Lifeboat Foundation

Interesting discovery! I’d love to see it in action.

A new ferroelectric polymer that efficiently converts electrical energy into mechanical strain has been developed by Penn State researchers. This material, showing potential for use in medical devices and robotics, overcomes traditional piezoelectric limitations. Researchers improved performance by creating a polymer nanocomposite, significantly reducing the necessary driving field strength, expanding potential applications.

A new type of ferroelectric polymer that is exceptionally good at converting electrical energy into mechanical strain holds promise as a high-performance motion controller or “actuator” with great potential for applications in medical devices, advanced robotics, and precision positioning systems, according to a team of international researchers led by Penn State.

Continue reading “Artificial Muscles Flex for the First Time: Ferroelectric Polymer Innovation in Robotics” »

388 likes, — AI-GPT Insights (@aigptinsights) on Instagram: The future of Al Automations is here.

#chatgptinsights #aigpt #ai #artificialintelligence #chatgpt

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” »

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.”

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.

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As the cookieless future continues to gain momentum, the global digital advertising sector is experiencing a tectonic shift. Companies are being forced to reimagine the way they reach out to customers.

Online marketing has been dominated by third-party cookies — tracking codes posted on websites to extract users’ information — and data brokers who sell the information in bulk.

If you are looking for a side hustle and have a knack for tech and language, picking up a gig to help employers create content like LinkedIn posts, blog posts, podcast show notes and even social media posts for Twitter and Instagram using ChatGPT could prove effective. Here’s how to do it.

ChatGPT is all the rage, and it turns out businesses are hiring experts in the tool to help them create content. Here’s how to start the side hustle.