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Researchers at the University of Toronto and the Barcelona Institute of Science and Technology have recently created new solution-processed perovskite photodetectors that exhibit remarkable efficiencies and response times. These photodetectors, introduced in a paper published in Nature Electronics, have a unique design that prevents the formation of defects between its different layers.

“There is growing interest in 3D range imaging for autonomous driving and consumer electronics,” Edward H. Sargent told TechXplore. “We have worked as a team for years on finding new materials that enable light sensing technologies such as next-generation image sensors and striving to take these in a direction that could have a commercial and societal impact.”

Photodetectors, sensing devices that detect or respond to light, can have numerous highly valuable applications. For instance, they can be integrated in robotic systems, autonomous vehicles, , environmental sensing technology, fiber optic communication systems and security systems.

This week our guest is author and technologist, David Weinberger, who has spent years lecturing at Harvard as well as acting as a fellow and senior researcher at the renowned Berkman Klein Center for Internet & Society. And just prior to covid, David released his latest book, Everyday Chaos: Technology, Complexity, and How We’re Thriving in a New World of Possibility. In this episode, David and I explore some of the key ideas he focused on in Everyday Chaos. This includes looking at the ways in which we have historically used reductionist thinking to make generalizations for society, products, and technology, and how the latest technologies like the internet and Machine learning are revealing how much more we can thrive when we embrace chaos and customization. This means letting individuals and data tell us what people want by exploring all the possibilities rather than attempting to predict and shape outcomes beforehand.

** Find out more about David at his website weinberger.org and buy his book at everydaychaosbook.com.

55 MINS

https://youtube.com/watch?v=PDLXaSV4qCQ&feature=share

KRAFTWERK “The Robots” Dmitriy N’Elpin & Inesa Shaurouskaya (Cover tribute. Version 2)

✅ Dmitriy N’Elpin – idea, arrangement, synthesizers, drums, guitar, vocals, vocals-vocoder, mastering.
✅ Inesa Shaurouskaya – synthesizers, special effects, vocals — vocoder.

Video editing – Andrey Grozovskiy.

Fan club — https://www.facebook.com/groups/24033

Gas turbines are widely used for power generation and aircraft propulsion. According to the laws of thermodynamics, the higher the temperature of an engine, the higher the efficiency. Because of these laws, there is an emerging interest in increasing turbines’ operating temperature.

A team of researchers from the Department of Materials Science and Engineering at Texas A&M University, in conjunction with researchers from Ames National Laboratory, have developed an artificial intelligence framework capable of predicting (HEAs) that can withstand extremely high temperature, oxidizing environments. This method could significantly reduce the time and costs of finding alloys by decreasing the number of experimental analyses required.

This research was recently published in Material Horizons.

I have a question for you that seems to be garnering a lot of handwringing and heated debates these days. Are you ready? Will humans outlive AI? Mull that one over. I am going to unpack the question and examine closely the answers and how the answers have been elucidated. My primary intent is to highlight how the question itself and the surrounding discourse are inevitably and inexorably rooted in AI Ethics.


A worthy question is whether humans will outlive AI, though the worthiness of the question is perhaps different than what you think it is. All in all, important AI Ethics ramifications arise.

Facebook (now Meta) popularized the Silicon Valley ethos with the saying “Move fast and break things”. This approach might have worked when disrupting the social media business, but it’s causing all sorts of problems for them as well as other major AI players. Breaking things and moving fast might be the reason why so many AI projects are failing. According to an MIT study, over 85% of AI projects fail to deliver their stated objectives, and 70% of data science projects never make it to fruition. Clearly moving fast and breaking things doesn’t work if you’re not getting closer to success.

There’s a difference between Iterating to Success and Breaking Things.


Early AI winners align organizational and business strategies to build value and manage risk.