A team from Max-Planck Institute for Intelligent Systems, ETH Zurich, Google Research Amsterdam, Mila and the University of Montreal make an effort to bring together causality and machine learning research programs, delineate implications of causality for machine learning and propose critical areas for future research.
“We’ve made huge progress, much more than even my friends and I expected a few years ago. But (the progress) is mostly about perception, things like computer vision and speech recognition and synthesis of some things in natural processing. We’re still far from human capabilities.”
In an unprecedented “call for collaboration,” a group of 22 respected AI experts that includes Andrew Ng, Yoshua Bengio, and Demis Hassabis have published a paper exploring how machine learning (ML) could help deal with climate change by reducing greenhouse gases (GHG).
Compared to SMT, NMT can train multiple features jointly and does not need prior domain knowledge, enabling zero-shot translation. In addition to higher BLEU score and better sentence structure, NMT can also help reduce morphology errors, syntax errors, and word order errors of SMT.