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Robots aren’t going to take everyone’s jobs, but technology has already reshaped the world of work in ways that are creating clear winners and losers. And it will continue to do so without intervention, says the first report of MIT’s Task Force on the Work of the Future.


Widespread press reports of a looming “employment apocalypse” brought on by AI and automation are probably wide of the mark, according to the authors. Shrinking workforces as developed countries age and outstanding limitations in what machines can do mean we’re unlikely to have a shortage of jobs.

But while unemployment is historically low, recent decades have seen a polarization of the workforce as the number of both high- and low-skilled jobs have grown at the expense of the middle-skilled ones, driving growing income inequality and depriving the non-college-educated of viable careers.

This is at least partly attributable to the growth of digital technology and automation, the report notes, which are rendering obsolete many middle-skilled jobs based around routine work like assembly lines and administrative support.

Houston Mechatronics (HMI) unveiled Aquanaut at the NASA Neutral Buoyancy Laboratory, one year after the announcement of the platform concept.

Aquanaut is a revolutionary multi-mode transforming all-electric undersea vehicle. The vehicle is capable of efficient long-distance transit and data collection in ‘AUV’ (autonomous underwater vehicle) mode.

After transforming into ‘ROV’ (remotely operated vehicle) mode the head of the vehicle pitches up, the hull separates, and two arms are activated so that Aquanaut may manipulate its environment.

Over the past few years, thermoelectric generators have become the focus of a growing number of studies, due to their ability to convert waste heat into electrical energy. Quantum dots, semiconductor crystals with distinctive conductive properties, could be good candidates for thermoelectric generation, as their discrete resonant levels provide excellent energy filters.

In a recent study, researchers at the University of Cambridge, in collaboration with colleagues in Madrid, Rochester, Duisburg and Sheffield, have experimentally demonstrated the potential of an autonomous nanoscale harvester based on resonant tunneling quantum dots. This harvester is based on previous research carried out by part of their team, who had proposed a three-terminal energy harvester based on two resonant-tunneling quantum dots with different energy levels.

The energy harvester device was realized at Cavendish Laboratory in Cambridge by a researcher called Gulzat Jaliel. The original theoretical proposal for the device, however, was introduced by Andrew Jordan in 2013, and the theoretical work behind the harvester was carried out by him in collaboration with renowned semiconductor physicist Markus Büttiker and a team of post-doctoral students in Geneva.

SpaceX’s Starship prototype is coming together: Elon Musk recently teased some photos of the spacecraft and its construction site looks like something straight out of Star Wars.

The spacecraft, which serves as a prototype for SpaceX’s Mars-bound Starship, is currently under development, CNN reported. It follows the Starhopper, SpaceX’s first Starship prototype that aced a major hover test in August. Now, SpaceX is ready to build a prototype that may be able to fly into our planet’s orbit.

Droid Junkyard, Tatooine pic.twitter.com/yACFR9y04P

Researchers have made news in letting their AI ambitions play out a formidable game of hide and seek with formidable results. The agents’ environment had walls and movable boxes for a challenge where some were the hiders and others, seekers. Much happened along the way, with surprises.

Stating what was learned, the authors blogged: “We’ve observed discovering progressively more complex tool use while playing a simple game of hide-and-seek,” where the agents built “a series of six distinct strategies and counterstrategies, some of which we did not know our environment supported.”

In a new paper released earlier this week, the team revealed results. Their paper, “Emergent Tool Use from Multi-Agent Autocurricula,” had seven authors, six of which had OpenAI representation listed, and one, Google Brain.

A team of scientists has developed a computational model that uses artificial intelligence to find sites for hydropower dams in order to help reduce greenhouse gas emissions.

Hydropower dams can provide large quantities of energy with carbon footprints as low as sources like solar and wind. But because of how they’re formed, some dams emit dangerously high levels of greenhouse gases, threatening sustainability goals.

With hundreds of currently proposed for the Amazon basin—an ecologically sensitive area covering more than a third of South America—predicting their in advance could be critical for the region, and the planet.