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Deep learning job postings have collapsed in the past six months

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Re: Deep learning job postings have collapsed in the past six months

#51

Post by baron_harkonnen »

eric_b wrote: post #28

I've worked in lots of big corps as a consultant. Every one raced to harness the power of "big data" ~7 years ago. They couldn't hire or spend money fast enough. And for their investment they (mostly) got nothing. The few that managed to bludgeon their map/reduce clusters in to submission and get actionable insights discovered... they paid more to get those insights than they were worth! I think this same thing is ha…

> they paid more to get those insights than they were worth!

This understates how awful ML is at many of these companies.

I've seen quite a few companies that rushed to hire teams of people with a PhD in anything that barely made it through a DS/ML boot camp.

To prove that they're super smart ML researchers without fail these hires rush to deploy a 3+ layer MLP to solve a problem that need at most a simple regression. They have no understanding of how this model works, and have zero engineering sense so they don't care if it's a nightmare of complexity to maintain. Then to make sure their work is 'valuable' management tries to get as many teams as possible to make use of the questionable outputs of these models.

The end is a nightmare of tightly coupled models that nobody can debug, trouble shoot or understand. And because the people building them don't really understand how they work the results are always very noisy. So you end up with this mess of expensive to build and run models talking noise to each other.

When I saw this I realized data science was doomed in the next recession, since the only solution to this mess is to just remove it all.

There is some really valuable DS work out there, but it requires real understanding of either modeling or statistics. That work will probably stick around, but these giant farms of boot camp grads churning out keras models will disappear soon.

Re: Deep learning job postings have collapsed in the past six months

#53

Post by maxlamb »

bratao wrote: post #37
The_rationalist wrote: post #21

I observe the state of the art on most Nlp tasks since many years: In 2018,2019 there was huge progress made each year on most tasks. 2020,except for a few tasks have mostly stagnated... NLP accuracy is generally not production ready but the pace of progress was quick enough to have huge hopes. The root cause of the evil is: Nobody has build upon the state of the art pre trained language: XLnet while there are hundre…

I do not agree with this. I work heavily with NLP models for production in the Legal domain (where my baseline is where a 8GB 1080 must predict more than 1000 words/sec). This year was when our team glued enough pieces of Deep Learning to outperform our previous statistic/old ML pipeline that was been optimized for years. Little things compound such as optimizers ( Ranger/Adahessian), better RNN ( IndRNN, Linear Tran…

Interesting. What's the main goal(s) of your NLP models?

Re: Deep learning job postings have collapsed in the past six months

#54

Post by blaird »

eric_b wrote: post #28

I've worked in lots of big corps as a consultant. Every one raced to harness the power of "big data" ~7 years ago. They couldn't hire or spend money fast enough. And for their investment they (mostly) got nothing. The few that managed to bludgeon their map/reduce clusters in to submission and get actionable insights discovered... they paid more to get those insights than they were worth! I think this same thing is ha…

Curious if there is a correlation with companies that failed to capitalize with the ones who relied on consultants versus really reshaping their own people.

I worked for a financial services co that saw massive gains from big data/ML/AWS. Given, we were already using statistical models for everything, we just now could build more powerful features, more complex models, and move many things to more-real time, with more frequent retrains/deploys bc of cloud.

I do agree that companies who don't already recognize the value of their data and maybe rely on a consultant to tell them what to do might not be in the position to really capitalize on it and would just be throwing money after the shiny object. It really does take a huge overhaul sometimes. We retooled all of our job families from analysts/statisticians to data engineers and scientists and hired a ton of new people

Re: Deep learning job postings have collapsed in the past six months

#55

Post by cashsterling »

eric_b wrote: post #28

I've worked in lots of big corps as a consultant. Every one raced to harness the power of "big data" ~7 years ago. They couldn't hire or spend money fast enough. And for their investment they (mostly) got nothing. The few that managed to bludgeon their map/reduce clusters in to submission and get actionable insights discovered... they paid more to get those insights than they were worth! I think this same thing is ha…

I also witnesses this first hand at a Biotech company I worked at... we were using many variants of machine learning algorithms to develop predictive models of cell culture and separation processes. Problem is... the models have so many parameters in order to get a useful fit that the same model can also fit a carrot or an elephant. We found that dynamic parameter estimation on ODE/DAE/PDE system models, while harder to develop, actually worked much better and gave us real insight into the processes.

So now my advice is others is "if you can start with some first principles equation or system of equations... start there and use optimization/regression to fit the model to the data."

AND: "if you don't think such equations exist for your problem... read/research more, because some useful equations probably do exist."

This is usually pretty straightforward for engineering and science applications... equations exist or can be derived for the system under study.

In my very limited exposure to other areas of machine learning application... I have found quite a bit of mathematical science related to marketing, human behavior, etc.

Re: Deep learning job postings have collapsed in the past six months

#56

Post by insomniacity »

eric_b wrote: post #28

I've worked in lots of big corps as a consultant. Every one raced to harness the power of "big data" ~7 years ago. They couldn't hire or spend money fast enough. And for their investment they (mostly) got nothing. The few that managed to bludgeon their map/reduce clusters in to submission and get actionable insights discovered... they paid more to get those insights than they were worth! I think this same thing is ha…

My employer is big enough that I know we're doing a bunch of ML/AI and probably getting some value out of it somewhere.

However someone is trying to make robotic process automation the Next Big Thing - which I think is hysterically funny.

Re: Deep learning job postings have collapsed in the past six months

#57

Post by freyr »

joelthelion wrote: post #15

Meh, only for people who bought into the hype without real use cases. Which I agree may be numerous. In my company though, we've been applying DL with great success for a few years now, and there are at least five years of work remaining. And that's not spending any time doing research or anything fancy: just picking the low-hanging fruit.

I think many companies have real problems, but find that DL ends up being a poor solution in practice for various reasons.

You need not only real use cases, but use cases that happens to well with DL’s trade offs and limitations. I think many companies hired with very unrealistic expectations here.

Re: Deep learning job postings have collapsed in the past six months

#58

Post by discreteevent »

rjtavares wrote: post #49
nutanc wrote: post #36

AI has a business problem. Very few businesses I know actually have a deep learning problem. But they want a deep learning solution. Lest they get left out of the hype train.

Blockbuster didn't have an Internet problem.

Dentistry didn't have a sledgehammer problem and, after all these years, it still doesn't.

Re: Deep learning job postings have collapsed in the past six months

#59

Post by bratao »

maxlamb wrote: post #53
bratao wrote: post #37

Earlier quoted context omitted.

I do not agree with this. I work heavily with NLP models for production in the Legal domain (where my baseline is where a 8GB 1080 must predict more than 1000 words/sec). This year was when our team glued enough pieces of Deep Learning to outperform our previous statistic/old ML pipeline that was been optimized for years. Little things compound such as optimizers ( Ranger/Adahessian), better RNN ( IndRNN, Linear Tran…

Interesting. What's the main goal(s) of your NLP models?

We work on multiple models, all related to legal proceedings and lawsuits, such as: - Structure Judicial Federal Register texts - Identify entities in Legal texts (citation to laws, other lawsuits) - Predict time to completion, risk and amount due of a lawsuit - Classifying judicial proceedings to non lawyers

Re: Deep learning job postings have collapsed in the past six months

#60

Post by freyr »

p1esk wrote: post #47
The_rationalist wrote: post #21

I observe the state of the art on most Nlp tasks since many years: In 2018,2019 there was huge progress made each year on most tasks. 2020,except for a few tasks have mostly stagnated... NLP accuracy is generally not production ready but the pace of progress was quick enough to have huge hopes. The root cause of the evil is: Nobody has build upon the state of the art pre trained language: XLnet while there are hundre…

It'd be ironic if your comment was generated by GPT-3. But forget GPT-3. In 10 years, looking back at AI history, the year 2020 will probably be viewed as the point separating pre GPT-4 and post GPT-4 epochs. GPT-4 is the model I expect to make things interesting again, not just in NLP, but in AI.

Are any of the recent NLP advancements due to improvements beyond throwing more data and horsepower at “dumb” models? Will GPT-4 be any different?

It seems like the current approaches will always fall short of our loftier AI aspirations, but we’re reaching a level of mimicry where we can start to ask, “Does it matter for this task?”

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