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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?

#121

Post by NumberCruncher »

In my last job at a big telco I was working with/on a scorecard driven next-best-offer system steering 80-90% of all outbound callcenter activities. I would not call it AI/ML because the scorecards were built with good old logistic regression and were pretty old (bad) but the process made us 25 M €/year (calculated NPV). I don't know how much of it was added by the scoring process. We also had a real-time system for SMS marketing built on the top of the same next-best-offer system making 12+ M €/year (real profit).

On the other hand I found an internal fraud costing us 2-3 M €/year applying only the weak law of big numbers. Big corp, big numbers.

Now I build a similar system for a smaller company. I think we will stick mainly to logistic regression. I actually use "neural networks" with hand-crafted hidden layers to identify buying patterns in our grocery store shopping cart data. It works pretty well from a statistical point of view but it is still a gimmick used to acquire new b2b partners.

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

#123

Post by Schwolop »

Once an analyst has manually reviewed something, a software system updates a row in a database to mark it as done. Our marketing team calls this machine learning, because the system "learns" not to give analysts the same work twice.

We also use ML to classify bittorrent filenames into media categories, but it's pretty trivial and frankly the initial heuristics applied to clean the data do more of the work than the ML achieves.

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

#124

Post by jorgemf »

Bartweiss wrote: post #46
jorgemf wrote: post #38

Earlier quoted context omitted.

> 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.

Even that doesn't seem like a clear distinction? If you ask me for the most likely new value for a dataset, I won't know. But if I graph a few things and then write a function to spit back the current mean or median, is that machine learning? I'm not trying to be snarky there, I agree that the bulk of ML tools are fundamentally just statistical tricks with some layer of abstraction. As a result, I have a lot of troub…

> Even that doesn't seem like a clear distinction?

Obviously no, ML uses statistics as statistics uses Maths. But not all ML uses statistics, some algorithms are biological inspired (swarm optimization) other uses theory of information for classification.

The point of ML is you learn something from data, not necessarily with statistics, although it is used in a lot of algorithms. But also function optimization is used in a lot of algorithms. The boundaries are very fuzzy, but for sure not all ML uses statistics and not all statistics are ML.

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

#125

Post by bluetwo »

antognini wrote: post #120
bluetwo wrote: post #100

Earlier quoted context omitted.

I was wondering the other day if anyone had applied this technology to EKGs. Do you also do that?

Funny you should ask, detecting QRS complexes has been my first project since starting here. I know of a few papers where the authors have applied neural networks to EKGs, but the applications have been purely academic. I'm not aware of any other companies that use NNs in practice. (There may well be some, but they tend to be secretive about how their algorithms work.) At any rate, the false positive rate of our soft…

Congrats on your application. Sounds very useful.

And thanks for the info. I worked years ago on a training program for EKGs and it seemed like a field ripe for application of ML and AI.

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

#126

Post by jorgemf »

marcosdumay wrote: post #78
jorgemf wrote: post #38

Earlier quoted context omitted.

> 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.

> it means it can predict things All the other Math areas call that kind of prediction by "interpolation". It's not a magical property that only ML has. I'd draw the line by the name. An algorithm is ML if it includes the computer deriving a complex model based on data gathered on the field.

ML and statistics are a subset of maths. As I said the statistics and ML overlap and also function interpolation. But some ML algorithms are based on biological systems (like swarm optimization), or theory of information.

If you have a problem that you want to classify some vectors, you have different ways to do it. You call all of them ML, but some use statistics, others use interpolation, other uses theory of information, etc. The model doesn't have to be complex or require a lot of data. Instead of saying all the different techniques you sum up saying ML.

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

#127

Post by jorgemf »

collyw wrote: post #113
jorgemf wrote: post #38

Earlier quoted context omitted.

> 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.

Predicting things seems to be the primary purpose of statistics in many cases.

I don't think so. I think it is more similar to this description: https://www.isixsigma.com/tools-templates/sampling-data/stat...

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

#128

Post by Radim »

I run a company that specializes in design & implementation of kick-ass ML solutions [1]. We've had successful projects in quite a few industries at this point:

LEGAL INDUSTRY

Aka e-discovery [2]: produce digital documents in legal proceedings.

What was special: stringent requirements on statistical robustness! (the opposing party can challenge your process in court -- everything about way you build your datasets or measure the production recall the has to be absolutely bullet proof)

IT & SECURITY

Anomaly detection in system usage patterns (with features like process load, frequency, volume) using NNs.

What was special: extra features from document content (type of document being accessed, topic modeling, classification).

MEDIA

Built tiered IAB classification [3] for magazine and newspaper articles.

Built a topic modeling system to automatically discover themes in large document collections (articles, tweets), to replace manual taxonomies and tagging, for consistent KPI tracking.

What was special: massive data volumes, real-time processing.

REAL ESTATE

Built a recommendation engine that automatically assembles newsletters, and learns user preferences from their feedback (newsletter clicks), using multi-arm bandits.

What was special: exploration / exploitation tradeoff from implicit and explicit feedback. Topic modeling to get relevant features.

LIBRARY DISCOVERY

Built a search engine (which is called "discovery" in this industry), based on Elasticsearch.

What was special: we added a special plugin for "related article" recommendations, based on semantic analysis on article content (LDA, LSI).

HUMAN RESOURCES (HR)

Advised on an engine to automatically match CVs to job descriptions.

Built an ML engine to automatically route incoming job positions to hierarchy of some 1,000 pre-defined job categories.

Built a system to automatically extract structured information from (barely structured) CV PDFs.

Built a ML system to build "user profiles" from enterprise data (logs, wikis), then automatically match incoming help requests in plain text to domain experts.

What was special: Used bayesian inference to handle knowledge uncertainty and combine information from multiple sources.

TRANSPORTATION

Built a system to extract structured fixtures and cargoes from unstructured provider data (emails, attachments).

What was special: deep learning architecture on character level, to handle the massive amount of noise and variance.

BANKING

Built a system to automatically navigate banking sites for US banks, and scrape them on behalf of the user, using their provided username/password/MFA.

What was special: PITA of headless browsing. The ML part of identifying forms, pages and transactions was comparatively straightforward.

--------------

... and a bunch of others :)

Overall, in all cases, lots of tinkering and careful analysis to build something that actually works, as each industry is different and needs lots of SME. The dream of a "turn-key general-purpose ML" is still ways off, recent AI hype notwithstanding.

[1] http://rare-technologies.com/

[2] https://en.wikipedia.org/wiki/Electronic_discovery

[3] https://www.iab.com/guidelines/iab-quality-assurance-guideli...

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

#129

Post by brockf »

At our data science company, we're building a marketing automation platform that uses deep reinforcement learning to optimize email marketing campaigns.

Marketers create their messages and define their goals (e.g., purchasing a product, using an app) and it learns what and when to message customers to drive them towards those goals. Basically, it turns marketing drip campaigns into a game and learns how to win it :)

We're seeing some pretty get results so far in our private beta (e.g., more goals reached, fewer emails sent), and excited to launch into public beta later this month.

For more info, check out https://www.optimail.io or read our Strong blog post at http://www.strong.io/blog/optimail-email-marketing-artificia....

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

#130

Post by jorgemf »

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.

> 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 definition.

Machine Learning: A machine that learns (regardless it uses statistics, information theory, function optimization, biological inspiration or whatever)

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