Ask HN: Where is AI/ML actually adding value at your company? • Hacker News Board

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

#151

Post by canyon289 » Tue, Dec 13, 2016, 12:58 AM UTC

altshiftprtscrn wrote: post #94

ep103 wrote: post #52

Earlier quoted context omitted.

That's pretty awesome. What are you doing academically to lean this? Went somewhere for a masters?

I just started in UC Berkeley's MIDS program. My only two misgivings about the program thus far: It is 1) pretty expensive and 2) geared towards working professionals rather than academics, but my employer is helping pay for a good chunk of the degree and I'm more interested in acquiring the skills and tools to go solve problems in industry as opposed to doing research. Otherwise it has been great thus far. The progr…

Interesting perspective. I work in Manufacturing and have created similar models in the past and I was in the MIDS program but I dropped out. Like you it was too expensive and had other misgivings as well.

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

#152

Post by perlin » Tue, Dec 13, 2016, 1:09 AM UTC

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…

Hi, I work at a manufacturing startup on pretty much the same exact problem (reducing scrape rates and downtime). I'd love to pick your brain if you have a moment to chat :) my email is mykola@oden.io

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

#153

Post by katkattac » Tue, Dec 13, 2016, 1:22 AM UTC

We use machine learning to detect anomalies on our customers' data and alert them of potential problems. It's not fancy or cutting edge, but it provides value.

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

#154

Post by perfmode » Tue, Dec 13, 2016, 1:27 AM UTC

nl wrote: post #92

Bartweiss wrote: post #24

Earlier quoted context omitted.

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…

ML = anything where parameters are learnt from data. Yes, this means ML is "just" statistics - the distinction being that it is automated so you can run it on larger amounts of data quickly. I thought this was pretty much an accepted definition.

An accepted definition:

"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." - Tom Mitchell

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

#155

Post by mattmanser » Tue, Dec 13, 2016, 1:38 AM UTC

andrewchambers wrote: post #150

altshiftprtscrn wrote: post #146

Earlier quoted context omitted.

This is pretty spot on. We know why the defects happen and why they cause downstream test failures, but we lack the ability to prevent (all of) them. To clarify on that 95% value because it is admittedly really vague: That's actually a 95% correct prediction rate. So far we get ~2.5% false-positives and ~2.5% false-negatives. 2.5% of the parts evaluated will be incorrectly allowed to continue and will subsequently fa…

I hope you get a decent chunk of those cost savings as a reward for your effort, great job.

D'Angelo Barksdale: Nigga, please. The man who invented them things? Just some sad-ass down at the basement at McDonald's, thinkin' up some shit to make some money for the real players.
Malik 'Poot' Carr: Naw, man, that ain't right.
D'Angelo Barksdale: Fuck "right." It ain't about right, it's about money. Now you think Ronald McDonald gonna go down in that basement and say, "Hey, Mista Nugget, you the bomb. We sellin' chicken faster than you can tear the bone out. So I'm gonna write my clowny-ass name on this fat-ass check for you"?
Wallace: Shit.
D'Angelo Barksdale: Man, the nigga who invented them things still workin' in the basement for regular wage, thinkin' up some shit to make the fries taste better or some shit like that. Believe.
[pause]

Wallace: Still had the idea, though.

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

#156

Post by suanmeiguo » Tue, Dec 13, 2016, 1:47 AM UTC

johndavi wrote: post #49

We exclusively rely on ML for our core product at Diffbot: automatic data extraction from web pages (articles, products, images, discussion threads, more in the pipeline), cross-site data normalization, etc. It's interesting and challenging work, but a definite point of pride for us to be a profitable AI-powered entity.

Oh interesting. I've used diffbot and never thought Diffbot relies on AI. Could you elaborate? I thought it's a simple crawling and parsing task but I might be naive on this.

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

#157

Post by nl » Tue, Dec 13, 2016, 1:50 AM UTC

jorgemf wrote: post #130

nl wrote: post #92

Earlier quoted context omitted.

ML = anything where parameters are learnt from data. Yes, this means ML is "just" statistics - the distinction being that it is automated so you can run it on larger amounts of data quickly. I thought this was pretty much an accepted definition.

> ML = anything where parameters are learnt from data. In some ML algorithms you don't learn parameters. For example: some clustering algorithms are based on examples, not on parameters. > Yes, this means ML is "just" statistics So, a decision tree based on information theory would you call it statistics? Information theory and statistics are not clearly the same. > I thought this was pretty much an accepted definiti…

Sure, I think minimization/maximization techniques (eg, as used in clustering and tree based learners) are generally regarded as ML too.

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

#158

Post by eon1 » Tue, Dec 13, 2016, 2:21 AM UTC

lost_name wrote: post #65

Nothing in my department yet, but we actually have a guy actively looking for a reason to implement some kind of ML so we can say our product "has it" I guess.

Yep, our tech guys are constantly looking for ways to implement things that may or may not be useful, or even understood; we've just gotta be able to say we have the latest in machine learned blockchain-based buzzword doodads to constantly reinforce our reputation as the most "high tech" organisation in our sector.

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

#159

Post by shmageggy » Tue, Dec 13, 2016, 2:29 AM UTC

rakmial wrote: post #91

Bartweiss wrote: post #24

Earlier quoted context omitted.

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…

Actually there's a very clear definition of what types of problems ML ought be used for, and that category of problem is what defines it. Those familiar with regression (and stats in general) ought to be familiar with it already - it's an issue of relationship of datatype between independent and dependent variable. In brief, you're going to run up against two types of data - categorical and continuous. (There are fac…

If I'm reading this correctly, it's just wrong. Whatever the distinction between data analysis and ML might be, it is more than just whether your data and predicted quantities are discrete or continuous.

> Categorical Independent vs. Categorical Dependent, for example, is fairly restrictive, as makes logical sense. You may cross-tabulate, you may score likelihood based on previous observation, but obviously, because all of the data involved are non-numeric, there's no chance for regression, ANOVA, etc

If you are implying that categorical -> categorical predictions are not ML: as a counter example, natural language is a categorical (words) input that could be used to predict any number of categorical variables (parse trees, semantic categories, etc). I think it's safe to say that the field of NLP is doing machine learning.

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

#160

Post by AustinBGibbons » Tue, Dec 13, 2016, 2:54 AM UTC

I work at Periscope Data - we do our own lead scoring using home-baked ML through SciPy. It was interesting to see it play out in the real-world - interpretation of features/parameters was definitely important to the people driving the marketing/sales orgs.

We also support linear regression in the product itself - it was actually an on-boarding project for one of the engineers who joined this year, and he wrote a blog post to show them off: https://www.periscopedata.com/blog/movie-trendlines.html About 1/3rd of our customers are using trendlines, which is pretty good, but we haven't gotten enough requests for more complex ML algorithms to warrant focusing feature development there yet.