Edge.org's annual question 'What do you consider the most interesting recent [scientific] news? What makes it important?' is an excellent read, as usual. Progress in deep learning has a strong showing (even DeepDream!), behind Crispr, the Pluto flyby, and the studies showing that the majority of psychology studies are not reproducible.
A few things stood out for me:
- If you're not in awe and a tiny bit terrified by recent advances in gene editing, you're not paying attention.
- Every year, a few authors invariably try to show how smart they are by going meta: 'what is news?' 'are news important?' I find that plain irritating. If you have nothing of substance to contribute, just don't.
- Alexander Wissner-Gross' answer that datasets are the primary bottleneck to progress in ML/AI advances, as opposed to algorithms, really resonates with me. I have seen enough algorithmic work invalidated by 'just add more data' over the years, that I am seriously questioning how much collective time we devote to improving modeling as opposed to data curation. When I look at the next big challenges in machine learning, I see few that have publicly available datasets comparable in size and quality to e.g. object recognition or speech.