A new biography of Elon Musk was published on Tuesday and contains colourful details of the life of the world's richest man. Musk afforded widespread access to his biographer, Walter Isaacson, the author of the bestselling biography of the Apple co-founder Steve Jobs, and the book contains a series of illuminating anecdotes about Musk. Here are eight things we learned from the book. Musk, 52, was born and raised in South Africa and endured a fraught relationship with his father, Errol, an engineer. Isaacson writes that Errol " bedevils Elon".

Continuous observation of climate indicators, such as trends in lake freezing, is important to understand the dynamics of the local and global climate system. Consequently, lake ice has been included among the Essential Climate Variables (ECVs) of the Global Climate Observing System (GCOS), and there is a need to set up operational monitoring capabilities. Multi-temporal satellite images and publicly available webcam streams are among the viable data sources to monitor lake ice. In this work we investigate machine learning-based image analysis as a tool to determine the spatio-temporal extent of ice on Swiss Alpine lakes as well as the ice-on and ice-off dates, from both multispectral optical satellite images (VIIRS and MODIS) and RGB webcam images. We model lake ice monitoring as a pixel-wise semantic segmentation problem, i.e., each pixel on the lake surface is classified to obtain a spatially explicit map of ice cover. We show experimentally that the proposed system produces consistently good results when tested on data from multiple winters and lakes. Our satellite-based method obtains mean Intersection-over-Union (mIoU) scores >93%, for both sensors. It also generalises well across lakes and winters with mIoU scores >78% and >80% respectively. On average, our webcam approach achieves mIoU values of 87% (approx.) and generalisation scores of 71% (approx.) and 69% (approx.) across different cameras and winters respectively. Additionally, we put forward a new benchmark dataset of webcam images (Photi-LakeIce) which includes data from two winters and three cameras.

Enjoy it while it lasts. US Treasury Secretary Steven Mnuchin may think artificial intelligence (AI) isn't going to start taking humans' jobs for 50 to 100 years, but most experts believe a revolution in automation is coming far sooner, promising massive increases in efficiency -- and job losses on a huge scale. A recent study put out by PwC estimated that as many as 30% of UK jobs could be "susceptible to automation by robots and AI" by the early 2030s -- with 38% in the US at risk, 35% in Germany, and 21% in Japan -- although it believes jobs will be created elsewhere in the economy to help offset this. Are we doing enough to prepare? Absolutely not, says Bob Moritz, global chairman of consultancy firm PwC.

It's no secret that automated machines and robots are rapidly replacing human workers. In Europe, concerns about the loss of good-paying jobs to automation is so strong that the idea of a universal basic income is gaining traction. France, for one example, could lose three million jobs by 2025 due to automation, a former education minister campaigning for the French presidency has argued. The topic has been front of mind this week for participants at the World Economic Forum's annual summit in Davos, Switzerland. There, CBS News asked business leaders to look past the hope and hype of artifical intelligence and reflect on the negatives of automation.