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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

#81

Post by supergeek133 »

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" to ask a question such as "if I give you a MAC Address give me the runtime for the last 6 months".

We're trying to shoot for the moon, when all I've ever asked is I want an API to show me indoor temp for particular device over a long period.

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

#82

Post by disgruntledphd2 »

Barrin92 wrote: post #65
samfisher83 wrote: post #31

A lot of thee c folks aren't tech folks or even math folks. They want to try to use deep learning to do prediction or get some insight when something as simple as regression would have worked.

what's particularly surprised me is how effective gradient boosting is in practise. I've seen so many cases of real world applications where just using catboost or whatever worked ~95% as well or even just as well as some super complicated deep learning approach and it saves you ten times the cost

To be fair, if you're willing to write code to perform feature engineering for you, you can often replace the complicated boosting approach with a much simpler regression model.

Turtles all the way down, I guess.

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

#83

Post by mumblemumble »

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…

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 with numbers in some program called R? And all the while, they're constantly whining that this same data is junk and unreliable and we need to upend a whole bunch of IT systems just so they can rock the boat even harder? Pffft.

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

#84

Post by apohn »

mrtksn wrote: post #61
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…

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…

>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.

This is one of the reasons I roll my eyes whenever I read something like "McKinsey says 75% of Big Data/AI/Buzzword projects do not deliver any value." What's the baseline for failing and/or delivering zero value because those projects were destined to fail?

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

#86

Post by monksy »

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 developing a talk on this.. how it fits in [hint.. your microservice app shouldn't do any heavy lifting of processing data])

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

#87

Post by mywittyname »

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…

This is absolutely right. And when you think about it, the reason behind has been staring us in the face: people who want to do machine learning approach everything as a machine learning problem. It's really common to see people handwave away the "easy stuff" because they want to get credit for doing the "hard stuff."

It's not just the data scientists fault. I once heard our chief data scientist point out that they don't want to hand off a linear regression as a machine learning model -- as if a delivered solution to a problem has a minimal complexity. She absolutely had a point.

Clients are paying for a Ph.D. to solve problems in a Ph.D way. If we delivered the client a simple, yet effective solution, there's the risk of blow-back from the client for being too rudimentary. I'm certain this extends attitude extends to in-house data scientists as well. Nobody wants to be the data "scientist" who delivers the work of a data "analyst." Even when the best solution is a simple SQL query.

Our company kind of sidesteps this problem by having a tiered approach, where companies are paying for engineering, analysis, visualization, and data science work for all projects. So if a client is at the simple analysis level, we deliver at that level, with the understanding that this is the foundational work for more advanced features. It turns out to be a winning strategy, because while every client wants to land on the moon, most of them figure out that they are perfectly happy to with a Cessna once they have one.

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

#88

Post by tachyonbeam »

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…

I worked in a place full of deep learning PhDs, and you'd have people trying to apply reinforcement learning to problems that had known mathematical solutions, and integer programming problems.

I don't think the issue is just that companies hire people who are awful at ML, it's also that people are trying to shoehorn deep learning into everything, even when it currently has nothing to offer and we have better solutions already. IMHO, we're producing too many deep learning PhDs.

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

#90

Post by bonoboTP »

apohn wrote: post #84
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…

>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. This is one of the reasons I roll my eyes whenever I read something like "McKinsey says 75% of Big Data/AI/Buzzword projects do not de…

> because of silly management decisions?

The whole point is, from their point of view those decisions are rational. It's much more lucrative from their (managers') personal point of view to develop a smokes-and-mirrors looks-good-on-ppt AI project. To be safe from risk, don't give the AI people too much responsibility, let them "do stuff", who cares, the point is we can now say we are an AI-driven company on the brochures, and we have something to report up to upper management. When they ask "are we also doing this deep learning thing? It's important nowadays!" we say "Of course, we have a team working on it, here's a PPT!". An actual AI project would have much bigger risks and uncertainty. I as a manager may be blamed for messing up real company processes if we actually rely on the AI. If it's just there but doesn't actually do anything, it's a net win for me.

Note how this is not how things run when there are real goals that can be immediately improved through ML/AI and it shows up immediately on the bottom line, like ad and recommendation optimizations in Youtube or Netflix or core product value like at Tesla etc.

The bullshit powerpoint AI with frustrated and confused engineers happens in companies where the connection is less direct and everyone only has a nebulous idea of what they would even want out of the AI system (extract valuable business knowledge!).

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