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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

#71

Post by blaird »

apohn wrote: post #64
blaird wrote: post #54

Earlier quoted context omitted.

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 mor…

>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've worked in Data Science customers facing roles for 2 companies, and one anecdotal correlation between success with Stats/ML/AI I've seen is how "Data Driven" people really are for their daily decision making. The more data driven you are, the more likely you…

Yep, agreed. If decisions can be made by a human often they'll stick to that, often arguing there is no need for data.

In my former space (credit card fraud detection and underwriting), you obviously need a data driven solution. Without even considering latency requirements, you aren't do 6-10B manual decisions/year. The rationale for a more complex ML approach is easier to prove the ROI for, given the need is already there, just with an inferior technical solution.

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

#72

Post by eanzenberg »

This needs to be normalized to “job posting collapse in the past 6 months” unless you expect DL jobs to grow while everything shrinks? I’m somewhat surprised by the analysis from someone’s who’s “data driven.” I mean, he even says so as much in the twitter thread:

“To be clear, I think this is an economic recession indicator, not the start of a new AI winter.”

So, looks like he discovered an economic recession.

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

#73

Post by eanzenberg »

make3 wrote: post #27
itg wrote: post #14

Earlier quoted context omitted.

He labels anyone who criticizes him as a troll. Unfortunately he is a public figure in the ML space and does have his share of trolls, but doesn't take too well to even well thought out replies.

he's so French, in the worse way possible. I say that as a French person myself

Also his analysis is shoddy. He shows an absolute decrease in DL job postings since covid hit, and claims that DL is in decline irrespective if other fields like SWE are also in a similar decline. Utterly surprised by the analysis given the data.

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

#74

Post by austinl »

apohn wrote: post #43
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…

"Like Big Data, I think we'll see a few companies execute well and actually get some value, while most will just jump to the next shiny thing in a year or two." Here's another aspect - in many places nobody listens to the actual people doing the work. In my last job I was hired to lead a Data Science team and to help the company get value of Stats/ML/AI/DL/Buzzword. And I (and my team) were promptly overridden on eve…

I've heard this happen in a lot of places — companies want to be "data-driven", but then leadership simply ignores the data. I think being data-driven is something that is built into company culture, or otherwise it's too easy to just ignore the results and ship.

The place I currently work is data-driven (perhaps to a fault). Every change is wrapped behind an experiment and analyzed. Engineers play a major role in this process (responsible for analysis of simple experiments), whereas the data org owns more thorough, long-term analysis. This means there are a significant number of people invested in making numbers go up. It also means we're very good at finding local maxima, but struggle greatly shipping larger changes that land somewhere else on the graph.

Some of the best advice I've heard related to this is for leadership to be honest about the "why". Sometimes we just want to ship a redesign to eventually find a new maximum, even through we know it will hurt metrics for a while.

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

#76

Post by disgruntledphd2 »

baron_harkonnen wrote: post #51
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…

And this is a good thing!

To be fair, I started to understand why developers gave out about bootcamp grads lacking a foundation when the bootcamps came for my discipline (data science).

The PhD fetish is pretty mental (even though I have one), as it's really not necessary.

Additionally, everyone thinks they need researchers, when they really, really don't.

Having worked with researchy vs more product/business driven teams, I found that the best results came when a researchy person took the time to understand the product domain, but many of them believe they're too good for business (in which case you should head back to academia).

What you actually need from an ML/Data Science person:

- Experience with data cleaning (this is most of the gig)

- A solid understanding of linear and logistic regression, along with cross-validation

- Some reasonable coding skills (in both R and Python, with a side of SQL).

That's it. Pretty much everything else can be taught, given the above prerequisites.

But it's tricky for hiring managers/companies as they don't know who to hire, so they end up over-indexing on bullshitters, due to the confidence, leading to lots of nonsese.

And finally, deep learning is good in some scenarios and not in others, so anyone who's just a deep learning developer is not going to be useful to most companies.

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

#77

Post by alephu5 »

cashsterling wrote: post #55
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…

I completely agree with this sentiment, I've seen a lot of people throw ML at problems because they don't know much mathematics. Especially when you have a lot of data, I can understand the allure of just wiring up the input & output to generate the model.

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

#78

Post by mritchie712 »

occamrazor wrote: post #75

Missing in the original chart/data: have ML/DL job postings decrease more or less than other comparable job categories (programming, business analyst, etc.)

Great point. Not as good point: is looking for pytorch and tf the right measure?

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

#79

Post by disgruntledphd2 »

freyr wrote: post #60
p1esk wrote: post #47

Earlier quoted context omitted.

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?”

No, almost all the progress is driven by bigger GPUs and datasets.

To be fair, things like CNN's and BERT were definitely massive improvements, but a lot of modern AI is just throwing compute at problems and seeing what sticks.

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