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Category: information science

Deformable lens enables real-time correction of image aberrations in single-pixel microscopy

Researchers from the Optics Group at the Universitat Jaume I in Castellón have managed to correct in real time problems related to image aberrations in single-pixel microscopy using a recent technology: programmable deformable lenses. The new method was described by the research team in an open-access article recently published in Nature Communications and is part of the development of the European CONcISE project.

The solution proposed by this team combines an adaptive lens (which “shapes” the light wavefront in real time) with a sensorless method that evaluates image sharpness directly from the data, without complex algorithms. This approach corrects distortions caused both by the system and by the sample itself, producing sharper images, close to the physical resolution limit, without adding complexity to the microscope.

This adaptive lens is known as a “multi-actuator adaptive lens” (M-AL), which can be easily integrated into the system without significantly modifying the traditional configuration of a single-pixel microscope based on structured illumination. These types of lenses consist of an optically transparent and deformable membrane (similar to a thin sheet of glass or polymer) that can change shape via actuators distributed around or behind it.

Atom-thin, content-addressable memory enables edge AI applications

Recent advances in the field of artificial intelligence (AI) have opened new exciting possibilities for the rapid analysis of data, the sourcing of information and the generation of use-specific content. To run AI models, current hardware needs to continuously move data from internal memory components to processors, which is energy-intensive and can increase the time required to tackle specific tasks.

Over the past few years, engineers have been trying to develop new systems that could overcome this limitation, running AI algorithms more reliably and efficiently. One proposed solution is the development of in-memory computing systems.

Content-addressable memory (CAM) is one of the earliest in-memory computing hardware systems, where memory components search for stored data faster, comparing each stored entry simultaneously based on its content, but faces challenges for AI applications because of the fundamental limitation of silicon transistors.

The 6 Steps to Reach the Singularity. Ep #114

The 6 steps to reach the singularity.

## The technological singularity, a point where AI surpasses human intelligence, is predicted to occur by 2045 and will profoundly transform humanity, requiring proactive adaptation and integration of AI into daily life ## ## Questions to inspire discussion.

Advancing AI and Machine Learning.

🧠 Q: How can we progress towards autonomous machine learning? A: Shift from supervised to unsupervised learning, enabling AI to identify patterns and make predictions without labeled data, thus advancing towards independent learning and improvement.

🤖 Q: What is the significance of achieving Artificial General Intelligence (AGI)? A: AGI represents the pinnacle of AI development, capable of matching or surpassing human-level intelligence across various domains, potentially leading to an unprecedented technological growth boom.

🧬 Q: What are initial steps towards neural augmentation? A: Develop brain-interfacing technologies to enhance specific aspects of human cognition, such as implants or non-invasive devices for improving memory, processing speed, or sensory perception.

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Discrimination of normal from slow-aging mice by plasma metabolomic and proteomic features

Tests that can predict whether a drug is likely to extend mouse lifespan could speed up the search for anti-aging drugs. We have applied a machine learning algorithm, XGBoost regression, to seek sets of plasma metabolites (n = 12,000) and peptides (n = 17,000) that can discriminate control mice from mice treated with one of five anti-aging interventions (n = 278 mice). When the model is trained on any four of these five interventions, it predicts significantly higher lifespan extension in mice exposed to the intervention which was not included in the training set. Plasma peptide data sets also succeed at this task. Models trained on drug-treated normal mice also discriminate long-lived mutant mice from their respective controls, and models trained on males can discriminate drug-treated from control females.

One image is all robots need to find their way

While the capabilities of robots have improved significantly over the past decades, they are not always able to reliably and safely move in unknown, dynamic and complex environments. To move in their surroundings, robots rely on algorithms that process data collected by sensors or cameras and plan future actions accordingly.

Researchers at Skolkovo Institute of Science and Technology (Skoltech) have developed SwarmDiffusion, a new lightweight Generative AI model that can predict where a robot should go and how it should move relying on a single image. SwarmDiffusion, introduced in a paper pre-published on the server arXiv, relies on a diffusion model, a technique that gradually adds noise to input data and then removes it to attain desired outputs.

“Navigation is more than ‘seeing,” a robot also needs to decide how to move, and this is where current systems still feel outdated,” Dzmitry Tsetserukou, senior author of the paper, told Tech Xplore.

Algorithm matches drugs to glioblastoma’s diverse cell types, offering hope for individualized therapies

Researchers have developed a new computational approach that uncovers possible drugs for specific cellular targets for treating glioblastoma, a lethal brain tumor. This approach enabled them to predict more effective treatment combinations to fight the disease on an individualized basis.

This laboratory and computational research effort was led by scientists at Georgetown’s Lombardi Comprehensive Cancer Center.

“The cellular targets we identified could be key to effectively fighting a disease that has seen only one new targeted drug approved in the last two decades,” says Nagi G. Ayad, Ph.D., senior author, associate director for translational research, and professor of oncology at Georgetown Lombardi.

Quantum mechanics works, but it doesn’t describe reality

Physicists like Sean Carroll propose not only that quantum mechanics is not only a valuable way of interpreting the world, but actually describes reality, and that the wave function – the centre equation of quantum mechanics – describes a real object.

But, in this article, philosophers Raoni Arroyo and Jonas R. Becker Arenhart argue that the case for wave function realism is deeply confused. While it is a useful component within quantum theory, this alone doesn’t justify treating it as literally real.

Tap the link to read more.

Physicists like Sean Carroll argue not only that quantum mechanics is not only a valuable way of interpreting the world, but actually describes reality, and that the central equation of quantum mechanics – the wave function – describes a real object in the world. But philosophers Raoni Arroyo and Jonas R. Becker Arenhart warn that the arguments for wave-function realism are deeply confused. At best, they show only that the wave function is a useful element inside the theoretical framework of quantum mechanics. But this goes no way whatsoever to showing that this framework should be interpreted as true or that its elements are real. The wavefunction realists are confusing two different levels of debate and lack any justification for their realism. The real question is: does a theory need to be true to be useful?

1. Wavefunction realism

Quantum mechanics is probably our most successful scientific theory. So, if one wants to know what the world is made of, or how the world looks at the fundamental level, one is well-advised to search for the answers in this theory. What does it say about these problems? Well, that is a difficult question, with no single answer. Many interpretative options arise, and one quickly ends up in a dispute about the pros and cons of the different views. Wavefunction realists attempt to overcome those difficulties by looking directly at the formalism of the theory: the theory is a description of the behavior of a mathematical entity, the wavefunction, so why not think that quantum mechanics is, fundamentally, about wavefunctions? The view that emerges is, as Alyssa Ney puts it, that.

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Elon Musk on AGI Timeline, US vs China, Job Markets, Clean Energy & Humanoid Robots

Questions to inspire discussion.

🤖 Q: How quickly will AI and robotics replace human jobs? A: AI and robotics will do half or more of all jobs within the next 3–7 years, with white-collar work being replaced first, followed by blue-collar labor through humanoid robots.

🏢 Q: What competitive advantage will AI-native companies have? A: Companies that are entirely AI-powered will demolish competitors, similar to how a single manually calculated cell in a spreadsheet makes it unable to compete with entirely computer-based spreadsheets.

💼 Q: What forces companies to adopt more AI? A: Companies using more AI must outcompete those using less, creating a forcing function for increased AI adoption, as inertia currently keeps humans doing AI-capable tasks.

📊 Q: How much of enterprise software development can AI handle autonomously? A: Blitzy, an AI platform using thousands of specialized agents, autonomously handles 80%+ of enterprise software development, increasing engineering velocity 5x when paired with human developers.

Energy and Infrastructure.

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