[misterman0]

The progress academics continuously make in NLP take us closer and closer to a local maxima that, when we reach it will be the mark of the longest and coldest AI winter ever experienced by man, because of how far we are from a global maxima and, progress made by academically untrained researchers will, in the end, be what melts the snow, because of how "out-of-the-current-AI-box" they are in their theories about language and intelligence in general.

[heyitsguay]

We hear that opinion all the time. As someone working in neural net-based computer vision I'd basically agree that the current approaches are tending towards a non-AGI local maximum, but I'd note that as compared to the 80s, this is an economically productive local maximum, which will likely help fuel new developments more efficiently than in previous waves. The next breakthrough may be made by someone academically untrained, but you can bet they'll have learned a whole lot of math, computer science, data science, and maybe neuroscience first.

[spinningslate]

Agreed. I'm nowhere near expert enough to opine on how far state-of-the-art is from some global maxima.

I'd contend that, for the most part, it doesn't matter. It's a bit like the whole ML vs AGI debate ("but ML is just curve fitting, it's not real intelligence"). The more pertinent question for human society is the impact it has - positive or negative. ML, with all its real or perceived weaknesses, is having a significant impact on the economy specifically and society generally.

It'll be little consolation for white collar workers who lose their jobs that the bot replacing them isn't "properly intelligent". Equally, few people using Siri to control their room temperature or satnav will care that the underlying "intelligence" isn't as clever as we like to think we are.

Maybe current approaches will prove to have a cliff-edge limitation like previous AI approaches did. That will be interesting from a scientific progress perspective. But even in its current state, contemporary ML has plently scope to bring about massive changes in society (and already is). We should be careful not to miss that in criticising current limitations.

[mrec]

> The next breakthrough may be made by someone academically untrained, but you can bet they'll have learned a whole lot of math, computer science, data science, and maybe neuroscience first.

I found this sentence particularly intriguing given that John Carmack recently announced that he was switching his main focus to AI.

[dna_polymerase]

> [..] this is an economically productive local maximum, which will likely help fuel new developments more efficiently than in previous waves.

This is exactly it. Every previously seen AI Winter had in common that funding was cut back. However, Google or other companies in the realm could approach a point where further investment wouldn't make sense to them. Until then there won't be a Winter, maybe Autumn, because smaller players disappear.

[feral]

The progress that has been made so far is already good enough to deliver tons of real business value. The tech is way ahead of application, as the tech has jumped forward so much in the last 5 years, and progress continues to be rapid.

I've direct evidence of that from my day job (building NLP chat bots for Intercom).

That business value will increase as we NLP progresses, even if we're moving towards a local optimum.

Even if we do get stuck, real products and real revenue powered by NLP will help fund research on successive generations.

Of course theres tons of hype about AI. But theres also a big virtuous cycle which just wasn't present in the setup which created previous AI winters.

[screye]

People thought that with LSTMs, and then we got transformers. People thought that with CNNs, and then we got Res-nets.

Progress is always that way. It plateaus, then suddenly jumps and then plateaus again.

If you complaint is about the general move away from statistics and deep learning becoming the norm, then there are a pretty decent number of labs who are working on coming up with whatever the next deep learning is. There is probabilistic programming and there models are some models with newer biologically inspired computation structures.

Even inside ML and deep learning, people are trying to come up with ways to better leverage unsupervised learning and building large common sense representations of the world.

There is certainly an oversupply of applied deeplearning practitioners, but there are other approaches being explored in the AI/ML community too.

[teshier-A]

Like the local maxima that the GLUE benchmark was for a few weeks (months?) before SuperGLUE got released ? This field is moving so fast, it's probably wiser to hold off over the top ominous predictions for a little while.

[octbash]

This sounds like some sort of perverse inverse of "This is good for bitcoin."

Making progress of NLP benchmarks? Must be a sign that we're moving even closer to an even longer and more bitter AI winter.

[tanilama]

It is not a summer/winter choice only if you chose to think this way. Such construction is superficial and drama oriented while reflecting very little what reality actually presents.

The current A.I. B.O.O.M is due to end or it is ending already, but this only means now we are equipped with really powerful approximators previous generation of researchers would not even dream of, that we left us with a really tantalizing question:

What is the right question to ask?

We have undoubtedly proved machine are superior to fit, now we need to make them curious.

[317070]

Yeah, some people even started giving it a name [0]: Schmidhuber's (local) optimum. It is a bit tongue-in-cheek, but the idea is that as long as Schmidhuber says he did it before, we are probably in the same basin of attraction as we were in the nineties.

The open question is whether AGI is the same as Schmidhuber's optimum, or even lies within Schmidhuber's basin.

[0] Cambridge style debate on the topic at Neurips 2019.

[tjansen]

But why does it have to be the longest AI winter? I would agree that current NLP approaches do not get us any closer to NLU. They won't hurt either though. They may even help to motivate people. I started working on NLU because the current state of voice assistants is so frustrating...

[The_rationalist]

But why does it have to be the longest AI winter? Because we have explored both paradigms (symbolic, subsymbolic and hybrid AI) The research has explored both existing paradigms and no other paradigm exist. Curve fitting (subsymbolic) is inherently limited. Maybe we need to reinvent symbolic AI, but almost nobody is working on it, and I'm not aware of any promising research paths/ideas for symbolic AI.

[visarga]

Isn't physics in the same situation? The theory is useful for countless applications but is ultimately flawed, and researchers are aware of that, of course. But we don't hear it every time there is a new application of physics. Why should the ultimate high standard be invoked so often in discussion only for ML? Other fields like psychology or economics are probably in an even worse position with their theories vs the reality.

ML is an empirical science, or a craft if you want, with useful applications. It's not the ultimate theory of intelligence.

[Iv]

Personally, seeing that connexionitsts and symbolists start talking with each other gives me hope that there won't be another AI winter before the AI singularity.

[2sk21]

Agreed. I should mention that reading Gary Marcus' new book really drives home your point.

[slumdev]

> progress made by academically untrained researchers will, in the end, be what melts the snow, because of how "out-of-the-current-AI-box" they are in their theories

This is an unnecessarily uncharitable view of academia.

"Outside the box thinking" is frequently just ignorance and Dunning-Kruger.

[laretluval]

Current academic NLP would have been considered quite out-of-the-current-box 10 years ago. Most academic progress is driven by young graduate students who think similarly to you.