“We’re making advertising more transparent to help prevent abuse on Facebook, especially during elections. Today we’re starting to roll out the Ad Archive API, so researchers and journalists can more easily analyze Facebook ads related to politics or issues of national importance”.
Source : Introducing the Ad Archive API | Facebook Newsroom
“After connecting to Facebook, the BlackBerry Hub app was able to retrieve detailed data on 556 of Mr. LaForgia’s friends, including relationship status, religious and political leanings and events they planned to attend. Facebook has said that it cut off third parties’ access to this type of information in 2015, but that it does not consider BlackBerry a third party in this case”.
Source : Facebook Gave Device Makers Deep Access to Data on Users and Friends – The New York Times
« Today I’m able to launch the full Geotaggers’ World Atlas covering every city in the world. Thanks to Flickr’s API, it exposes over 10 years of photo locations, and as a web map it lets you explore not just the largest centers of activity but also their context, anywhere on earth » – Eric Fischer.
Voir aussi le même concept avec Twitter (GNIP-Mapbox), nettement moins pertinent pour les pratiques touristiques.
Source : Linking the most interesting places in the world | Mapbox
Dans le cadre de sa démarche d’ouverture des données de transport, la RATP propose une API qui permet d’accéder, en temps réel, aux horaires des prochains passages pour les bus, tramways, métros et RER de son réseau.
Source : Open Data RATP — Horaires Temps réel RATP
Machine Learning (ML) is a fast-moving, competitive field. As important as good algorithms are to ML, the state-of-the-art algorithms are reliably available. Compute clusters are also an important ingredient, and they too are easy to access (especially using services like Amazon EC2 and Amazon EC2 Elastic GPUs). What isn’t reliably available are large-scale high quality data sets. The things you need to train your classifiers. That’s where MTurk comes into play.
Source : re:Invent 2016 recap — Machine Learning with Amazon Mechanical Turk
The Emotion API takes a facial expression in an image as an input, and returns the confidence across a set of emotions for each face in the image, as well as bounding box for the face, using the Face API.
Source : Microsoft Cognitive Services – Emotion API
Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API. It quickly classifies images into thousands of categories (e.g., « sailboat », « lion », « Eiffel Tower »), detects individual objects and faces within images, and finds and reads printed words contained within images. You can build metadata on your image catalog, moderate offensive content, or enable new marketing scenarios through image sentiment analysis
Source : Vision API – Image Content Analysis — Google Cloud Platform