Acting as an optics simulator, XLuminA explores all possible optical configurations.
Max Planck researchers developed an AI framework that autonomously designs microscopy experiments, optimizing it 10,000 times faster.
Category: robotics/AI – Page 265
Classification of Heterogeneous Mining Areas Based on ResCapsNet and Gaofen-5 Imagery
Land cover classification (LCC) of heterogeneous mining areas is important for understanding the influence of mining activities on regional geo-environments. Hyperspectral remote sensing images (HSI) provide spectral information and influence LCC. Convolutional neural networks (CNNs) improve the performance of hyperspectral image classification with their powerful feature learning ability. However, if pixel-wise spectra are used as inputs to CNNs, they are ineffective in solving spatial relationships. To address the issue of insufficient spatial information in CNNs, capsule networks adopt a vector to represent position transformation information. Herein, we combine a clustering-based band selection method and residual and capsule networks to create a deep model named ResCapsNet. We tested the robustness of ResCapsNet using Gaofen-5 Imagery. The images covered two heterogeneous study areas in Wuhan City and Xinjiang Province, with spatially weakly dependent and spatially basically independent datasets, respectively. Compared with other methods, the model achieved the best performances, with averaged overall accuracies of 98.45 and 82.80% for Wuhan study area, and 92.82 and 70.88% for Xinjiang study area. Four transfer learning methods were investigated for cross-training and prediction of those two areas and achieved good results. In summary, the proposed model can effectively improve the classification accuracy of HSI in heterogeneous environments.
The AI Revolution With Grease Under Its Fingernails
Saar Yoskovitz is Co-Founder & CEO at Augury, a pioneer in AI-driven Machine Health and Process Health solutions for industrial sectors.
American manufacturers are at a crossroads, needing to decide between evolution and obsolescence. The tools that historically drove profitability and efficiency are no longer having an impact. Labor is hard to find and harder to keep. The National Association of Manufacturing projects that 2.1 million manufacturing roles will go unfilled by 2030. This hard truth is compounded by findings in Augury’s State of Production Health report, which reveals that 91% of manufacturers say that the mass exodus of industry veterans will worsen the knowledge gap.
An alarming rate of brain drain is looming over the industrial sector. As tenured employees reach retirement age and fewer professionals line up to take their place, more manufacturers are turning to artificial intelligence (AI) to bridge the gap.
Self-replicating machine
A self-replicating machine is a type of autonomous robot that is capable of reproducing itself autonomously using raw materials found in the environment, thus exhibiting self-replication in a way analogous to that found in nature. Homer Jacobson, Edward F. Moore, Freeman Dyson, John von Neumann, Konrad Zuse and in more recent times by K. Eric Drexler in his book on nanotechnology, Engines of Creation (coining the term clanking replicator for such machines) and by Robert Freitas and Ralph Merkle in their review Kinematic Self-Replicating Machinesmoons and asteroid belts for ore and other materials, the creation of lunar factories, and even the construction of solar power satellites in space. The von Neumann probeuniversal constructor, a self-replicating machine that would be able to evolve and which he formalized in a cellular automata environment. Notably, Von Neumann’s Self-Reproducing Automata scheme posited that open-ended evolution requires inherited information to be copied and passed to offspring separately from the self-replicating machine, an insight that preceded the discovery of the structure of the DNA molecule by Watson and Crick and how it is separately translated and replicated in the cell.https://en.m.wikipedia.org/wiki/Self-replicating_machine#:~:...n_probe_is, [ 9 ] A self-replicating machine is an artificial self-replicating system that relies on conventional large-scale technology and automation. The concept, first proposed by Von Neumann no later than the 1940s, has attracted a range of different approaches involving various types of technology. Certain idiosyncratic terms are occasionally found in the literature. For example, the term clanking replicator was once used by Drexler [ 10 ] to distinguish macroscale replicating systems from the microscopic nanorobots or “assemblers” that nanotechnology may make possible, but the term is informal and is rarely used by others in popular or technical discussions. Replicators have also been called “von Neumann machines” after John von Neumann, who first rigorously studied the idea.
Google’s new Project Astra could be generative AI’s killer app
Google DeepMind has announced an impressive grab bag of new products and prototypes that may just let it seize back its lead in the race to turn generative artificial intelligence into a mass-market concern.
Top billing goes to Gemini 2.0—the latest iteration of Google DeepMind’s family of multimodal large language models, now redesigned around the ability to control agents—and a new version of Project Astra, the experimental everything app that the company teased at Google I/O in May.
MIT Technology Review got to try out Astra in a closed-door live demo last week. It was a stunning experience, but there’s a gulf between polished promo and live demo.
Google just launched a ton of new products—including Gemini 2.0, which could power a new world of agents. And we got a first look.
Machine learning reveals a functional network of genes and proteins in human cancer
Large-scale protein and gene profiling have massively expanded the landscape of cancer-associated proteins and gene mutations, but it has been difficult to discern whether they play an active role in the disease or are innocent bystanders.
In a study published in Nature Cancer, researchers at Baylor College of Medicine revealed a powerful and unbiased machine learning-based approach called FunMap for assessing the role of cancer-associated mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.
“Gaining functional information on the genes and proteins associated with cancer is an important step toward better understanding the disease and identifying potential therapeutic targets,” said corresponding author Dr. Bing Zhang, professor of molecular and human genetics and part of the Lester and Sue Smith Breast Center at Baylor.
Pillbot: Swallowable robot with thrusters performs endoscopy at home
PillBot’s thrusters and high-res cameras make remote stomach diagnostics a reality—revolutionizing gastroenterology.
Endiatx’s swallowable camera uses pumpjet thrusters for remote stomach exams, replacing invasive procedures and advancing telemedicine.
3D Printer Eliminates The Printer Bed
Anyone who has operated a 3D printer before, especially those new to using these specialized tools, has likely had problems with the print bed. The bed might not always be the correct temperature leading to problems with adhesion of the print, it could be uncalibrated or dirty or cause any number of other issues that ultimately lead to a failed print. Most of us work these problems out through trial and error and eventually get settled in, but this novel 3D printer instead removes the bed itself and prints on whatever surface happens to be nearby.
The printer is the product of [Daniel Campos Zamora] at the University of Washington and is called MobiPrint. It uses a fairly standard, commercially available 3D printer head but attaches it to the base of a modified robotic vacuum cleaner. The vacuum cleaner is modified with open-source software that allows it to map its environment without the need for the manufacturer’s cloud services, which in turn lets the 3D printer print on whichever surface the robot finds in its travels. The goal isn’t necessarily to eliminate printer bed problems; a robot with this capability could have many more applications in the realm of accessibility or even, in the future, printing while on the move.
There were a few surprising discoveries along the way which were mentioned in an IEEE Spectrum article, as [Campos Zamora] found while testing various household surfaces that carpet is surprisingly good at adhering to these prints and almost can’t be unstuck from the prints made on it. There are a few other 3D printers out there that we’ve seen that are incredibly mobile, but none that allow interacting with their environment in quite this way.