For most companies ML is just part of the long term strategy, with covid priorities have shifted from long term R&D to short term survival, so I don't see anything out of the ordinary here
Deep learning job postings have collapsed in the past six months
111–120 of 274 posts
Re: Deep learning job postings have collapsed in the past six months
#112Earlier quoted context omitted.
> 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…
Just an anecdote but if you go to most baseball data departments, where there is real competition between teams, you don't just have PHds. You have people with undergrads/domain knowledge, and people with PHds.
This isn't to say that PHd knowledge isn't valuable but if you look at firms in finance that have had success with data i.e. RenTech, they hire very smart people with PHds but it isn't only the PHd. You need someone who has the knowledge AND someone who has common sense/can get results. That is very hard to do correctly (and yes, some people who come from academia literally do not want anything to do with business...it is like the devs who come from a CS PHd and insist on using complicated algo and data structure everywhere, optimising every line, etc.).
Re: Deep learning job postings have collapsed in the past six months
#113I imagine this correlates to the "blockchain" postings.
Re: Deep learning job postings have collapsed in the past six months
#114I 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…
Everyone wants to fire up Tensorflow, Keras and PyTorch these days. Fewer people want to work in Airflow and SSIS, spend days tuning ETL, etc. This is the domain of data engineering, which bridges software engineering and data science with a dash of devops. I’ve been working in this field for a couple of years and it’s clear to me that data engineering is a necessary foundation and impact multiplier for data science.
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Re: Deep learning job postings have collapsed in the past six months
#115I 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…
Everyone wants to fire up Tensorflow, Keras and PyTorch these days. Fewer people want to work in Airflow and SSIS, spend days tuning ETL, etc. This is the domain of data engineering, which bridges software engineering and data science with a dash of devops. I’ve been working in this field for a couple of years and it’s clear to me that data engineering is a necessary foundation and impact multiplier for data science.
Don't forget data cleaning. A huge issue I've seen is just getting sufficient data of a high enough quality.
Also, (for supervised classification problems) labelling is a big problem.
It is almost as if we need a "data janitor" title.
Re: Deep learning job postings have collapsed in the past six months
#116Earlier 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?”
Will GPT-4 be any different?
That's the point - it does not need to be different. If it demonstrates similar improvement to what we saw with GPT-1 --> GPT-2 --> GPT-3, then it will be enough to actually start using it. It's like the progression MNIST --> CIFAR-10 --> ImageNet --> the point where object recognition is good enough for real world applications.
But in addition to making it bigger, we can also make it better: smarter attention, external data queries, better word encoding, better data quality, more than one data type as input, etc. There's plenty of room for improvement.
Re: Deep learning job postings have collapsed in the past six months
#117I'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 think if a business is set up to scale by volume they can see gains from it. For example, say a business is already doing well at 100k conversions a day. They manage to apply "big data/ML" to optimize those conversions and gain a 3% lift, they are now making over a 1,095,000 extra conversions a year they would not have otherwise made.
Re: Deep learning job postings have collapsed in the past six months
#118I'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 don't agree, most of the low hanging fruit in ML engineering hasn't been picked yet. ML is like electricity 100 years ago, it will only expand and eat the world. And the research is not slowing down, on the contrary, it advances by leaps and bounds.
The problem is that we don't have enough ML engineers and many who go by this title are not really capable of doing the job. We're just coming into decent tools and hardware, and many applications are still limited by hardware which itself is being reinvented every 2 years.
Take just one single subfield - CV - it has applications in manufacturing, health, education, commerce, photography, agriculture, robotics, assisting blind persons, ... basically everywhere. It empowers new projects and amplifies automation.
With the advent of pre-trained neural nets every new task can be 10x or 100x easier. We don't need as many labels anymore, it works much better now.
Re: Deep learning job postings have collapsed in the past six months
#11999% of the time you don't need a deep recurrent neural network with an attention based transformer. Most times, you just need a bare-bones logistic regression with some carefully cleansed data and thoughtful, domain-aware feature engineering.
Yes, you're not going to achieve state-of-the-art performance with logistic regression. But for most problems the difference between SOTA and even simple models is not nearly as large as you might think. And two, even if you're cargo-culting SOTA techniques, it's probably not going to work unless you're at an org with an 8-digit R&D budget.
Re: Deep learning job postings have collapsed in the past six months
#120I'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…
Dyson asked Fermi about his take on his model fitting with four parameters. The reply was: I remember my friend Johnny von Neumann used to say, with four parameters I can fit an elephant, and with five I can make him wiggle his trunk.