I use it as something to worry about not knowing how to use, and how that might make me unemployable in a few years, while also having no obvious need for it at all, and therefore no easy avenue towards learning it, especially since it requires math skills which have completely rusted over because I also never need those, so I'd have to start like 2 levels down the ladder to work my way back up to it.

I've found it very effective in the role of "source of general, constant, low-level anxiety".

I'm not using it at this moment, but in a couple of weeks I'll have a newborn, and I was thinking of taking pictures of him every time he is upset, and then tagging the photo with what ends up being the resolution (feed, change, or nap) and seeing if I could build a classifier that could figure out what he needs just from a picture of his angry face.

Semi-related, but if anyone is using the AWS tools for their AI, please ping me. I'm looking for a speaker for a community event in SF in June. (contact info in profile)

We analyze images of skin lesions and give a probability whether they are malignant melanoma. The analysis is performed on-board on mobile phones.

Detecting what pornstars, actions, production and categories appear in 5mil+ adult videos.

Classifying future elite race horses, before they've raced.[1][2]

I didn't develop the original approach but am involved in working to improve the predictive outcomes.

[1] https://www.breezeupiq.com

[2] http://www.performancegenetics.com

I work for a large semiconductor manufacturer. In our R&D operations we require a large volume of newly designed parts to be purchased from suppliers. These parts are not off the shelf but are all custom created by us. Once a design is approved we try to get them into the lab the fastest way possible so the earlier we can get the designs quoted with a supplier, the better.

I use machine learning to predict if a newly designed part will have to be purchased soon to go in the lab. That prediction saves me about 3 weeks of lead time to get it to a supplier. Before machine learning, the first time the purchasing department knew that it will need to be purchased is when the designer filled out a form. Now they know as soon as he submits his design and can get a head start.

Burning CPU cycles on an opaque, needlessly complicated model created by a canny professor who realized that putting ML in grant proposals gets you 5x the cash compared to regression.

I run a website called Hey Am I Fat. We've trained a classifier based off a simple softmax regression in TensorFlow that tries to detect whether people are fat or not. If you submit a picture of yourself to HeyAmIFat.com we can tell you if you're fat or not within minutes.

We use it for automated seizure detection in EEG data. Most seizures have no clinical manifestation (e.g., shaking or trembling), and if a seizure goes on too long (~30 minutes) the patient can suffer permanent brain damage or even die. This is particularly problematic for patients in the ICU since they tend to seize silently more often and it isn't easy to tell that they are seizing without having a neurologist examine the EEG data.

We also use machine learning for detecting other features of EEG data and removing artifacts. (Eye blinks, for example, cause big artifacts.)

Replacing project managers and introducing reward/score based AI bots that keep track of progress of individuals as function of stories they are working on, and these bots send out reminders (slack chat bots perhaps). This will can help improve productivity at large firms with multiple (and sometimes unnecessary) levels of manager hierarchies such as IBM, Dell, HP, oil companies etc. Let's be honest a certain programmer has a pattern with which he/she works. Time spent on a problem, number of initial bugs, number of commits, time spent vs difficulty of a certain problem and so forth. We have all this data to quantify and train a bot. I mean why not :)

(In progress) Automate e-commerce business completely, including customer/supplier e-mail responses, voice communication, identifying data structure of supplier feeds and performing automated conversion between formats for integration, sales estimation, hidden platform variables identification (how to win a buy box?), price competition etc. Tech based on Python, DL (Keras), RNN, GAN, SVM, decision trees etc.

Checking for issues with loan photos as they are posted. It's been interesting to find that many ML platforms and libraries don't do a great job of recognizing humans with dark skin. Hopefully, by adding Kiva's 1 mil+ images to the mix we can give them a better basis point to learn from. Hoping to use it for a lot of other things as well, but we're still getting to know how to leverage it.

In search, Learning to Rank is a common application for ML ...

https://en.wikipedia.org/wiki/Learning_to_rank

https://cwiki.apache.org/confluence/display/solr/Learning+To...

Building a smart home security system, so you can be notified when your friends have arrived, or if someone is trying to break into your home. Machine learning is very useful here, as you can train the system to recognise what your friends look like, or what a break-in might look like.

I work for a large multi-hospital corporation.

  We use machine learning to predict the risk of a patient, after an inpatient admission, being readmitted in 30 days post discharge.  If the risk is high, we proactively put process in place to reduce the readmission risk.

Forecast loan payment defaults for 90 days or longer, 3 months in advance using a tree based model.

I am just getting into it, but I am primarily looking at it because I have about 8 months worth of home automation sensor data stored in a MySQL database thanks to my home automation software (thanks home-assistant!) and I want to see if ML can look at patterns from that sensor data and infer what to do in my apartment.

For instance when I get home (gps sensor) and when I open my front door (door binary sensor), can a system understand that I typically turn my TV on when that happens?

Automatically answering HR queries worded in different ways

I use Stanford NER to extract book titles and authors from HN comments. Previous thread: https://smackernews.com/item/14202557 HN

I'm using it to isolate acapellas from music for making mashups/remixes. Not quite "real world" yet, since even my most recent models aren't perfectly reliable, but it's getting there.

Predicting mistakes made by humans during audio/video transcription, giving them auto-complete suggestions, classifying severity of changes, predicting the difficulty level based on audio characteristics.

I've been working with clustering algorithms to create a personal news aggregator from sitemaps and RSS feeds, using an NLP library (Stanford CoreNLP) to do feature extraction. [1] I'd like to extend it to classify articles into different categories so I could filter things like transcripts, opinion columns, web scrapes gone wrong, but I'm not sure how to set that up yet.

[1] Very rough prototype: https://confabulator.io/newsclustering/

Detecting moments of user frustration in web apps (ie. rage clicks, shaking mouse, refreshing the page) https://logrocket.com

Fraud detection (credit cards, online banking)

Detecting illegal mining activity via satellite

network intrusion (packet analysis)

Predicting machine failure

Stock market trend prediction (up/down)

Simbox fraud

Finding similar parts for large vehicles to help engineers

I can keep going, but this is a sample I've seen over the years. We mainly do work in time series data for enterprise basically mostly "things google doesn't do". A bulk of what we do doesn't appear in research papers because it's not "GAN art" eg: "the current hype".

I use some basic tf-idf machine learning for clustering similar articles for http://tracket.com

At Transloadit we're going to use it for predicting how many machines we'll need to encode incoming video and audio files in near realtime, and then scaling that capacity up in parallel, as files are still being uploaded or imported. Already half a year in the making, but in our early tests it seems we can outperform the custom algorithms we had in place for this, by a lot.

Detecting malware. https://www.barkly.com/product

Not ML per se but ANN have proven to be quite useful in tumor growth modeling. I am using them to model cancerous cells' genotype.

Classification of Contenets Segmentation of MEdical Images Neural Networks can also be used in DeNoising, DeHAzing Super-Resolution

* financial forecasting

* resource demand forecasting

* balancing distributed energy resource loads

* automating metadata generation

* categorizing 'events'

* finding anomalies in satellite data

Most important:

* running bots on my xbox

Use it in my software engineer screening service to determine if a user should answer more questions on a particular topic and dive deeper into a particular subject. Using Azure ML, been working out well so far. Went from messing around for days with other ML libraries to up and running within 20 minutes on Azure.

Hotdog/Not Hotdog

We use machine learning for parsing jobs and resumes and then to match them with each other.

To see the results - post your resume here: https://www.postjobfree.com/post-resume

We use ml to identify users who might try to commit fraud when purchasing things from our site, as well as prevent identify fraud when users verify their ID with us. We are also looking into taking advantage of adaptive design to improve ux and conversion.

-Customer analysis / clustering / behavior visualization

-Financials analysis / predictions

-Image classification

Automatic text summarization.

Predicting day-to-day harvest yields of perishable fruit, like Strawberries.

I find it useful for learning 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.

At work we're using it to battle fraud, we're also using it to recommend apps and themes to our users and we're using it internally for various forecasting tasks.

Evolving mobile games to discover variants that earn 5 star reviews. Using https://improve.ai

At my work we use it to do large-scale analysis of remote sensing data, in particular segmentation of satellite imagery to provide land monitoring services.

image processing and effects using deep learning: http://somatic.io

improving relevancy of search results - http://www.coveo.com/en/platform/machine-learning

Multilingual named entity recognition and disambiguation

predicting the evolution of running cases, and the arrival of new cases, for capacity planning in a call center

Recommender system using visual features

Trying to predict horse racing

Not me, but a friend of mine heads a company that uses drones to identify defects / damage on containers. He uses ML to process the images captured by the drones, which is fed through an ML processor to detect such damages & flag them for insurance claims purposes.