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Rocks and minerals contribute essential raw materials for any civilization, and in a technological society minerals (and the rare elements they contain) are especially sought after. In the past, most discoveries of mineral deposits have resulted from perseverance and luck.

In the last 200 years scientists realized that minerals are not distributed randomly. Many of the over 5,000 different minerals occurring on Earth exist in a so-called paragenesis. A paragenesis is a mineral assemblage formed under specific physico-chemical rules, like a certain chemical composition of the host rock or when the right conditions — like temperature and pressure — are met.


A machine learning model can predict the locations of minerals on Earth — and potentially other planets — by taking advantage of patterns in mineral associations.

Self-driving efforts today focus on particular niches, such as the urban robotaxi, delivery, trucking or freeeway driving. Other than Tesla, most major players don’t have a focus on the general personal robocar — a car which consumers will buy, which will drive them door to door on city streets and most other roads. Tesla is very far behind other teams, and barely counts in the minds of many in the industry, thought it gets the most press. A few startups pursue the full robocar dream, but thinking has changed.

In spite of that perceived dream, that is not what the industry is building, or what it is going to release for some time. It may be some time before you can buy a car for yourself with this ability, not just because it’s hard, but because it’s not where the money is. This has led some people to think that robocars are still very far away, and also to a common perception that the technology is many years behind what people expected. Indeed, some people expected, or at least hoped for, faster timelines, but others did not.

The public has a different perception, in part because of Tesla, but also because of a document written over a decade ago by NHTSA (the federal road safety agency) and now manged by the Society of Automotive Engineers known as “the levels.” This document filled the need for a taxonomy of self-driving, but it was written by non-developers when the technology was immature. As such it’s largely useless and even counterproductive, but people are so hungry for a taxonomy that it still is often referred to. The leading teams (mostly tech companies not auto OEMs) do not use these level or attempt to adhere to them. They are mostly a way to talk about the dwindling role of the human in the operation of a self-driving car, a bit like a document about the role of the horse in the horseless carriage.

Generative AI in supply chains will be able to forecast demand, predict when trucks need maintenance and work out optimal shipping routes, according to analysts.“AI may be able to totally (or nearly) remove all human touchpoints in the supply chain including ‘back office’ tasks,” said Morgan Stanley analysts.

But “Generative AI, in my mind is, once in a lifetime kind of disruption that’s going to happen … so there are going to be losses of jobs in the more traditional setting, but I also believe it’s going to create new jobs like every prior technology disruption has,” said Navneet Kapoor, chief technology and information officer at shipping giant Maersk.

Artificial intelligence is likely… More.


Artificial intelligence is likely to shake up the transportation industry — transforming how supply chains are managed and reducing the number of jobs carried out by people, according to analysts and industry insiders.

face_with_colon_three Year 2020


Cyanobacteria — colloquially also called blue-green algae — can produce oil from water and carbon dioxide with the help of light. This is shown by a recent study by the University of Bonn. The result is unexpected: Until now, it was believed that this ability was reserved for plants. It is possible that blue-green algae will now also become interesting as suppliers of feed or fuel, especially since they do not require arable land. The results have now been published in the journal PNAS.

What do rapeseed, avocado and olive tree have in common? They are all used by humans as producers of oil or fat. However, the ability to produce oil from water and carbon dioxide with the help of light is something that is essentially common to all plants, from unicellular algae to the giant sequoia trees. “We have now shown for the first time that cyanobacteria can do the same,” explains biologist Prof. Dr. Peter Dörmann from the Institute of Molecular Physiology and Biotechnology of Plants (IMBIO) at the University of Bonn. “This was a complete surprise, not only to us.”

Until now, experts had assumed that cyanobacteria lack this property. After all, they are actually bacteria, even if their trivial name “blue-green algae” suggests otherwise. They therefore differ considerably from plants in many respects: Cyanobacteria are closer related to the intestinal bacterium E. coli than to an olive tree. “There are indeed ancient reports in the literature that cyanobacteria can contain oil,” says Dörmann. “But these have never been verified.”

Summary: Treating a mouse model of multiple sclerosis (MS) with the pregnancy hormone estriol could reverse myelin breakdown in the brain’s cortex, a primary area affected in MS.

MS results in inflammation that damage the myelin coating around nerve fibers in the brain’s cortex, leading to disability worsening. Current MS treatments only target inflammation and can’t repair myelin damage.

However, the new study found that estriol not only prevented brain atrophy but also induced remyelination, suggesting it could repair MS-induced damage.

Tesla has released a rare and interesting look at its latest Supercharger monitoring system, which will become an important tool for managing an increasingly valuable asset.

Older Tesla owners will remember the days when the automaker was operating Supercharger monitoring systems on screens at a select few stations.

You could see the entire global use and output of the Supercharger network on these screens. They were really interesting, and we used them in our reporting a few times.

For years, machine learning has been used to analyse human languages or decode ancient communication. Now scientists are harnessing the power of artificial intelligence (AI) to decode animal languages, making two-way communication with another species more likely than ever before. Video by: Pedro Films and Tree House Productions Executive Producer: Camelia Sadeghzadeh.