One particularly promising method within ADAS involves defining agents in code and using a meta-agent—an AI that can create and improve…
S Hu, C Lu, J Clune [University of British Columbia] (2024) paper: https://arxiv.org/abs/2408.08435 website:
Can AI agents design better AI agents?
In the rapidly advancing field of artificial intelligence, researchers are…
Project page for Automated Design of Agentic Systems.
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KAN 2.0: Kolmogorov-Arnold Networks Meet Science.
Ziming Liu, Pingchuan Ma, Yixuan Wang, Wojciech Matusik, Max Tegmark MIT 2024.
Artificial Intelligence (AI) and science are two powerful forces that seem, on the surface, to be at odds with each other.
Kolmogorov Arnold Networks. Contribute to KindXiaoming/pykan development by creating an account on GitHub.
IIT reveals advances in the world’s first jet-powered humanoid robot, showcasing its experimental area and early validation efforts.
Summary: A new machine learning model, AutMedAI, can predict autism in children under two with nearly 80% accuracy, offering a promising tool for early detection and intervention.
The model analyzes 28 parameters available before 24 months, such as age of first smile and eating difficulties, to identify children likely to have autism. Early diagnosis is crucial for optimal development, and further validation of the model is underway.
Quantum simulation enables scientists to simulate and study complex systems that are challenging or even impossible using classical computers across various fields, including financial modeling, cybersecurity, pharmaceutical discoveries, AI and machine learning. For instance, exploring molecular vibronic spectra is critical in understanding the molecular properties in molecular design and analysis.
Engineers have designed a tiny battery, smaller than a grain of sand, to power microscopic robots for jobs such as drug delivery or locating leaks in gas pipelines.
A tiny battery designed by MIT engineers could enable the deployment of cell-sized, autonomous robots for drug delivery within in the human body, as well as other applications such as locating leaks in gas pipelines.
The new battery, which is 0.1 millimeters long and 0.002 millimeters thick — roughly the thickness of a human hair — can capture oxygen from air and use it to oxidize zinc, creating a current with a potential of up to 1 volt. That is enough to power a small circuit, sensor, or actuator, the researchers showed.
“We think this is going to be very enabling for robotics,” says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT and the senior author of the study. “We’re building robotic functions onto the battery and starting to put these components together into devices.”
Engineering researchers at the University of Minnesota Twin Cities have demonstrated a state-of-the-art hardware device that could reduce energy consumption for artificial intelligent (AI) computing applications by a factor of at least 1,000.
The research is published in npj Unconventional Computing titled “Experimental demonstration of magnetic tunnel junction-based computational random-access memory.” The researchers have multiple patents on the technology used in the device.
With the growing demand for AI applications, researchers have been looking at ways to create a more energy efficient process, while keeping performance high and costs low. Commonly, machine or artificial intelligence processes transfer data between both logic (where information is processed within a system) and memory (where the data is stored), consuming a large amount of power and energy.
ROCHESTER, Minn. — Mayo Clinic scientists are using artificial intelligence (AI) and machine learning to analyze electroencephalogram (EEG) tests more quickly and precisely, enabling neurologists to find early signs of dementia among data that typically go unexamined.
The century-old EEG, during which a dozen or more electrodes are stuck to the scalp to monitor brain activity, is often used to detect epilepsy. Its results are interpreted by neurologists and other experts trained to spot patterns among the test’s squiggly waves.
In new research published in Brain Communications, scientists at the Mayo Clinic Neurology AI Program (NAIP) demonstrate how AI can not only speed up analysis, but also alert experts reviewing the test results to abnormal patterns too subtle for humans to detect. The technology shows the potential to one day help doctors distinguish among causes of cognitive problems, such as Alzheimer’s disease and Lewy body dementia. The research suggests that EEGs, which are more widely available, less expensive and less invasive than other tests to capture brain health, could be a more accessible tool to help doctors catch cognitive issues in patients early.