Toggle light / dark theme

One of the most important issues in contemporary societies is the impact of automation and intelligent technologies on human work. Concerns with the impact of mechanization on jobs and unemployment go back centuries, at least since the late 1,500 ’ s, when Queen Elizabeth I turned down William Lee ’ s patent applications for an automated knitting machine for stockings because of fears that it might turn human knitters into paupers. [2] In 1936, an automotive industry manager at General Motors named D.L. Harder coined the term “automation” to refer to the automatic operation of machines in a factory setting. Ten years later, when he was a Vice President at Ford Motor company, he established an “Automation Department” which led to widespread usage of the term. [3]

The origins of intelligent automation trace back to US and British advances in fire-control radar for operating anti-aircraft guns to defend against German V-1 rockets and aircraft during World War II. After the war, these advances motivated the MIT mathematician Norbert Weiner to develop the concept of “cybernetics”, a theory of machines and their potential based on feedback loops, self-stabilizing systems, and the ability to autonomously lean and adapt behavior. [4] In parallel, the Dartmouth Summer Research Project on Artificial Intelligence workshop was held in 1956 and is recognized as the founding event of artificial intelligence as a research field. [5]

Since that decade, workplace automation, cybernetic-inspired advanced feedback systems for both analogue and digital machines, and digital computing based artificial intelligence (together with the overall field of computer science) have advanced in parallel and co-mingled with one another. Additionally, opposing views of these developments have co-existed with one side highlighting the positive potential for more capable and intelligent machines to serve, benefit and elevate humanity, and the other side highlighting the negative possibilities and threats including mass unemployment, physical harm and loss of control. There has been a steady stream of studies from the 1950 ’ s to the present assessing the impacts of machine automation on the nature of work, jobs and employment, with each more recent study considering the capability enhancements of the newest generation of automated machines.

In aviation, any advancement in design must either reduce weight or the benefit has to be worth the extra weight. Researchers at the University of Bath seem to have achieved the perfect balance between the two by developing a way to reduce aircraft engine noise by up to 80% while adding almost no extra weight.

As Green Car Congress reports, the research team at the University of Bath developed a graphene oxide-polyvinyl alcohol aerogel, which only weighs 2.1kg (4.6lbs) per cubic meter and therefore makes it the lightest sound insulation ever manufactured.


Researchers developed a graphene aerogel that reduces engine noise to the same level as a hair dryer.

Exactly one month ago today, Elation Hypercars threw its hat into the ring and unveiled its first four-wheeled beast known as Freedom. The all-electric hypercar, which promises a staggering 1,400 horses and a 400-mile range, is due to be delivered in 2022 and now has its first prototype.


It was named after a hunting dog and is equally fierce.

The Wright brothers had designed the world’s first successful, heavier-than-air, powered airplane.

Find U.S. Department of Energy (DOE)-funded research about the Wright brothers’ innovative approach to development on OSTI.GOV:
• Accelerating Learning with Set-Based Concurrent Engineering: https://www.osti.gov/biblio/1605517
• Control Co-Design: An engineering game changer: https://www.osti.gov/biblio/1615248
• Engineering a Better Future: Interplay between Engineering Social Sciences and Innovation: https://www.osti.gov/biblio/1530161

Next week, Audi will open what it is calling “world’s first” charging hub concept in Nuremberg, Germany, complete with reservation options and a lounge area. Audi’s pilot project is intended to test charging solutions for the impending demand for EV infrastructure, particularly in urban areas where drivers might not have access to charging at home.

Audi AG is a German automaker founded over 100 years ago. Known for its automotive offerings in the luxury and performance segment, the company has recently begun to shift its vehicle lineup toward electrification, following suit with its parent company Volkswagen Group.

This past summer, Audi announced its electrification strategy, which includes an end date of 2033 for all new ICE models.

Welcome to Web 3.0.

It’s happening.

Major human-focused industries are injecting virtual interactions into the very design of next-gen vehicles, as Boeing announced that its 3D engineering designs will have digital twins that speak to each other via “robots” that converse, while human mechanics at factories throughout the world will be linked via $3,500 HoloLens headsets developed by Microsoft itself, according to an initial report from Reuters.

In other words, Boeing just took a major step into Web 3.0, with airline service operations and production becoming unified within a single digital ecosystem. And it could happen in just two years.

Boeing wants to enter 2022 fighting for engineering dominance Critics of Boeing cite the firm’s previous commitments to triggering an imminent digital revolution. But insiders familiar with Boeing’s announcement say its general aims to improve safety and quality have gained a stronger sense of urgency and significance through the aerospace company’s struggles with several threats. Nevertheless, Boeing plans to fly into 2022 fighting for its engineering dominance in the industry following the 737 MAX crisis, while also preparing for a future aircraft program in the coming decade. But make no mistake, this is a $15-billion gamble. And to make good on its pledge, Boeing will also have to develop a means of preventing manufacturing issues, like the structural flaws that delayed its 787 Dreamliner in 2021.

Full Story:

And he’s been searching for it for a decade.

It’s a nightmare scenario that might become increasingly common in a world of digital currency. A man threw away an old PC hard drive while doing a quick spring clean of his home in Newport Wales, U.K., in 2013. Fast-forward almost a decade and he’s still desperately petitioning to be allowed to go through his local landfill.

The reason the man, 35-year-old IT engineer James Howells, wants to trawl through his local trash site is that the hard drive he threw out included a wallet with 7,500 Bitcoin.

At the time he threw out the hard drive, that amount of Bitcoin would have been worth 665 thousand dollars (500 thousand pounds). Today, it would have made him a millionaire, as it would be worth a total of 357 million dollars.

In an interview with The Guardian in 2013, Howells explained how he had been looking through the landfill, which is roughly the size of a football field. “I had a word with one of the guys down there, explained the situation. And he actually took me out in his truck to where the landfill site is, the current ditch they’re working on. It’s about the size of a football field, and he said something from three or four months ago would be about three or four feet down,” Howells explained.

Roughly $140 billion in ‘lost’ Bitcoin worldwide Following initial scavenging efforts for the lost hard drive, Howells seemed resigned to losing the digital fortune. More recently, however, he has recruited local residents in Newport to help him search for the device. Anyone who helps him find it, he says, is promised to be rewarded millions — if the hard drive is still readable, that is. The 35-year-old also offered to donate 25 percent of the potential findings — roughly 70 million dollars– to a “Covid Relief Fund” for his home city.

Full Story:

Elon Musk, the billionaire CEO of Tesla, is defending himself once again against public criticism. It is time for Tesla CEO Elon Musk to answer to questions regarding his income tax bill.

“If you opened your eyes for two seconds, you would know that I will pay more taxes than any American in history this year.” Musk tweeted earlier this week.

Tesla CEO Elon Musk tweeted a reaction to Massachusetts Sen. Elizabeth Warren’s criticism of him as Time Magazine’s Person of the Year for not paying his taxes.

Most often, we recognize deep learning as the magic behind self-driving cars and facial recognition, but what about its ability to safeguard the quality of the materials that make up these advanced devices? Professor of Materials Science and Engineering Elizabeth Holm and Ph.D. student Bo Lei have adopted computer vision methods for microstructural images that not only require a fraction of the data deep learning typically relies on but can save materials researchers an abundance of time and money.

Quality control in materials processing requires the analysis and classification of complex material microstructures. For instance, the properties of some high strength steels depend on the amount of lath-type bainite in the material. However, the process of identifying bainite in microstructural images is time-consuming and expensive as researchers must first use two types of to take a closer look and then rely on their own expertise to identify bainitic regions. “It’s not like identifying a person crossing the street when you’re driving a car,” Holm explained “It’s very difficult for humans to categorize, so we will benefit a lot from integrating a .”

Their approach is very similar to that of the wider computer-vision community that drives facial recognition. The model is trained on existing material microstructure images to evaluate new images and interpret their classification. While companies like Facebook and Google train their models on millions or billions of images, materials scientists rarely have access to even ten thousand images. Therefore, it was vital that Holm and Lei use a “data-frugal method,” and train their model using only 30–50 microscopy images. “It’s like learning how to read,” Holm explained. “Once you’ve learned the alphabet you can apply that knowledge to any book. We are able to be data-frugal in part because these systems have already been trained on a large database of natural images.”