Comment by The_rationalist | Hacker News Reader

Comment by The_rationalist | Hacker News Reader

The_rationalist

<rant> Are there any real use case for GPT-2? Does it solve any problem? I've read almost all state of the art leaderboards of all Nlp tasks of paperswithcode.com and truth is except text generation, openAI has not one state of the art, they are not even visible in leaderboards. OpenAI is maybe the AI research center with the biggest funding and comparatively to other well known (Microsoft, Facebook, Google or even zalando..) they are the ones with the least results.

From my observations most SOTAs come from chineses researchers by far, followed by deepmind.

BTW isn't that a sad truth that not even one of all major AI actors has a draft of an AGI architecture, something comparable to CYC or opencog. https://wiki.opencog.org/w/CogPrime_Overview

Two other observations I would like to share: Many important NLP tasks have almost nobody publicly working on them it seems, on paperswithcode.com or NLP-progress (from github) some tasks have only one or two papers... And many others have not evolved since 2016. Most of the time it seems trivial to beat the old state of the art, just use BERT or XLnet on a task where nobody applied it before and hop, free state of the art for you! Yet researchers don't seems to chase those low hanging, high returns fruits. Also researchers seems to work a lot in isolation, many new generic improvements like new optimizers (RAdam for example) and new activation functions (Swish) allow to beat most of older state of the art on almost all task just by using them. Yet researchers will take years before using them because of an absurd inertia. Also unlike an open source program, BERT and XLnet have very low response and activity on github despite major open issues... </rant>

Inference, question-answering, NER detection/disambiguation are pretty important NLP tasks (at least from a practitioner's perspective). While GPT-2 has gained mindshare for its generative capabilities, BERT and other pre-trained Transformer Encoder models are used for production workloads (we use BERT for text classification and explored using it for clustering).

It's useful to view the GitHub projects for these models as reference implementations. They're intended to provide a roadmap for reproducing the research and to aid in implementing production libraries.

Regarding the latter, take a look at the work by HuggingFace, the Flair project, Spark-NLP and others.

"Inference, question-answering, NER detection/disambiguation are pretty important NLP tasks" Yes indeed.

"While GPT-2 has gained mindshare for its generative capabilities, BERT and other pre-trained Transformer Encoder models are used for production workloads" You rephrased my point pretty well, while openAI search for "fun" tasks, deepmind and others allow progress on real world tasks.

You use BERT which is nice but do you consider using it's successor: XLnet?

"take a look at the work by HuggingFace, the Flair project, Spark-NLP and others." I was aware of Flair (from Zalando) but thank you for Huggingface and Spark-NLP, I will take a look!

The article mentions TabNine, which has made it to the HN frontpage before.

For reference, the blog article for the release of "Deep TabNine", the auto-completion engine based on GPT-2: https://www.tabnine.com/blog/deep/.

And earlier this month they released a local version: https://www.tabnine.com/blog/local/.

> are there any real use case?

Writing Bloomberg's "market wrap" articles.

> Most of the time it seems trivial to beat the old state of the art, just use BERT or XLnet on a task where nobody applied it before

If it was a high-return fruit somebody would be doing it. Not necessarily publishing papers about it or trying to beat useless artificial benchmarks on it.

If it was a high-return fruit somebody would be doing it." Not necessarily.

Not necessarily publishing papers about it Yes.

trying to beat useless artificial benchmarks on it. Wtf is this bullshit? AI benchmarcks are what direct progress in AI and allow to quantify it. And they are less and less artificial and more and more real world: E.g quora, reddit, Wikipedia and Facebook datasets.

>"If it was a high-return fruit somebody would be doing it." Not necessarily. With high likelihood given current funding of ML with $$$ applications but yes, not necessarily.

> AI benchmarcks are what direct progress in AI

Sadly this is largely true.

The AI benchmarks + culture around it are the bullshit.

What actually moves forward the field of AI is:

- accessible

- reproducible

- comprehensible

results done with some thought and reasoning which is explained well, published well, and justified by more than some #$!& "our F1 score went up by 2 therefore our approach makes sense" bullshit.

AI benchmarks have done as much to retard progress in AI as they have to promote it.

Current AI benchmark top scores are gamification for big companies to waste even more resources running algorithms they can't explain. They are not machine learning, they are machine pissing contests.

Many important NLP tasks have almost nobody publicly working on them

Well, then perhaps you should go work on them, instead of ranting here.

Why the ad hominem? I am pointing a problem of allocation of ressources on the AI research field. It's not to me to fixe that, but yes I am actively working on a logical fallacies detector which is the first of human history and works for the 256 possible forms of syllogisms, I'm expanding it to other logical forms such as modus ponens/tollens.

It's not to me to fixe that

There's nothing to fix. People work on what they want to work on. Things that seem important to you are not important to me, and the opposite. I'm OK with that.

"People work on what they want to work" ideally yes, but ultimately they work on something that please them AND that give them a decent salary. Funding should not go to fun (but useless in the real world) Nlp tasks. "Things that seem important to you are not important to me, and the opposite." and here's go relativism or the abandon of thought... It's indeed difficult to quantify cardinally the utility of an NLP task against an other, but we can agree on an ordinality (order of magnitude) E.g do you understand that POS tagging or dependency/constictuency parsing are angular tasks needed by much of the others. Thus making them the most important NLP tasks as they enable other Nlp tasks and are the most used in practice? You think that what exactly is more important? Are you talking about text generation? Why is that important? Something important enable to solve important problems in the real world. How text generation solve any real world problem is beyond my knowledge. But if you rationally think that it's more important that angular Nlp tasks, you can probably explain why and give an example or two? Yes, an AGI will need to emit text just as humans do, indeed. But before that she needs to understund the natural language before emitting it. GPT-2 maybe capture an aesthetic of the initial input pretty well but it does not generate meaningful sentences or only by accident, so no GPT-2 does not advance the quest to create an intelligent agent mastering natural language.

do you understand that POS tagging or dependency/constictuency parsing are angular tasks needed by much of the others.

I'm not sure. I rarely have to do that explicitly in my head. Perhaps a model should learn to infer/guess them implicitly, from context, just like I do.

what exactly is more important?

In my opinion, having a world model (for common sense) and situational awareness (e.g. through sensor fusion, or from prior conversational history, or using some externally supplied conditioning) would be far more important.

GPT-2 does not generate meaningful sentences or only by accident

You think adding POS tags would help it generate meaningful sentences?

I'm not sure. I rarely have to do that explicitly in my head. Well I can't prove it but I strongly believe that our brains use part of speech too, unconsciously. Perhaps a model should learn to infer/guess them implicitly, from data. That's exactly what deep learning POS tagger do, they are far better than hard coded algorithms. SOTA has 97.96% of accuracy.

In my opinion, having a world model (for common sense) and situational awareness (e.g. through sensor fusion, or from prior conversational history, or using some externally supplied conditioning) would be far more important. Haha you basically want a general intelligence (AGI), I want it too! And not enough persons works on "architecting" such a thing. Opencog may interest you a lot then. But the reality is many other "simpler" tasks are needed to make this happen.

having a world model (for common sense) is an NLP task There are some interesting results https://github.com/sebastianruder/NLP-progress/blob/master/e... OpenAI does not work on this task sadly, at least for now.

You think adding POS tags would help it generate meaningful sentences? I would be clearly insufficient yet necessary. I believe they already use internally a POS tagger and a dependency parser.

they already use internally a POS tagger and a dependency parser.

Interesting. Where did you see that?

Well it was just a belief. I may be wrong. I asked them by curiosity https://github.com/openai/gpt-2/issues/168 So we will know.

How do you think it could be used there? A separate model just for providing tags, or the same model but trained to predict tags as well?

I was imagining using a separate model just for providing tags as they are very accurate. It would theoretically give gpt-2 useful data.

GPT-2 has not (yet) been trained to predict POS tags to my knowledge, nor BERT, or ernie 2 or xlnet has, but I think they have great potential to improve POS accuracy.

Peer review is so shit at major AI conferences that his paper was most likely rejected for nonsensical reasons

Why do you say that? Does it add anything to the conversation?

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