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?

#191

Post by altshiftprtscrn » Wed, Dec 14, 2016, 1:48 PM UTC

canyon289 wrote: post #151

altshiftprtscrn wrote: post #94

Earlier quoted context omitted.

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.

Care to elaborate at all on those additional misgivings? One thing I could see is that the material might not be very mind-blowing to someone who already has a software background.

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

#192

Post by altshiftprtscrn » Wed, Dec 14, 2016, 2:00 PM UTC

aaachilless wrote: post #104

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…

Disclaimer: I have no experience implementing any kind of ML. How easy will it be to update your model if/when the downstream process changes? At a previous job we had a process that relied heavily on visual inspection from employees. I often considered applying ML to certain inspection steps, but always figured it would be most useful for e.g. final inspection to avoid having to update the models frequently as the p…

That's an interesting concern that I hadn't considered if I'm understanding you correctly. I'm imagining you could have a situation where a downstream process change helps mitigate the effect of the upstream defect. In that situation your measure of what constitutes good and bad parts will need to change in the ML model.

I think I'm somewhat lucky in that with my product downstream processes are unlikely to change in a significant enough way to warrant "retraining" the model, but I guess that's probably the only way to handle that - retrain in the event of a significant process change. Our product stays fairly stable once it releases to production and the nature of the downstream processes is that they would have very little effect on the perceived severity of the defect at the final electrical test.

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

#193

Post by altshiftprtscrn » Wed, Dec 14, 2016, 2:04 PM UTC

shas3 wrote: post #67

whistlerbrk wrote: post #37

Earlier quoted context omitted.

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

Surely it would be guarded as a trade secret, as it usually happens in large companies.

Yup - To do a proper write-up that would actually be interesting to read would require divulging IP.

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

#194

Post by altshiftprtscrn » Wed, Dec 14, 2016, 2:41 PM UTC

rrherr wrote: post #179

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…

Nice! You might like these links too. "Machine Learning Meets Economics", uses manufacturing quality as an example. http://blog.mldb.ai/blog/posts/2016/01/ml-meets-economics/ http://blog.mldb.ai/blog/posts/2016/04/ml-meets-economics2/

This is awesome - thank you! I went through a similar exercise described in your link in evaluating the utility of the tool I described above. This is a nice write up of the logic.

In my case the % occurrence of the defect was very high and the False-Positive cost is also very high so my tool could provide value without being too stellar of a model.

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

#195

Post by altshiftprtscrn » Wed, Dec 14, 2016, 4:10 PM UTC

taejo wrote: post #180

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…

> 2.5% of parts evaluated will be wrongly identified as scrap by the model and tossed 2.5% of what, though? if only 1 in a million parts are actually bad, you're still tossing many more good parts than bad parts.

Correct - as mentioned the cost savings still work out. The defect rate is around 30% (Nowhere close to 1 in a million).

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

#196

Post by ilikeatari » Wed, Dec 14, 2016, 4:11 PM UTC

pacey wrote: post #188

ilikeatari wrote: post #71

We leverage machine learning in the asset replacement modeling space. Basically there is an optimum time to sell your vehicle and purchase a new one based on our model. Our company works with large fleet organizations and provides analytics suite for vehicle replacement, mechanic staffing, benchmarking, telematics and other aspects of fleet management.

Is this useful for individuals also? I would really like to know the optimal time to sell my car. Or is this more like chart analysis which only works as long as the people having access to that information is limited?

Theoretically it could be, but most fleets gather much more data about their vehicles than an average consumer. For example all repairs, parts and labor costs, maintenance, mileage, engine hours and much much more. In addition, majority now leverage telematics which greatly improves the resolution and depth of this data. This data is quite necessary to make our model work. From high level perspective though most consumers sell their vehicles way before that optimal time frame.

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

#197

Post by got2surf » Thu, Dec 15, 2016, 5:33 PM UTC

activatedgeek wrote: post #175

got2surf wrote: post #77

My company builds software to analyze customer feedback. We use "real" ML for sentiment classification, as well as some of our natural language processing and opinion mining tools. However, most of the value comes from simple statistical analysis/probabilities/ratios, as other commenters mentioned. The ML is really important for determining that a certain customer was angry in a feedback comment, but less important i…

What do you mean by "real"?

Sorry, using "real" in quotes wasn't too descriptive.

A few machine learning-based classifiers (we've used Bayesian and SVM approaches). Word embeddings and topic modeling (similar to word2vec) which are based on shallow neural networks.

Those are a few of what I would consider the "real" machine learning tools we use. Most of the application, though, is statistics/pattern recognition/visualizations on top of the data calculated by the ML approaches.

The interesting thing is (in my opinion/experience) that a 10% improvement in some of the ML performance (a 10% increase in accuracy, for example) will translate to a 1-3% improvement in end user experience (they see slightly better insights and patterns, but it is a marginal improvement). On the other hand, layering a new visualization or statistical heuristic on top of the data can lead to a significant boost in user experience.

Again, this is just for our specific application/domain, but we focus on making the ML results more accessible to users instead of focusing on the marginal accuracy of the ML results themselves.

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

#198

Post by yummyfajitas » Sun, Dec 18, 2016, 4:31 PM UTC

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…

The best distinction I've found between ML and statistics is the following.

Statistics is about modelling the underlying probability distribution that generates your data. A convergence/generalization/etc result will usually be dependent on this underlying distribution.

ML is when you don't care much about the underlying distribution (modulo regularity assumptions), and your model doesn't even come from the same family at all.

I.e. linear regression is usually statistics, because you often believe the underlying data looks like f(x) ~ f(x0) + f'(x0)(x-x0)). Random forests are machine learning because you don't actually think the real world secretly has a random forest flotaing around.

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

#199

Post by kmikeym » Tue, Dec 20, 2016, 12:41 AM UTC

eon1 wrote: post #158

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.

Sounds like you work at Xerox!

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

#200

Post by iasondemiros » Thu, Dec 22, 2016, 11:04 AM UTC

Here at Qualia (qualia.ai) we process mostly textual data from online sources (news, blogs, social media, internal data). Our background is in NLP when back in the days AI meant deep parsing, HPSG, tree-adjoining grammars, synsets, frames and speech acts, discourse, and different flavors of knowledge representations. It also meant LISP and Prolog. The domain quickly evolved from knowledge and rule-based to data-driven and statistical, mostly thanks to Brown and the IBM MT team in the 90s (that are now part of the Renaissance Fund).

We use hierarchical clustering for topic detection. We also work on topic models (Blei and his legacy). We use word embeddings for information retrieval and various ML algorithms for different applications of mood and emotional learning: Bayes, SVM, Winnow (linear models) and sometimes decision trees and lists. We also learn from past events and crises in order to create models, mostly statistical, and try to estimate how an event might evolve in the future. We have also tried graph-based community detection algorithms on Twitter (min-cut). Finally we have experimented with non-linear statistical analysis on micro-blogging data, by applying methods such as correlation functions, escape times, and multi-step Markov chains (but with limited success).

I 'd like to add here that I feel ML is well defined (supervised, semi-supervised, unsupervised and using unlabeled data), statistical learning is more fuzzy (a good starting point is Vapnik's work) and regarding AI, I am not sure I know any more what it means! I am always open to discussion and ideas. Let me know.