A vast patch of abnormally warm water in the Pacific Ocean — nicknamed the blob — resulted in increased levels of ozone above the Western US, researchers have found.
The blob — which at its peak covered roughly 9 million square kilometres (3.5 million square miles) from Mexico to Alaska — was assumed to be mainly messing with conditions in the ocean, but a new study has shown that it had a lasting affect on air quality too.
“Ultimately, it all links back to the blob, which was the most unusual meteorological event we’ve had in decades,” says one of the team, Dan Jaffe from the University of Washington Bothell.
A new in-depth read on #transhumanism and life extension by one of the leading and oldest progressive magazines in America:
Only Human Meet the hackers trying to solve the problem of death.
By Anna Wiener
Posted in futurism
This Swiss-made trash can teach the world how to get rid of garbage in style.
Keep up with the latest: http://bit.ly/2jAqlHz
Ford is investing $1 billion in a secretive artificial intelligence startup headed by former Google and Uber execs to advance its self-driving car efforts.
The startup, Argo AI, was founded by Bryan Salesky, the former director of hardware for Google’s self-driving-car efforts, and Peter Rander, Uber’s engineering lead at its autonomous cars center.
The $1 billion investment will be spread out over five years as Ford looks to commercialize its self-driving technology by 2021.
Google and Kaggle today announced a new machine learning challenge that asks developers to find the best way to automatically tag videos.
The challenge, which comes with a $30,000 prize for the first-place finisher (and $25,000, $20,000, $15,000 and $10,000 for the next four teams), asks developers to classify and tag videos from Google’s updated YouTube-8M V2 data set. This data set features a total of 7 million YouTube videos that add up to 450,000 hours of video. YouTube-8M already includes labels, too, and developers can use this as their training data. The challenge then is to tag 700,000 previously unseen videos.
Wide models are great for memorization, deep models are great for generalization — why not combine them to create even better models? In this talk, Heng-Tze Cheng explains Wide and Deep networks and gives examples of how they can be used.
Check out our blog post, paper, YouTube video, TensorFlow tutorials: https://goo.gl/MwVlVa
Visit the TensorFlow website for all session recordings: https://goo.gl/bsYmza
