, supposedly pure) world is built on differences in ranges of voltages in physical circuits. (Or ranges of magnetic polarization, or ranges of light frequencies). We tame a continuously variable world by defining boundaries on continuous ranges and then pretend those are pure symbolic values.
This is the problem the author is wrestling with. They haven't differentiated the computational substrate from the computation. Can we build systems that "understand" using probabilistic function approximation? Sure! That's how we work. But the program running on that messy substrate isn't there yet and that's what's got the author in a tizzy.
Re: Machine learning won't solve natural language understanding
#144I think that's very true and it's maybe even more clear when you consider mathematics. You can maybe imitate but not effectively learn mathematics empirically. There is an infinite number of mathematical expressions or sequences that can be generated, so learning can never be done, you cannot compress yourself to mathematical understanding. (which is obvious if you try to feed language models simple arithmetic, they…
> they can maybe do 5+5 because it shows up somewhere in the data, but then they can't do 3792 + 29382 I'd say you're underselling modern AIs. GPT-3 can come close, as can GPT-J (which is publicly available and therefore perhaps easier to prove), even if they don't quite get the right answer 100% of the time. I gave GPT-J the following prompt (which you can try yourself at https://6b.eleuther.ai/ just wait a few min…
You have to be careful when analyzing GPT-3 and derivatives due to BPE encoding. It's accuracy greatly improves when adding commas to strings of numbers: https://www.gwern.net/GPT-3#fn21
Re: Machine learning won't solve natural language understanding
#145GPT-3 seems to refute these arguments. It is able to solve most understanding tasks reasonably well.
Re: Machine learning won't solve natural language understanding
#146Related: Searle's "Chinese Room" philosophy thought experiment. https://en.wikipedia.org/wiki/Chinese_room
"The shortest, funniest refutation of Searle's Chinese Room" by Daniel Dennett:
Re: Machine learning won't solve natural language understanding
#147I first came across this form of misunderstanding in a radio interview on the BBC (probably Radio 3..) with John Searle. This would have been early 80s. His seemingly deep misunderstanding of the domain triggered a lifelong interest in the subject. The failing seems to be to do with drawing the system boundary in the wrong place (at the walls of the Chinese Room).
See Daniel Dennett's refutation of Searle's "Chinese Room" thought experiment: https://ase.tufts.edu/cogstud/assets/searle.jpg
Re: Machine learning won't solve natural language understanding
#148Caveat lector: I'm analysing this as a linguist. Not as a programmer. (Unless you count a bunch of bash scripts as "programming".) >Let us start first with describing what we call the “missing text phenomenon” (MTP), that we believe is at the heart of all challenges in natural language understanding. The missing text is not the heart of the challenge; it's just a surface issue. The actual problem is deeper - machines…
GPT-3 can figure out a nonsensical word and use it in the same example. The prompt is between quotes, everything else is generated. > "I got a cat. That wug sleeps on my bed." On every night. He’s really nice. But he isn’t small. > I got a dog. That wug sleeps at my house. On every night. She’s really nice. But she isn’t small. ~~~ Another one in dialogue format. > Human: I got a cat. That wug sleeps on my bed. > AI:…
Can GPT-3 figure out that a common word is being used outside its usual context, so its usual meanings don't apply? Let's say my example was instead
"I got a cat. That little orc sleeps on my bed."
I wonder how GPT-3 would deal with it. Maybe it would think "cat" and "orc" refer to different objects/beings? Or associate "cat" with features that clearly wouldn't fit a cat like "green", "humanoid", or "fantasy monster"?
Re: Machine learning won't solve natural language understanding
#149Earlier quoted context omitted.
I don’t understand how we don’t? I make sound snapping my fingers. Whistling. Why is it so hard to accept mirror neurons fired when early humans heard birds and animals, each other’s grunts and over time we refined it? Everyone has some capacity to refine and strengthen muscle. Why do we need some abstract meta-construct to explain where language comes from? It comes from us. Fleshy meat bags that mutate state over a…
I'm not talking about "where language comes from." I'm talking about a mechanical understanding of how humans produce and understand language. Chomsky's idea goes deeper than "just random sounds," given his theory of universal grammar.
Religion shows we can create very detailed belief in ornate ideas that are scientifically nonsensical. It’s possible the concepts linguists seek to develop are similarly self reinforcing feedback loops that are meaningless to science.
There’s been an explosion in biological science since Chomsky became prominent. He even admits his work is abstract and to serve his ends. We may be putting more into his ideas due to his fame and infamy than we should.
Trial and error, social constraints on sounds and the mechanics of biology can explain it elegantly. What do the ornate theories and ephemeral organs provide except to satisfy “linguists” biological agency to create and imagine?
Linguistics came about in a much less scientifically aware era in human history. My money is on it going the way of religion; an abstraction that’s so orthogonal to advancing science we leave it behind.
Re: Machine learning won't solve natural language understanding
#150Well, yes and no ...
Any language model alone isn't going to solve natural language understanding, but some future ML full-brain model surely will achieve AGI and therefore language understanding as part of that.
What's missing from a pure language model, is any grounding in reality and ability to interact with the world to test and extend its knowledge beyond what is derivable from the corpus it was trained on. It's level of understanding is ultimately limited by the content of the training corpus, regardless of how massive that may be.
Something like GPT-3 is really just a statistical twist on Doug Lenat's Cyc; it's understanding is always going to be limited by it's own fundamental nature. Yes, one deals with language, and one with facts, but ultimately both are just large, fixed, self-referential bodies of data.
Cyc really is a great analogy, and for some reason it took decades for Lenat et al to eventually realize that regardless of how much fixed data you added to it, it was never going to be enough. A closed black box can never know what's outside of the box, although it may gamely try to tell you if you ask it.
These modern language models, GPT-3, etc, have certainly been a bit of an eye opener, and can perform some impressive feats (question answering, etc), but one shouldn't be tempted to believe that if scaled up sufficiently they'll eventually somehow transcend their own nature and become more than a language model... a one-trick pony capable of generating plausible continuations of whatever you seed it with.