If humankind wants to survive the rise of artificial intelligence, we need to embrace the machines and become a melded cyborg organism, projected Elon Musk, CEO of SpaceX and Tesla, on Monday. Enhancing our minds is Musk’s answer to finding the meaning of life.
ON THE WEST coast of Australia, Amanda Hodgson is launching drones out towards the Indian Ocean so that they can photograph the water from above. The photos are a way of locating dugongs, or sea cows, in the bay near Perth—part of an effort to prevent the extinction of these endangered marine mammals.
Researchers from MIT have developing a deep-learning algorithm that, given a still image from a scene, can create a brief video that simulates the future of that scene. Trained on 2 million unlabeled videos that include a year’s worth of footage, the algorithm generated videos that human subjects deemed to be realistic 20 percent more often than a baseline model.
FOR GOOGLE, IT’S not enough that its products rely on machine learning and artificial intelligence. The company also wants you, its customer, to understand how these technologies work.
Last year, a few months after it open sourced its deep learning engine, a Google researcher partnered with The New York Times to create this data visualization explaining neural networks
The gap between human and machine translators could be narrowing as researchers find a new way to improve the learning capabilities of Google Translate’s neural network.
On the same day that Google announced its translation services were now operating with its Neural Machine Translation (NMT) system, a team of researchers released a paper on arXiv showing how its NMT could be pushed one step further.
Microsoft today launched a number of key cancer-fighting projects it has under way, showing off its application of machine learning to solve bigger challenges than identifying dog breeds.
One such application, called Project Hanover, is seeking to make personalized, precision cancer therapy available to all cancer patients by helping oncologists sift through reams of biomedical research papers faster.
Over the past four years, readers have doubtlessly noticed quantum leaps in the quality of a wide range of everyday technologies. Most obviously, the speech-recognition functions on our smartphones work much better than they used to.
In fact, we are increasingly interacting with our computers by just talking to them, whether it’s Amazon’s Alexa, Apple’s Siri, Microsoft’s Cortana, or the many voice-responsive features of Google.
One of my favorite deep learning papers is Learning to Generate Chairs, Tables, and Cars with Convolutional Networks. It’s a very simple concept – you give the network the parameters of the thing you want to draw and it does it – but it yields an incredibly interesting result.
The network seems like it is able to learn concepts about 3D space and the structure of the objects it’s drawing, and because it’s generating images rather than numbers it gives us a better sense about how the network “thinks” as well.