161–170 of 274 posts
Something I've learned: when non-engineers ask for an AI or ML implementation, they almost certainly don't understand the difference between that and an "algorithmic" solution. If you solve "trending products" by building a SQL statement that e.g. selects items with the largest increase of purchases this month in comparison to the same month a year ago, that's still "AI" to them. Knowing this can save you a lot of wa…
I managing some teams right now that do a mix of high-end ML stuff with more prosaic solutions. The ML team is smart, and pretty fast with what they do, but they tend to (as many comments here have mentioned) focus on delivering only PhD level work. This translates into taking simple problems and trying to deorbit the ISS through a wormhole on it rather than just getting something in place that answers the problem. I…
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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…
These claims are usually high level and based on surveys or whatever. Failing usually means leadership gave up. As far as high level awareness of project success rates, it's probably accurate enough to justify the point: companies are generally bad at doing X. This tends to be true for many different kinds of X, because business is hard.
I generally don't agree that people make up destined to fail projects for selfish gains. I'm sure it happens, but that seems bottom of the barrel in terms of problems to fix. With DS specifically, leaders just don't know what to do. So they hire data scientists, and the data scientists don't know anything about the business, so they make some dashboards or whatever and nobody uses them. It's really not easy. Business is hard.
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…
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"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…
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.
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My sense is that the original sin here is conflating data science with machine learning. A good data scientist might choose to use machine learning to accomplish their job. Or they might find that classical statistical inference is the better tool for the task at hand. A good data scientist, having built this model, might choose to put it into production. Or they might find that a simple if-statement could do the job…
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.
For example, I can get pretty preoccupied with multicollinearity or heteroskedasticity when I'm wearing my statistician hat, while they barely qualify as passing diversions when I'm wearing my machine learning engineer hat. If I'm doing ML, I'll happily deliberately bias the model. That would be anathema if I were doing statistical inference.
It's tough atm.
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…
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 because "upper management didn't find value"
According to data from Revealera.com, if you normalize the data, the % of job openings that mention 'deep learning' has actually remained stable YoY: https://i.imgur.com/sDoKwD0.png * Revealera.com crawls job openings from over 10,000 company websites and analyzes them for technology trends for hedge funds.
Btw. I don't like twitter's new feature that prevents everyone from responding to a tweet that was used by @fchollet. It no longer feels like twitter if you can't engage.
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…
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…