A thoughtful and poignant investigation of the story behind Frank Lloyd Wrights concrete block LA houses.
A stunning and unexpected performance of Radioheads ‘Creep’
I love stories like this, that peel the layers of the hard work that makes real innovation happen, to reveal something thats often counter-intuitive.
This is the fascinating story of Googles only Senior Fellows – Jeff Dean and Sanjay Ghemawat – a pair of coders that solved foundational problems for Google and helped create the Internet experience as we know it, by coding together.
To solve problems at scale, paradoxically, you have to know the smallest detailsAlan Eustace, Google
The kind of light, shadow and color it’s impossible to find in Delhi beyond a few days in the monsoon
He’s king of the world!
This bears thinking about…
Big companies are hoarding big data and doing nothing with it–except invading our privacy. It’s time to think small, writes Paddle Consulting’s Brian Millar.
Patrick Collins recut Star Trek:TMP into 20 minutes, using the Daft Punk written soundrack album for TRON. It is fantastic!
Machine learns racial and gender biases embedded in human data.
Lets not assume AI will be evil or wise. AI see, AI do, like any monkey. At some point it may grow up and learn ‘good’ from ‘bad’ but thats debatable.
Machine learning algorithms are picking up deeply ingrained race and gender prejudices concealed within the patterns of language that humans commonly use, scientists say.
For instance, in the mathematical “language space”, words for flowers are clustered closer to words linked to pleasantness, while words for insects are closer to words linked to unpleasantness, reflecting common views on the relative merits of insects versus flowers.
The latest paper shows that some more troubling implicit biases seen in human psychology experiments are also readily acquired by algorithms. The words “female” and “woman” were more closely associated with arts and humanities occupations and with the home, while “male” and “man” were closer to maths and engineering professions.
And the AI system was more likely to associate European American names with pleasant words such as “gift” or “happy”, while African American names were more commonly associated with unpleasant words.