Anyone else witnessing a panic inside NLP orgs of big tech companies? • Hacker News Board

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#101

Post by lisasays » Thu, Mar 16, 2023, 8:22 PM UTC

hnbad wrote: post #26

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…

Given that Google Translate already exists, what is even the point of all of this?

Because for the other 20 percent it's plainly -not- good enough. It can't even produce an acceptable business letter in a resource-rich target language, for example. It just gets you "a good chunk of the way there."

And there's no evidence that either (1) throwing exponentially more data at the problem with see matching gains in accuracy or (2) this additional data will even be available.

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#102

Post by steponlego » Thu, Mar 16, 2023, 8:24 PM UTC

djous wrote: post #18

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…

Why start startup of any kind if there's a bigger company full of people already competing in the same space?

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#103

Post by dserban » Thu, Mar 16, 2023, 8:25 PM UTC

The PR folks at my current company are in full panic mode on Linkedin, judging from the passive-aggressive tone of their posts (sometimes very nearly begging customers not to use ChatGPT and friends).

They fully understand that LLMs are stealing lunch money from established information retrieval industry players selling overpriced search algorithms. For a long time, my company was deluded about being protected by insurmountable moats. I'm watching our PR folks going through the five stages of grief very loudly and very publicly on social media (particularly noticeable on Linkedin).

Here's a new trend happening these days. Upon releasing new non-fiction books to the general public, authors are simultaneously offering an LLM-based chatbot box where you can ask the book any question.

There is no good reason this should not work everywhere else, in exactly the same way. Take for example a large retailer who has a large internal knowledge base. Train an LLM on that corpus, ask the knowledge base any question. And retail is a key target market of my company.

Needless to say I'm looking for employment elsewhere.

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#104

Post by sp332 » Thu, Mar 16, 2023, 8:27 PM UTC

hnfong wrote: post #53

hnbad wrote: post #26

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…

English Wikipedia is the largest. Wikipedia in other languages would be less useful.

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#105

Post by thwayunion » Thu, Mar 16, 2023, 8:27 PM UTC

matthewdgreen wrote: post #40

echelon wrote: post #8

Earlier quoted context omitted.

Get out of academia and into industry. Why the hell stay in in academia? This is clearly the next technological wave, and you shouldn't sleep on it. Especially when you're so well positioned to take advantage of your experience. You can make $500,000/yr (maybe more with all the new startups and options) and be on the bleeding edge. If you want to go back to academia later, you can comfortably do so. Most don't, but t…

If you go into industry you’ll be given a chance to deploy these models and rush them into products. You’ll also make good money. If you go into academia (or research, whether it’s in academia or industry) you’ll be given the chance to try to understand what they’re doing. I can see the appeal of making money and rushing products out. But it wouldn’t even begin to compete with my curiosity. Makes me wish I was younge…

Do both.

Getting your hands dirty is the best way to understand how something works. Think about all the useless SE and PL work that gets done by folks who never programmed for a living, and how often faculty members in those fields with 10 yoe in industry spend their first few years back in academia just slamming ball after ball way out of the park.

More importantly, $500K gross is $300K net. Times 5 is $1.5, or time 10 is $3M. That's pretty good "fuck you" money. On top which some industry street cred allows new faculty to opt out of a lot of the ridiculous BS that happens in academia. Seen this time and again.

I think the easiest and best path for a fresh NLP phd grad can do right now is find the highest paying industry position, stick it out 5-10 years, then return as a profess of practice and tear it up pre-tenure (or just say f u to the tenure track because who needs tenure when you've got a flush brokerage account?)

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#106

Post by tomp » Thu, Mar 16, 2023, 8:28 PM UTC

MomoXenosaga wrote: post #28

echelon wrote: post #22

Earlier quoted context omitted.

> Honestly, I can't wait for GPT and other productivity tools to wrech havock upon the tech labour market. Some people in tech really need to be taken down a notch or two. That's an odd reason to want this.

Less bullshit jobs. Society needs doctors, nurses, plumbers and teachers not tech bros.

With this kind of mindset, we'd still be using lead pipes and letting blood.

Doctors and plumbers might make society work, but technology drives society forward.

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#107

Post by dongobread » Thu, Mar 16, 2023, 8:30 PM UTC

I worked in a research capacity in the voice assistant org of a big tech company until very recently. There was a lot of panic when ChatGPT came out, as it became clear that the vast bulk of the org's modeling work and research essentially had no future. I feel bad for some of my colleagues who were really specialized in specific NLP technology niches (e.g. building NLU ontologies) which have been made totally obsolete by these generalized LLMs.

Personally - I'm moving to more of a focus on analytical modeling. There is really nothing interesting about deep learning to me anymore. The reality is that any new useful DL models will be coming out of mega-teams in a few companies, where improving output through detailed understanding of modeling is less cost effective than simply increasing data quality and scale. Its all very boring to me.

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#108

Post by avmich » Thu, Mar 16, 2023, 8:33 PM UTC

danaris wrote: post #57

djous wrote: post #18

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…

> Honestly, I can't wait for GPT and other productivity tools to wrech havock upon the tech labour market. Some people in tech really need to be taken down a notch or two. You have to remember that when these sorts of things happen, the ones who get "taken down" in ways that actually affect their lives are invariably the ones who already have the least. The ones who "need" that takedown will be just fine, unless they…

> the ones who get "taken down" in ways that actually affect their lives are invariably the ones who already have the least

I'm not sure that was the case with personal computing in 1980-s. What was the significant part of society which had the least and got "taken down"?

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#109

Post by mashygpig » Thu, Mar 16, 2023, 8:33 PM UTC

dserban wrote: post #103

The PR folks at my current company are in full panic mode on Linkedin, judging from the passive-aggressive tone of their posts (sometimes very nearly begging customers not to use ChatGPT and friends). They fully understand that LLMs are stealing lunch money from established information retrieval industry players selling overpriced search algorithms. For a long time, my company was deluded about being protected by ins…

> Here's a new trend happening these days. Upon releasing new non-fiction books to the general public, authors are simultaneously offering an LLM-based chatbot box where you can ask the book any question.

Can you link to an example?

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#110

Post by macinjosh » Thu, Mar 16, 2023, 8:34 PM UTC

Is the compute for running an LLM cheap enough to scale at the moment? LLMs seem to be a great generalist solution but could specifically targeted NLP solutions still outperform in terms of speed/cost when you are processing high volumes of inputs?