Source : DATAVISUALISATION — I wanted to identify / map the films… (Via Christophe Cariou)
We think PlaNet has an advantage over humans because it has seen many more places than any human can ever visit and has learned subtle cues of different scenes that are even hard for a well-traveled human to distinguish.
Rue Tholoze, Paris, un petit défi pour les voitures du futur…
For Google, this is a competitive necessity. Facebook has improved the speed of stories in its walled garden with Instant Articles. Apple News provides a streamlined experience on iPhones for those who use it. Google’s turf is the web, and the company doesn’t want to lose out to more user-friendly spots outside its reach on your phone. Speed matters on mobile. Google wants to make sure you know it serves up stories just as fast as everywhere else.
Google vs Facebook vs Apple against the Open Web.
Wouldn’t the country be better if all politicians were that transparent?
Précisément, Watson peut-il sérieusement répondre à cette question ?
Source : Watson for President 2016
Now, Facebook says it has mapped almost 2 billion people better than any previous project. The company’s Connectivity Labs announced this week that it created new, high-resolution population-distribution maps of 20 countries, most of which are developing. It won’t release most of the maps until later this year, but if they’re accurate, they will be the best-quality population maps ever made for most of those places.
ARM ecosystem director Nizar Romdan explained that the chips that his company creates with partners like Nvidia, Samsung, and Texas Instruments will generate visuals on par with and then surpass what you get from the PlayStation 4 and Xbox One consoles by the end of 2017.
Now, major documents from human history such as Universal Declaration of Human Rights (UDHR), Newton’s Opticks, Magna Carta and Kings James Bible, have been saved as digital copies that could survive the human race.
At Europeana we are able to show the 20th century black hole in our dataset by looking at the temporal distribution of works within the dataset.
This project is an attempt to use modern deep learning techniques to automatically colorize black and white photos.
Source : Automatic Colorization