Machine learning won't solve natural language understanding • Hacker News Board

Re: Machine learning won't solve natural language understanding

#191

Post by jakeinspace » Fri, Aug 13, 2021, 10:01 PM UTC

darawk wrote: post #106

This article is....total nonsense. None of the challenges described are impossible to solve with statistical methods. And i'd be willing to bet nearly any amount of money that they will be solved that way, long before they are solved in any other way. The problems posed here are being addressed as we speak with knowledge graphs and graph neural networks, and some of the others can be addressed with more nuanced and c…

Although I agree that statistical / ML methods will continue to conquer hurdle after hurdle in NLP, I wouldn't say that the article is total nonsense. The fact that NLP models trained purely on text data do not have access to the context available to a human interlocutor (years of visual, auditory, and other sense experience, along with all the higher-level mental hierarchies built from those experiences) is clearly a major handicap. I'm not positive that makes text-only NLP techniques inherently limited, but it has to be acknowledged. As far as I'm aware, we're still some years off from having NLP models capable of incorporating this sort of information.

If I had to guess, I'd say that the first NLP model truly able to pass the Turing Test will be trained on a multitude of data source - textual, visual, and audio. It will have a moderately sophisticated internal representation of "the world", though I doubt that model will be very amenable to human understanding. I'm not even sure what I really mean when I say a model of the world, because I don't think this quasi-intelligent NLP actor will necessarily have an understanding of the physical world in the way humans or animals do, especially when it's been trained with the purpose of demonstrating language understanding.

Re: Machine learning won't solve natural language understanding

#192

Post by danielmarkbruce » Sat, Aug 14, 2021, 4:32 AM UTC

ivalm wrote: post #108

danielmarkbruce wrote: post #77

Earlier quoted context omitted.

I'm not a physicist or mechanical engineer and this is a genuine question - why would we not be able to reliably design anything if we only had statistical models? Empirical formulas are used in various places in science/engineering and seem to work well (with exceptions). As an example - would we not arrive at something like f = ma using statistics and then use it without really knowing why (or maybe all the implica…

I think the main issue is interpolation vs extrapolation. Statistical models work well within the domain where data is available but have trouble generalizing to where data is unavailable. The very cool thing with physics is that theory was able to make a lot of true but wild predictions that only much later were backed up with experiment. Such powerful theories are very useful for finding interesting phenomena to st…

I'm saying "does it matter how we arrive at a model?".

Re: Machine learning won't solve natural language understanding

#193

Post by CharlesW » Sat, Aug 14, 2021, 4:20 PM UTC

joe_the_user wrote: post #52

canjobear wrote: post #39

The history of NLP is littered with people claiming on theoretical grounds that XYZ is unattainable using purely statistical methods, and that some notion of the logical structure of language needs to be brought in. And yet one by one, the XYZ have been attained by statistical methods. If you think there's something NLP can't do using machine learning, make a challenge dataset. That would be much more useful than yet…

I think a lot of people get the impression current NLP models like GPT-3 lack something - "understanding" or something. But they can't say exactly what it is. If they could define exactly what it was, they could indeed describe a way to train for it or program it. So a theoretical argument could useful if gave us an idea what are our mysterious something it. However, I'm not sure if this article makes a contribution…

> I think a lot of people get the impression current NLP models like GPT-3 lack something - "understanding" or something. But they can't say exactly what it is.

It's always seemed obvious to me (as an outsider) that it's missing reason and explainability. GPT-3 is a neat tool, but it seems like anthropomorphism to suggest that it's more than the best mimic humanity has been able to create so far.