Deep learning job postings have collapsed in the past six months • Hacker News Board

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

#261

Post by teraku » Tue, Sep 01, 2020, 2:35 PM UTC

throwaway7281 wrote: post #164

supergeek133 wrote: post #81

I feel like it was also a classic case of running before we could crawl. Jumping from A to Z before we could go from 0 to 1. I work at an Residential IoT company, there are quite a few really valid use cases for Big Data and even ML. (Think about predictive failure). We hired more than one expensive data scientist in the past few years, and had big strategies more than once. But at the end of the day it's still "hard…

My impression too. I earn my money turning your mess into a data "landscape" - I saw people wanting to jump on the ML wagon, who did not even heard of version control for code before. Not a winter, no, but a long bumpy road ahead.

This is my job, too. And it involves so many politics, since the first thing companies do is hiring data scientists and afterwards figuring out they need clean data but they already have all these "products" that data scientists build that are a mess built ontop of a mess...

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

#262

Post by RandoHolmes » Tue, Sep 01, 2020, 3:09 PM UTC

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…

When I hear ML, deep learning, etc, I consider it a red flag for exactly the reasons you state.

It's kind of batty actually, people looking for ideas to make money just been taking old ideas and attaching ML to the side of it as if that automatically made it better. And then not educating their customers on the limitations of ML both generally and with respect to their data size.

I personally think the companies that make and sell the software that the police used to make incorrect arrests should be legally liable. Yes, the police shouldn't have blindly trusted the software, but I guaran-fucking-tee you part of why they did is the marketing from the company themselves.

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

#263

Post by fnbr » Tue, Sep 01, 2020, 3:35 PM UTC

I am a DL researcher at a top industry lab.

I'm completely unsurprised by this. Regularly, at lunch, I'll ask my coworkers if they know of any DL applications that are making O($billions), and no one knows any outside of FAANG.

FAANG is making an insane amount of money due to DL. Outside of them though, I don't know who's making money here. When I was interviewing for jobs, there were a ton of startups that were trying to do things with DL that would have been better done with a few if statements and a random forest, and that had a total market size in the millions.

I think that, eventually, there'll be a market for this stuff, but I'm not convinced that it's anywhere near being widespread.

I was also a consultant before my current role. The vast majority of non-tech firms don't have their data in well organized + cleaned databases. Just moving from a mess of Excel sheets to Python scripts + SQL databases would have made a HUGE difference to the vast majority of clients I worked with, but even that was too big of a transformation.

Basically, everyone with the sophistication to take advantage of DL/ML already has the in-house expertise to do it. There's almost no one in the intersection of "Could make $$$ doing DL" && "Has the technical infrastructure to integrate DL".

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

#264

Post by lumost » Tue, Sep 01, 2020, 4:53 PM UTC

monksy wrote: post #86

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…

> big data That's because it didn't get a chance to mature and to show how it could be powerful. People kept trying to force hadoop into it and call themselves "big data experts" We've gotten a bit more clarity in this world with streaming technologies. However, there hasn't been a good and clear voice to say "hey .. this is how it fits in with your web app and this is what you expect of it". (I'm thinking about deve…

Most buzzwords exist for consultants to sell their services. Successful buzzwords turn engineers into "Big Data Engineers" who only want to work on something called big data, and convince management that they need more "big data expertise".

In practice most of these technologies and their peers exist to support real applications, and it would be almost immediately recognizable that they are the appropriate choice when working on a similar application. You don't need a streaming engineer/big data consultant to cram them in.

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

#265

Post by mindentropy » Tue, Sep 01, 2020, 7:38 PM UTC

stjohnswarts wrote: post #181

barkingcat wrote: post #168

Earlier quoted context omitted.

This can be applied as "nobody listens to the people who actually do the work" as in company hires ML/AI experts to analyze purchase records and service records, and spits back out trends that the service front line workers (tier 1) already knew dead solid. Then the company doesn't listen to either group of people (neither tier 1 sales/support people, nor the ML people) and then fires / shuts down the entire division…

Some of the better historic manufacturers that "made it" were known to have good managers go and visit the filthy masses on the factory floor and get a feel for what's going on. It was very valuable for me when I used to help with manufacturing testing. I always spent some time with the techs and the people on the floor assembling stuff. A lot of it was useless but a lot of it was worthwhile and we learned to trust e…

I think what you have done is similar to what is depicted in "The Goal" by Eliyahu M. Goldratt. The graphic novel depicts it in a more succinct manner.

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

#266

Post by aspaceman » Tue, Sep 01, 2020, 9:34 PM UTC

mumblemumble wrote: post #166

aspaceman wrote: post #154

Earlier quoted context omitted.

There’s people who consider classical inference and the like to be machine learning just as much as neural nets are. I like that perspective.

There are some things, like OLS and logistic regression, that are commonly used for both purposes. But there's a sort of moral distinction between machine learning and statistical inference, driven by whether you consider your key deliverable to be y-hat or beta-hat, that ends up having implications. For example, I can get pretty preoccupied with multicollinearity or heteroskedasticity when I'm wearing my statisticia…

Oh gotcha. That’s an interesting way to draw the line and I appreciate the distinction.

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

#267

Post by zonethundery » Wed, Sep 02, 2020, 5:52 PM UTC

mumblemumble wrote: post #83

austinl wrote: post #74

Earlier quoted context omitted.

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 th…

Imagine what it must be like for the senior leadership of an established company to actually become data-driven. All of a sudden the leadership is going to consent to having all of their strategic and tactical decision-making be questioned by a bunch of relatively new hires from way down the org chart, whose entire basis for questioning all that expertise and business acumen is that they know how to fiddle around wit…

I've seen this happen in private industry and government, and is most of the reason analytics feels like a dead-end career if it is not the primary product of the firm.

"Data science"/analytics groups are a cost center telling management things they do not want to hear, and disrupting management narrative (with receipts). There's no point to it; either you deliver "moneyball"-like opportunities that are ignored, or torture the data to fit existing narratives. In both cases, you eventually get stabbed by an experienced bureaucratic knife fighter.

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

#268

Post by bluishgreen » Fri, Sep 04, 2020, 6:10 PM UTC

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…

You make it sound like "overfitting" is not an available concept machine learning researchers have even heard of or dealt with. If you tell me you don't have enough data that even the smallest model you can generate will still overfit, then that is another matter. But there are well known techniques and analytical processes to overcome overfitting. Also using situation specific equations is a common practice in ML and is usually referred to as Feature Engineering.

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

#269

Post by mistasteve » Tue, Sep 08, 2020, 1:45 AM UTC

ForHackernews wrote: post #165

mrtksn wrote: post #61

Earlier quoted context omitted.

If you think about it, that's the natural outcome. Why? Because people in corporations don't have the incentive to benefit the business but to progress their careers and that's done through meeting the goals for their position and make their upper ups progress with their careers too. So essentially, you have a system where people spend other people's resources for living and their success is judged by making the chai…

> It's probably the reason why startups can be much more fulfilling for deeply technical people I think the opposite is just as often true: Startups often don't have any real customers, so it's all about buzzwords and whatever razzle-dazzle they can put in a pitch deck to raise the next round.

In my experience startups have the delusion and stupidity but they burn thru the 7 stages of death faster so there's the novelty of new beginnings!

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

#270

Post by mistasteve » Tue, Sep 08, 2020, 1:47 AM UTC

austinl wrote: post #74

apohn wrote: post #43

Earlier quoted context omitted.

"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 th…

Goodhart's law is a thing...