"Yet buried in this scenario of a takeover of superhuman artificial intelligence are five assumptions which, when examined closely, are not based on any evidence."
"documents the downward trend in the labor share of income since the early 1990s, as well as its heterogeneous evolution across countries, industries, and workers of different skill groups, using newly assembled data for a large sample of advanced and emerging market and developing economies. The chapter then analyzes the forces behind these trends. Technological progress, reflected in the steep decline in the relative price of investment goods, along with varying exposure to routine-based occupations, explains about half the overall decline in advanced economies, with a larger negative impact on the earnings of middle-skilled workers. "
A lot of frivolous things were to be seen at the F8 Deverloper's Conference, but behind the fun there is a tech revolution going on which will generate many other applications.
I find myself recommending this article in multiple situations and settings. It is on almost every syllabus I create regardless of the stated intent of the course. It is an excellent entry point into systems thinking and a cribsheet that anyone hoping to improve organizations and systems ought to have handy
Interesting interview with one of the key persons of Bitnation, a crypto-anarchist initiative crating a platform for virtual nations where such nations would compete for citizens, offering better services.
A truly innovative product would learn from the mistakes of predecessor platforms and would focus on things like privacy, security and accessibility. Instead, Clubhouse is yet another example of technology designed by, and largely for, privileged, white, Western and able-bodied men.
" Google's announcement is a classic example of what you might call privacy theater: While marketed as a step forward for consumer privacy, it does very little to change the underlying dynamics of an industry built on surveillance-based behavioral advertising."
The time has come, said Re, for AI scientists to focus on higher-level tasks such as bringing domain expertise to AI rather than twiddling hyper-parameter settings of neural networks.