Assistive technology services are integrating OpenAI’s GPT-4, using artificial intelligence to help describe objects and people.
Category: robotics/AI – Page 796
Artificial intelligence algorithms, such as the sophisticated natural language processor ChatGPT, are raising hopes, eyebrows and alarm bells in multiple industries. A deluge of news articles and opinion pieces, reflecting both concerns about and promises of the rapidly advancing field, often note AI’s potential to spread misinformation and replace human workers on a massive scale. According to Jonathan Chen, MD, PhD, assistant professor of medicine, the speculation about large-scale disruptions has a kernel of truth to it, but it misses another element when it comes to health care: AI will bring benefits to both patients and providers.
Chen discussed the challenges with and potential for AI in health care in a commentary published in JAMA on April 28. In this Q&A, he expands on how he sees AI integrating into health care.
The algorithms we’re seeing emerge have really popped open Pandora’s box and, ready or not, AI will substantially change the way physicians work and the way patients interact with clinical medicine. For example, we can tell our patients that they should not be using these tools for medical advice or self-diagnosis, but we know that thousands, if not millions, of people are already doing it — typing in symptoms and asking the models what might be ailing them.
Artificial intelligence (AI) can help people shop, plan, and write—but not cook. It turns out humans aren’t the only ones who have a hard time following step-by-step recipes in the correct order, but new research from the Georgia Institute of Technology’s College of Computing could change that.
The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research, such as drug design and energy storage. However, the lack of a simulation technique that offers both high fidelity and scalability across different time and length scales has long been a roadblock for the progress of these technologies.
Researchers from the Center for Advanced Systems Understanding (CASUS) at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) in Görlitz, Germany, and Sandia National Laboratories in Albuquerque, New Mexico, U.S., have now pioneered a machine learning–based simulation method that supersedes traditional electronic structure simulation techniques.
Their Materials Learning Algorithms (MALA) software stack enables access to previously unattainable length scales. The work is published in the journal npj Computational Materials.
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.
Mechanical strain, how a material changes shape when force is applied, is an important property for an actuator, which is any material that will change or deform when an external force such as electrical energy is applied. Traditionally, these actuator materials were rigid, but soft actuators such as ferrroelectric polymers display higher flexibility and environmental adaptability.
388 likes, — AI-GPT Insights (@aigptinsights) on Instagram: The future of Al Automations is here.
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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.
Windows Latest notes that Microsoft accidentally teased a version of its OS with a floating taskbar at the company’s 2023 Ignite conference. It’s believed to be part of internal testing that’s exploring new design changes for the next Windows.
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.”
“Concrete is one of the most consumed materials on our planet, second only to water,” Garg said. “Over time, the concrete used to build our infrastructure degrades over time via exposure to deicing salts; freeze and thaw cycles; and ingress of water—all of which can lead to corrosion of the rebar that is used to strengthen the structures. Ultimately, this leads to failure, sometimes catastrophically, as seen in the 2021 condominium collapse in Surfside, Florida, where 98 lives were lost.”