Machine learning won't solve natural language understanding

This seems to be misunderstanding what PAC learnable means. It's saying that for parameters epsilon and delta, you can get an algorithm for learning a hypothesis that, with probability 1 - delta, has an average error rate less than epsilon, given some number of training samples polynomial in 1/delta and 1/epsilon.

This has nothing to do with the mental model. The language model doesn't have to spit out probabilities. It can use a simple thresholding function and spit out certainties. They just might be wrong, but a human's "certain" mental models might be wrong, too.

Well, I would have agreed with you a year or two ago. I have been experimenting with GPT-3, via OpenAI beta APIs. The generative (not extractive) summarization is impressive. Question answering functionality is also good.

Fast forward ten years when we have: 1. Much faster, with much more memory, custom neural processing hardware. 2. Advances in modeling techniques, similar to how Transformer models seemed revolutionary a few years ago.

Wait ten years.

There's no reason in principle a machine can't learn this kind of thing (humans are still machines, after all), but I think this certainly points to why humans learn language so much faster than machine models. We can synthesize language understanding with a much richer, sensory-based model of the world and recognize that words correspond to clusters of sensations like specific sights, sounds, tastes, feelings, and smells.

A logical next step in AI research is figuring out some kind of way to get these various systems that do one thing and one well to interoperate the way all the parts of a brain do, so Alpha Go, some object recognizer trained on Go boards, and a language model trained on sentences about the game of Go can recognize they're all modeling the same thing and augment each other with crosstalk and competitive hypothesizing in roughly the way animal reasoning seems to work.

Natural language evolves with what's happening in the world. Natural language also refers to what's happening in the works. For ML to keep up with that it would have to understand what's going on in the world, no?

Imagine you see a discussion about a border issue between two countries. You see a sentence that says: "What if country X does what Lukashenka did?"

If you've paid attention to the news about the border issue between Belarus and Lithuania you'd understand that the sentence is referring to flying in Iraqi migrants to Belarus to then send them to Lithuania to try to cross the border. Effectively to "flood the border".

It gets worse too depending on the perspective. For those that support Lukashenka the meaning might be different, because they don't accept that the above is what really happened.

For an ML model to understand all of this, it would have to have an understanding of what's going on in the real world. And it would have to be able to glean this understanding from few examples. I'm not sure how feasible this is.

> evaluating the output of some NLP system regarding the above tasks is subjective [...] However, language understanding does not admit any degrees of freedom. A full understanding of an utterance or a question requires understanding the one and only one thought that a speaker is trying to convey

I'm not understanding how NLU would be inherently more objective than something like the given example of named-entity recognition.

> None of the above challenging semantic understanding functions can be ‘approximately’ or ‘probably’ correct – but absolutely correct. In other words, we must get, from a multitude of possible interpretations of the above question, the one and only one meaning that, according to our commonsense knowledge of the world, is the one thought behind the question some speaker intended to ask. In summary, then, true understanding of ordinary spoken language is quite a different problem from mere text (or language) processing where we can accept approximately correct results – results that are also correct with some acceptable probability.

Why would only absolute accuracy be acceptable? Even humans misinterpret things with some frequency. In many cases, the text just won't contain enough context for anyone to give anything more than an educated guess to the writer's intentions.

I'd further argue that language is lossy and thoughts aren't discrete on the scales relevant to language, such that even in the best of cases we don't interpret exactly the same thought as the speaker has in mind - just something that's approximately close enough to allow for useful communication.

> The equivalence between (machine) learnability (ML) and compressibility (COMP) has been mathematically established.

Hopefully the article doesn't take "learning can be viewed as compression of information" to mean "all that a trained model can do is compression of its inputs".

> Natural language understanding, on the other hand, and due to MTP, requires intelligent ‘uncompressing’ techniques that would uncover all the missing and implicitly assumed text. Thus, machine learning and language understanding are incompatible – in fact, they are contradictory.

Aargh!

> Note that antonyms/opposites such as ‘small’ and ‘big’ (or ‘open’ and ‘close’, etc.) occur in the same contexts with equal probabilities. As such, (1a) and (1b) are statistically equivalent

If you're working with a Markov chain maybe.

> In ML/Data-driven approaches there is no type hierarchy where we can make generalized statements about a ‘bag’, a ‘suitcase’, a ‘briefcase’ etc. where all are considered subtypes of the general type ‘container’. Thus, each one of the above, in a purely data-driven paradigm, are different and must be ‘seen’ separately in the data. If we add to the semantic differences all the minor syntactic differences to the above pattern (say changing ‘because’ to ‘although’ — which also changes the correct referent to “it”) then a rough calculation tells us a ML/Data-driven system would need to see something like 40,000,000 variations of the above to learn how to resolve references in sentences such as (2).

Machine learning models don't need to see every possible combination to be able to generalise, and they can learn that bags/suitcases/etc. are related.

For this and the rest of the article, I keep feeling like I must be (ironically) misinterpreting the author. There's repeated bold theoretical claims about what machine learning will never do, but about tasks that modern models are already to some degree capable of.