Lifeboat Foundation

MIT engineers have developed ultralight fabric solar cells that can quickly and easily turn any surface into a power source.

These durable, flexible solar cells, which are much thinner than a human hair, are glued to a strong, lightweight fabric, making them easy to install on a fixed surface. They can provide energy on the go as a wearable power fabric or be transported and rapidly deployed in remote locations for assistance in emergencies. They are one-hundredth the weight of conventional solar panels, generate 18 times more power-per-kilogram, and are made from semiconducting inks using printing processes that can be scaled in the future to large-area manufacturing.

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The quiet October 29 announcement of the partnership is light on details, stating that Trust Lab and In-Q-Tel — which invests in and collaborates with firms it believes will advance the mission of the CIA — will work on “a long-term project that will help identify harmful content and actors in order to safeguard the internet.” Key terms like “harmful” and “safeguard” are unexplained, but the press release goes on to say that the company will work toward “pinpointing many types of online harmful content, including toxicity and misinformation.”

Though Trust Lab’s stated mission is sympathetic and grounded in reality — online content moderation is genuinely broken — it’s difficult to imagine how aligning the startup with the CIA is compatible with Siegel’s goal of bringing greater transparency and integrity to internet governance. What would it mean, for instance, to incubate counter-misinformation technology for an agency with a vast history of perpetuating misinformation? Placing the company within the CIA’s tech pipeline also raises questions about Trust Lab’s view of who or what might be a “harmful” online, a nebulous concept that will no doubt mean something very different to the U.S. intelligence community than it means elsewhere in the internet-using world.

Japanese fashion mogul Yusaku Maezawa has picked eight passengers that he said will join him on a trip around the moon, powered by SpaceX’s yet-to-be-flown Starship spacecraft. The group includes American DJ Steve Aoki and popular space YouTuber Tim Dodd, better known as the Everyday Astronaut.

The mission, called Dear Moon, was first announced in 2018. Maezawa initially aimed to take a group of artists with him on a six-day trip around the moon but later announced he had expanded his definition of an “artist.” Instead, he said he would be open to people from all walks of life as long as they viewed themselves as artists, Maezawa said in a video announcement last year.

Continue reading “Full crew for SpaceX’s privately funded moon mission announced” »

https://www.youtube.com/watch?v=pJo-qmgEv7U

OpenAI released a new chatbot called ChatGPT which is insanely good in human conversation. It also writes code with very high accuracy. What is going to happen to all programming jobs and data scientist jobs now? Let’s see a demo of ChatGPT and discuss a few important concerns associated with this. Towards the end of the video, I will ask a question that will do a rest test on ChatGPT’s ability to replace programmer job, you will be amazing to see how ChatGPT responds to that specific query 😊

Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&…escription for my affordable video courses.

Continue reading “ChatGPT Writes Code 🔥 End Of All Programming Jobs? Your Mind Will Blow After Seeing This!” »

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Next generation computing needs.

Elon Musk tweeted that a view count would appear on tweets “just like videos,” and Jack Dorsey agreed that it’s a “much better metric.”

Cambridge scientists have created a comprehensive tool for predicting an individual’s risk of developing prostate cancer, which they say could help ensure that those men at greatest risk will receive the appropriate testing while reducing unnecessary—and potentially invasive—testing for those at very low risk.

CanRisk-Prostate, developed by researchers at the University of Cambridge and The Institute of Cancer Research, London, will be incorporated into the group’s CanRisk web tool, which has now recorded almost 1.2 million risk predictions. The free tool is already used by health care professionals worldwide to help predict the risk of developing breast and ovarian cancers.

Prostate cancer is the most common type of cancer in men. According to Cancer Research UK, more than 52,000 men are diagnosed with the disease each year and there are more than 12,000 deaths. Over three-quarters (78%) of men diagnosed with prostate cancer survive for over ten years, but this proportion has barely changed over the past decade in the U.K.

AlphaCode received an average ranking in the top 54.3% in simulated evaluations in recent coding competitions on the Codeforces competitive coding platform when limited to generation 10 solutions per problem. 66% of those problems, however, were solved using its first submission.

That might not sound all that impressive, particularly when compared to seemingly stronger model performances against humans in complex board games, though the researchers note that succeeding at coding competitions are uniquely difficult. To succeed, AlphaCode had to first understand complex coding problems in natural languages and then “reason” about unforeseen problems rather than simply memorizing code snippets. AlphaCode was able to solve problems it hadn’t seen before, and the researchers claim they found no evidence that their model simply copied core logix from the training data. Combined, the researchers say those factors make AlphaCode’s performance a “big step forward.”

Google’s DeepMind AI division has tackled everything from StarCraft to protein folding. So it’s probably no surprise that its creators have eventually turned to what is undoubtedly a personal interest: computer programming. In Thursday’s edition of Science, the company describes a system it developed that produces code in response to programming typical of those used in human programming contests.

On an average challenge, the AI system could score near the top half of participants. But it had a bit of trouble scaling, being less likely to produce a successful program on problems where more code is typically required. Still, the fact that it works at all without having been given any structural information about algorithms or programming languages is a bit of a surprise.