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

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.

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 in the hype train that fills the Valley these days: our customers see "Data Science product" and don't get that it's really basic predictive analytics under the hood. I'm not sure the product would actually sell as well as it does without that labeling.

To clarify: the company I work for actually uses ML. I actually work on the data science team at my company. My opinion is that we don't actually need to do these things, as our products are possible to create without the sophistication of even the basic techniques, but that battle was lost before I joined.

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...

We're a computer vision company, we do a lot of product detection + recognition + search, primarily for retailers, but we've also got revenue in other verticals with large volumes of imagery. My co-founder and I both did our thesis' on computer vision.

In our space, the recent AI / ML advances have made things possible that were simply not realistic before.

That being said, the hype around Deep Learning is getting pretty bad. Several of our competitors have gone out of business (even though they were using the magic of Deep Learning). For example, JustVisual went under a couple of months ago ($20M+ raised) and Slyce ($50M+ raised) is apparently being sold for pennies on the dollar later this month.

Yes, Deep Learning has made some very fundamental advances, but that doesn't mean it's going to make money just as magically!

From Coursera - we use ML in a few places:

1. Course Recommendations. We use low rank matrix factorization approaches to do recommendations, and are also looking into integrating other information sources (such as your career goals).

2. Search. Results are relevance ranked based on a variety of signals from popularity to learner preferences.

3. Learning. There's a lot of untapped potential here. We have done some research into peer grading de-biasing [1] and worked with folks at Stanford on studying how people learn to code [2].

We recently co-organized a NIPS workshop on ML for Education: http://ml4ed.cc . There's untapped potential in using ML to improve education.

[1] https://arxiv.org/pdf/1307.2579.pdf

[2] http://jonathan-huang.org/research/pubs/moocshop13/codeweb.h...

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...

Here at Matterport, our research team is using deep learning to understand the 3D spaces scanned by our customers. Deep learning is great for a company like ours, where so much of our data is visual in nature and extracting that information in a high-throughput way would have been impossible before the advent of deep learning.

One way we're applying this is automatic creation of panoramic tours. Real estate is a big market for us, and a key differentiator of our product is the ability to create a tour of a home that will play automatically as either a slideshow or a 3D fly-through. The problem is, creating these tours manually takes time, as it requires navigating a 3D model to find the best views of each room. We know these tours add significant value when selling a home, but many of our customers don't have the time to create them. In our research lab we're using deep learning to create tours automatically by identifying different rooms of the house and what views of them tend to be appealing. We are drawing from a training set of roughly a million user-generated views from manually created guided tours, a decent portion of which are labelled with room type.

It's less far along, but we're also looking at semantic segmentation for 3D geometry estimation, deep learning for improved depth data quality, and other applications of deep learning to 3D data. Our customers have scanned about 370,000 buildings, which works out to around 300 million RGBD images of real places.

One of my coworkers used basic reinforcement learning to automate a task someone used to have to do manually. We have two data ingestion pipelines. One that we ingest immediately, and a second for our larger customers which is throttled during the day and ingested at night. For the throttled pipeline, we initially had hard coded rate limits, but as we made changes to our infrastructure, the throttle was processing a different amount than it should have been. Sometimes it would process too much, and we would start to see latency build up in our normal pipeline, and other times it processed too little. For a short period of time, we had the hard coded throttle with a Slack command to override the default. This allowed an enginneer to change the rate limit if they saw we were ingesting to little or too much. While this worked, it was common that an engineer wasn't paying attention, and we would process the wrong amount for a period of time. One of my coworkers used extremely basic reinforcement learning to make the throttle dynamic. It looks at the latency of the normal ingestion pipeline, and based on that, decides how high to set the rate limit on the throttled pipeline. Thanks to him, the throttle will automatically process as much as it can, and no one needs to watch it.

The same coworker also used decision trees to analyze query performance. He trained a decision tree on the words contained in the raw SQL query and the query plan. Anyone could then read the decision tree to understand what properties of a query made that query slow. There's been times we're we've noticed some queries having odd behavior going on, such as some queries having unusually high planning time. When something like this happens, we are able to train a decision tree based on the odd behavior we've noticed. We can then read the decision tree to see what queries have the weird behavior.

At Persyst we use neural networks for EEG interpretation. Our latest version has human-level performance for epileptogenic spike detection. We are now working on bringing the seizure detection algorithm to human-level performance.

The startup I'm part of uses ML to predict which end users are likely to churn for our customers.

We work with B2B and B2C SAAS, mobile apps and games, and e-commerce. For each of them, it is a generalized solution customized to allow them to know which end users are most at risk of churning. The amount of time range varies depending on their customer lifecycles, but for longest lifecycles we can, with high precision, predict churn more than 6 months ahead of actual attrition.

Even more important than "who is at risk?" is "why are they at risk?". To answer this we highlight patterns and sets of behavior that are positively and negatively associated with churn, so that our customers have a reason to reach out, and are armed with specific behaviors they want to encourage, discourage, or modify.

This enables our customers to try to save their accounts / users. This can work through a variety of means, campaigns being the most common. For our B2B customers, the account managers have high confidence about whom they need to contact and why.

All of this includes regular model retraining, to take into account new user events and behaviors, new product updates, etc. We are confident in our solution and offer our customers a free trial to allow us to prove ourselves.

I can't share details, but we just signed our biggest contract yet, as of this morning. :)

For more http://appuri.com/

A recent whitepaper "Predicting User Churn with Machine Learning" http://resources.appuri.com/predicting_user_churn_ml/

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.

Our low-latency trading group uses regression widely. We have experimented with more complex models but haven't found a compelling use for them yet.

We use ML to model complex interactions in electrical grids in order to make decisions that improve grid efficiency, which has been (at least in the short term) more effective than using an optimizer and trying to iterate on problem specification to get better results.

Generally speaking, I think if you know your data relationships you don't need ML. If you don't, it can be especially useful.

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 in highlighting trending topics over time, for example.

Detecting fraud. I work for a credit card company.

Not really a new application though...

FinTech: Credit risk modeling. Spend prediction. Loss prediction. Fraud and AML detection. Intrusion detection. Email routing. Bandit testing. Optimizing planning/ task scheduling. Customer segmentation. Face- and document detection. Search/analytics. Chat bots. Sentiment analysis. Topic analysis. Churn detection.

We've been using "lite" ML for phenotype adjudication in electronic health records with mild success. Random forests and support vector machines will outperform simple linear regression when disease symptoms/progression don't neatly map to hospital billing codes.

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.

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.

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.

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.

We use Convolutional Networks for semantic segmentation [1] to identify objects in the users environment to build better recommendation systems and to identify planes (floor, wall, ceiling) to give us better localization of the camera pose for height estimates. All from RGB images.

[1] https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn...

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.

We use deep learning at attentive.ai to generate alerts based on unusual events in surveillance video.

We use neural nets to generate descriptors of videos where motion is observed, and classify events as normal/abnormal.

Based on past experimental data, we use ML to predict how effective a given CRISPR target site will be. This information is very valuable to our clients.

Machine learning is great for helping you understand a new dataset quickly. I often train a basic logistic regression classifier and introspect the coefficients to learn what features are important, which are unimportant, and how they are correlated.

There are a number of other statistical techniques you can use for this but scikit-learn makes this very very easy to do.

Pretty basic here.. we are a payments processor so we check volume, average ticket $, credit score and things of that nature to determine the quality and lifetime of a new merchant account.

- We use a complex multivariate model to predict customer conversion and prioritize lead response - We use text analysis to improve content for effectiveness and conversion Among other things

I would suspect AI/ML profits come largely from improving ad revenue at very stable companies.

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...

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.

I run a deep learning company focused on a lot of banking and telco fraud workloads like [1]. We have also done dl to predict failing services to auto migrate workloads before server failure.

The bulk of what we do is anomaly detection.

[1] https://skymind.io/case-studies [2] insights.ubuntu.com/2016/04/25/making-deep-learning-accessible-on-openstack/

We realized that by adjusting training models we could incorporate autonomous recognition of not only images but intent and behavior into our application suite.

Deep learning to identify available space in kit from images! We are dead proud of it !

Trad learning for many applicatons : fault detection, risk management for installations, job allocation, incident detection (early warning of big things), content recommendation, media purchase advice, others....

Probabilistic learning for inventory repair - but this is not yet to impact, the results are great but the advice has not yet been ratified and productionised.

I'm using some of the pre-built libraries to find/fix low hanging fruit of data quality issues for https://www.findlectures.com, for instance finding speaker names.

The first pass is usually a regex to find names, then for what's left run a natural language tool to find candidate names, and then manual entry.

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....

At Graphistry, we help investigate & correlate events, initially for security logs. E.g., Splunk & Sumo centralize data and expose grep + bar charts, then we add visual graph analytics that surfaces entities, events, & how they connect/correlate. "It started here, then went there, ..." . We currently do basic ML for clustering / dimensionality reduction, where the focus is on exposing many search hits more sanely.

Also, some GPU goodness for 10-100X visual scale, and now we're working on investigation automation on top :)

Helping to moderate comments on theguardian.com!

https://skillsmatter.com/skillscasts/9105-detecting-antisoci...

(We're still beginners as will be apparent from the video but it's proving useful so far. I should note, we do have 'proper' data scientists too, but they are mostly working on audience analysis/personalisation).

We're building models of human behavior to provide interactive intelligent agents with a conversational interface. AI/ML is literally the backbone of what we're doing.

Providing users the best recommendations so they participate more, get more from the service, and churn less. Detecting fraud and so saving money. Predicting users who are about to leave and allowing us to reach out to them. Dynamic pricing to take optimum advantage of the supply and demand curve. Delayed release of product so it doesn't all get reserved immediately and people don't have to camp the release times.

Wrote a grammar checker that used both ML models and rules (which in turn used e.g. part-of-speech taggers based on ML).

Wrote a system for automatically grading kids' essays (think the lame "summarize this passage"-type passages on standardized tests). In that case it was actually a platform for machine learning - ie, plumb together feature modules into modeling modules and compare output model results.

At ScreenSquid we use statistical analysis to find screen recordings of the most active users on your website. This saves our customers a ton of time avoiding playing with filters trying to find "good" recordings.

https://screensquid.com/2016/12/introducing-star-ratings/

Predict probability of car accidents based on the sensors of your smartphone

Our main product uses machine learning and natural language processing to predict how long JIRA tickets are going to take to resolve.

(www.queckt.com is anyone's interested)

Without AI/ML, we wouldn't have a product.

I have a follow on question to this to all the respondents: Can you briefly describe the architecture you are using? Cloud-based offering vs self-hosted, software libraries used, etc...

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.

We use RNNs for voice keyword recognition.

Slightly tangential, but how do you collect training data for AI/ML models you are developing

We are using machine learning to identify software as benign software or malware for customers.

Sift's product is based on ML.

Lots of KYC things, like fraud, AML and CTF. Helps with finding new patterns.

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.

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... 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...

We use ML for recommendation systems (I work at a Classifieds company)

PCB autorouting

Can't say for what/where, but, yes. Use it to super-scale work of human analysts who evaluate the quality of some stuff.