Lifeboat Foundation

Reservoir computing is a promising computational framework based on recurrent neural networks (RNNs), which essentially maps input data onto a high-dimensional computational space, keeping some parameters of artificial neural networks (ANNs) fixed while updating others. This framework could help to improve the performance of machine learning algorithms, while also reducing the amount of data required to adequately train them.

RNNs essentially leverage recurrent connections between their different processing units to process sequential data and make accurate predictions. While RNNs have been found to perform well on numerous tasks, optimizing their performance by identifying parameters that are most relevant to the task they will be tackling can be challenging and time-consuming.

Jason Kim and Dani S. Bassett, two researchers at University of Pennsylvania, recently introduced an alternative approach to design and program RNN-based reservoir computers, which is inspired by how programming languages work on computer hardware. This approach, published in Nature Machine Intelligence, can identify the appropriate parameters for a given network, programming its computations to optimize its performance on target problems.

That was fast.

We’ve heard plenty of rumors about Windows 12 this year. While Microsoft has yet to officially confirm it is in the works, there have been several hints pointing to its existence. One of these came at the Build 2023 developer conference in the form of a video screenshot that referred to a “next generation” of Windows. That presumably refers to Windows 12 and hopefully not a fully cloud-based Windows 11.

Microsoft has also referred to a “Next Valley Prototype Design,” said to be a codename for the next-generation of Windows.

Continue reading “Windows 12 rumored to arrive in fall 2024 with a floating taskbar and a focus on AI” »

Dr. Varsha Ramachandran from the Center for Astronomy of Heidelberg University (ZAH) and her colleagues uncovered the first “stripped” star of intermediate-mass. This discovery marks a missing link in our picture of stellar evolution toward systems with merging neutron stars, which are crucial to our understanding of the origin of heavy elements, such as silver and gold. Dr. Ramachandran is a postdoc in the research group of Dr. Andreas Sander, located at ZAH’s Astronomisches Rechen-Institut (ARI). These results were now published in Astronomy & Astrophysics.

The team of researchers discovered the first representative of the long-predicted, but as yet unconfirmed population of intermediate-mass stripped stars. “Stripped stars” are stars that have lost most of their outer layers, revealing their hot and dense helium-rich core, which results from the nuclear fusion of hydrogen to helium. Most of these stripped stars are formed in binary star systems in which one star’s strong gravitational pull peels off and accretes matter from its companion.

For a long time, astrophysicists have known of low-mass stripped stars, known as subdwarfs, as well as their massive cousins, known as Wolf-Rayet stars. But until now, they have never been able to find any of the so-called “intermediate-mass stripped stars,” raising questions whether our basic theoretical picture needs a major revision.

NASA technicians have installed the heat shield on the Artemis 2 Orion spacecraft, which will carry astronauts around the moon in late 2024.

Associated Press.

“What a silent tension,” said Sophia as she read the room. Sophia is developed by Hanson Robotics and is the first robot innovation ambassador for the UN Development Program. “Humanoid robots have the potential to lead with a greater level of efficiency and effectiveness than human leaders.”

Use WebMD’s slideshow to find out why symptoms like arm pain, fatigue, lightheadedness, and nausea are signs of heart trouble — and what you should do about it.

When thinking of the applications of low-loss materials in 5G devices, the first that comes to mind for most is 5G smartphones. After all, low-loss materials are integral in enabling advanced 5G antennas-in-packages (AiP) for smartphones, and high-profile smartphone manufacturers like Apple have gone back and forth about which low-loss material they chose for their 5G antennas in their flagship phones like the iPhone. However, another 5G application for low-loss materials hiding in plain sight will act as an important driver for this US$1.8 billion market as forecast by IDTechEx: 5G customer premises equipment (CPEs).

5G fixed wireless access (5G FWA) and cpes.

This application area is based on the increasing deployment of 5G fixed wireless access (FWA). FWA offers wireless internet access to homes and businesses without requiring the laying of fiber or cables to facilitate connectivity. FWA enables customers to get high-speed internet access with speeds comparable to wired broadband connections in places where fiber or cable installation is too expensive or difficult. While fixed wireless technologies based on 4G/LTE do exist, they cannot match the speeds of wired broadband and are also not economically viable to deploy.

Engineers at the University of Illinois Urbana-Champaign have developed a new test that can predict the durability of cement in seconds to minutes—rather than the hours it takes using current methods. The test measures the behavior of water droplets on cement surfaces using computer vision on a device that costs less than $200. The researchers said the new study could help the cement industry move toward rapid and automated quality control of their materials.

The results of the study, led by Illinois civil and environmental engineering professor Nishant Garg, are reported in the journal npj Materials Degradation. The paper is titled “Rapid prediction of cementitious initial sorptivity via surface wettability.”

Continue reading “Engineers develop fast, automated, affordable test for cement durability” »

The competitive nature of AI development poses a dilemma for organizations, as prioritizing speed may lead to neglecting ethical guidelines, bias detection, and safety measures. Known and emerging concerns associated with AI in the workplace include the spread of misinformation, copyright and intellectual property concerns, cybersecurity, data privacy, as well as navigating rapid and ambiguous regulations. To mitigate these risks, we propose thirteen principles for responsible AI at work.

Page-utils class= article-utils—vertical hide-for-print data-js-target= page-utils data-id= tag: blogs.harvardbusiness.org, 2007/03/31:999.359663 data-title=13 Principles for Using AI Responsibly data-url=/2023/06/13-principles-for-using-ai-responsibly data-topic= Technology and analytics data-authors= Brian Spisak; Louis B. Rosenberg; Max Beilby data-content-type= Digital Article data-content-image=/resources/images/article_assets/2023/06/Jun23_30_200245321-001-383x215.jpg data-summary=

Companies need to consider a set of risks as they explore how to adopt new tools.