PAINTERS like Claude Monet and Pablo Picasso transformed art by inventing new styles such as impressionism and cubism. Could a machine do the same?
EARLIER this year Françoise Hardy was asked in a YouTube video why President Donald Trump sent his press secretary, Sean Spicer, to lie about the size of the inauguration crowd. First, Ms Hardy argues.
THE DAY RICHARD Feynman died, the blackboard in his classroom read: “What I cannot create, I do not understand.”
When Ian Goodfellow explains the research he’s doing at Google Brain, the central artificial intelligence lab at the internet’s most powerful company, he points to this aphorism from the iconic physicist, Caltech professor, and best-selling author.
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.
Classic machine learning (especially as it is taught in classes) emphasizes a nice safe static environment where you are given some unchanging data and are asked to produce a nice predictive model one time.
It is formally easier that casual inference or statistical inference as being right often is enough, no matter what the reason. It lives in an overly idealized world where one implicitly assumes the following simplifying assumptions.