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I'm not at a big tech company, and we don't sell algorithms, but my team does use a lot of NLP stuff in internal algorithms. The only panic I have is trying to keep up and take the time to learn the new stuff. If anything, things like GPT-4 are going to make my team 10x more successful without having to hire an army of PhDs.
During my master's degree in data science, we had several companies visit our faculty to recruit students. Not a single one was a specialized NLP company, but many of them had NLP projects going on. Most of those projects were the usual "solution looking for a problem to solve". Even those projects that might have had _some_ utility, would have been way more effective to buy/license a product than to develop an in-ho…
>> "solution looking for a problem to solve" I wonder if this is a bad as everyone thinks. When a new technology arrives which is not completely understood, isn't the right approach to try to find some applications for it? Sure, most will fail, but some valid use cases will likely emerge. I'm pretty sure almost all technologies at some point were solutions looking for a problem to solve. Examples include the internet…
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All these people don't understand how hireable and desirable they are now. They need to get out of academia and plugged into AI positions at tech companies and startups. Their value just went up tremendously, even if their PhD thesis got cancelled. Easily millionaires waiting to happen. --- edit: Can't respond to child comment due to rate limit, so editing instead. > That is not how it works at all. Speak for yoursel…
That is not how it works at all. You won't get hired if you don't have the academic pedigree in the first place. That means a completed Ph.D and good publications in good journals.
Is the entire field of data science (Itself maybe a decade old in terms of being a college major?) now obsolete, in terms of being a distinct job field? Are all data science majors now going to be "just" coming up with the proper prompts to get GPT to correctly massage datasets?
I'm not at a big tech company, and we don't sell algorithms, but my team does use a lot of NLP stuff in internal algorithms. The only panic I have is trying to keep up and take the time to learn the new stuff. If anything, things like GPT-4 are going to make my team 10x more successful without having to hire an army of PhDs.
They may panic, but they shouldn't. They can quickly pivot. GPT programs can be used off the shelf, but they can also use custom training. Every large org has a huge internal set of documents, plus a large external set of documents relevant to its work (research articles, media articles, domain relevant rules and regulations). They can train a GPT bot to their particular codebase. And that is now. Soon (I'd give it a…
When I was studying Computational Linguistics I kept running into the unspoken question: given that Google Translate already exists, what is even the point of all of this? We were learning all these ideas about how to model natural language and tag parts of speech using linguistic theory so we could eventually discover that utopian solution that would let us feed two language models into a machine to make it perfectl…
I learnt some very basics of computational linguistics since it was related to a side project. I kept wondering why people were spending huge amounts of resources into tagging and labelling corpora of thousands of words, while to me it seems that in theory it should be possible to feed wikipedia (of a certain language) into a program and have it spit out some statistically correct rules about words and grammar. I gue…
We're a bit more specialised than these new models. But that's it, really.
As one of the comments on reddit posts - it's not just big tech companies, but also entire university teams which feel the goalposts moving miles ahead all of a sudden. Imagine working on your PhD on chat bots since start of 2022. Your entire PhD topic might be irrelevant already...
And aren’t PhDs supposed have a theoretical underpinning?
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Fellow German here. Funny thing about DeepL: It translates "pathetisch" as "pathetic". For example: "Das war eine pathetische Rede." -> "That was a pathetic speech." I guess we have to get used to software redefining the meaning of words. It was kind of funny when that happened regarding Google Maps / neighborhood names, but with LLMs it's a different ballgame.
Pathetic can mean emotional in English as well. Though I only discovered that by reading the dictionary. For anyone who doesn't speak German, pathetisch means with pathos, impassioned.
During my master's degree in data science, we had several companies visit our faculty to recruit students. Not a single one was a specialized NLP company, but many of them had NLP projects going on. Most of those projects were the usual "solution looking for a problem to solve". Even those projects that might have had _some_ utility, would have been way more effective to buy/license a product than to develop an in-ho…
those projects were just PR so that c-levels could sell how they were preparing their company for a digital world This is exactly it. The 2017-2019 corporate version of "invest in AI" meant to build an in-house team to do ML experiments on internal data, and then usually evolved a bit to get some "ml-ops" thrown in so they could "deploy" the models they built. I spent some time with a few companies doing this and it…
Disagree. I was on one of these R&D/prototyping teams running ML experiments and you're right, it was the company wanting to present itself as future-leaning, ready to adapt, and I would say that at this point it was a good move to have employees who understand where the tech is going.
Companies with internal teams that are able to implement open source models are in a much better negotiating position for the B2B contracts they're looking at for integrating GPT into their workflow, they won't need GPT as much, if they can fallback on their own models, and they will be better able to sit down with the sales engineers and call bullshit when they're being sold snake oil.