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Ask HN: Where is AI/ML actually adding value at your company?

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Re: Ask HN: Where is AI/ML actually adding value at your company?

#31

Post by altshiftprtscrn »

I work in manufacturing. We have an acoustic microscope that scans parts with the goal of identifying internal defects (typically particulate trapped in epoxy bonds). It's pretty hard to define what size/shape/position/number of particles is worthy of failing the device. Our final product test can tell us what product is "good" and "bad" based on electrical measurements, but that test can't be applied at the stage of assembly where we care to identify the defect.

I recently demonstrated a really simple bagged-decision tree model that "predicts" if the scanned part will go on to fail at downstream testing with ~95% certainty. I honestly don't have a whole lot of background in the realm of ML so it's entirely possible that I'm one of those dreaded types that are applying principles without full understanding of them (and yes I do actually feel quite guilty about it).

The results speak for themselves though - $1M/year scrap cost avoided (if the model is approved for production use) in just being able to tell earlier in the line when something has gone wrong. That's on one product, in one factory, in one company that has over 100 factories world-wide.

The experience has prompted me to go back to school to learn this stuff more formally. There is immense value to be found (or rather, waste to be avoided) using ML in complex manufacturing/supply-chain environments.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#32

Post by sidlls »

AndrewKemendo wrote: post #23
sidlls wrote: post #21

Earlier quoted context omitted.

No, we actually use ML. We just don't need to, in my opinion, because the problems our products solve are more or less solvable without these techniques. My point was that using ML, even though we don't need to, "adds value" by virtue of the hype train. We need ML to sell products, not to create them. I do agree that this sort of arrangement lends itself to supporting skepticism around AI and ML. On the other hand I…

Got it. Thanks for the clarification. It is true that people are using ML where other, simpler options are available, but I wouldn't immediate discount the value of using nets for your problem. I don't know enough about your problem/implementation to speak to it really.

Yes, thanks for highlighting the deficiency in my original post. I can see it is easily interpretated as you did. I added a clarification (or what I hope is one).

Re: Ask HN: Where is AI/ML actually adding value at your company?

#34

Post by yessql »

Bartweiss wrote: post #27
jc4p wrote: post #12

I think a lot of the real benefits from ML "at work" is more in just cleaning of data and running through the gauntlet of simplest regressions (before jumping onto something more magical whose outputs and decision making process you can't exactly explain to someone). I would classify something like this blog post as ML, would you? http://stackoverflow.blog/2016/11/How-Do-Developers-in-New-Y...

When people talk about the growth (or sometimes 'excess') of ML solutions these days, I always wonder about this. A basic linear regression probably isn't ML, a backprop neural net clearly is, but somewhere between the two is a very fuzzy line between "statistics and data cleaning" and "actually machine learning". I think a lot of people have just pushed the ML angle of an already-reasonable approach to tie into that…

ML courses often start with linear regression, and if you build up complicated polynomials to find a nonintuitive model of your problem, I would definitely consider that machine learning.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#35

Post by ekarulf »

Amazon Personalization.

We use ML/Deep Learning for customer to product recommendations and product to product recommendations. For years we used only algorithms based on basic statistics but we've found places where the machine learned models out perform the simpler models.

Here is our blog post and related GitHub repo: https://aws.amazon.com/blogs/big-data/generating-recommendat... https://github.com/amznlabs/amazon-dsstne

If you are interested in this space, we're always hiring. Shoot me an email ($my_hn_username@amazon.com) or visit https://www.amazon.jobs/en/teams/personalization-and-recomme...

Re: Ask HN: Where is AI/ML actually adding value at your company?

#37

Post by whistlerbrk »

altshiftprtscrn wrote: post #31

I work in manufacturing. We have an acoustic microscope that scans parts with the goal of identifying internal defects (typically particulate trapped in epoxy bonds). It's pretty hard to define what size/shape/position/number of particles is worthy of failing the device. Our final product test can tell us what product is "good" and "bad" based on electrical measurements, but that test can't be applied at the stage of…

This is brilliant, would love to read a full write up on it. I hope you get a big raise.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#38

Post by jorgemf »

Bartweiss wrote: post #24
sidlls wrote: post #7

The entire product I built over the last year can be reduced to basic statistics (e.g. ratios, probabilities) but because of the hype train we build "models" and "predict" certain outcomes over a data set. One of the products the company I work for sells more or less attempts to find duplicate entries in a large, unclean data set with "machine learning." The value added isn't in the use of ML techniques itself, it's…

It's interesting to me that with all the ML hype, it's still not clear what constitutes ML. A basic k-means or naive Bayes approach will show up in ML textbooks, but those aren't clearly different from "use some statistics to make a prediction". There's an interesting group of marginal approaches that have existed as-is for years, but have increasingly focused their branding on machine learning as its profile has ris…

> but those aren't clearly different from "use some statistics to make a prediction"

You can reduce 90% of ML to this. Even neural networks are based on statistics.

If I have to draw a line between statistics and ML is that ML learns, it means it can predict things, however statistics only gives you information about the data you have. But for sure statistics and ML overlap a lot.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#39

Post by sidlls »

yessql wrote: post #34
Bartweiss wrote: post #27

Earlier quoted context omitted.

When people talk about the growth (or sometimes 'excess') of ML solutions these days, I always wonder about this. A basic linear regression probably isn't ML, a backprop neural net clearly is, but somewhere between the two is a very fuzzy line between "statistics and data cleaning" and "actually machine learning". I think a lot of people have just pushed the ML angle of an already-reasonable approach to tie into that…

ML courses often start with linear regression, and if you build up complicated polynomials to find a nonintuitive model of your problem, I would definitely consider that machine learning.

I wouldn't. I'd call it "basic computational statistics." But I think I might be in the minority on that.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#40

Post by [deleted] »

jakozaur wrote: post #6

At Sumo Logic we do "grep in cloud as a service". We use machine learning to do pattern clustering. Using lines of text to learn printfs they came from. The primary advantage for customer is easier to use and troubleshoot faster. https://www.sumologic.com/resource/featured-videos/demo-sumo...

[deleted]
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