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📣Just announced at [#GTC25](https://www.facebook.com/hashtag/gtc25?__eep__=6&__cft__[0]=AZXGE68SvdjQyRxtqhq57u6xDScMuziTjPrrOj7ic9_n1QMWssMuQdAZ4MLZmg3kpo3u92u-w_Z12HEaFeSJnvxJ_h_dNAloE8I86x4WxG8730kGwR10dtKo0yYVmS4GQdeMF0xu2E5mpp8VTUcHoNIO&__tn__=*NK-R): NVIDIA will be open-sourcing cuOpt, an AI-powered decision optimization engine.

âžĄïž [ https://nvda.ws/43REYuW](https://nvda.ws/43REYuW open-sourcing this powerful solver, developers can harness real-time optimization at an unprecedented scale for free.

The best-known AI applications are all about predictions — whether forecasting weather or generating the next word in a sentence. But prediction is only half the challenge. The real power comes from acting on information in real time.

That’s where cuOpt comes in.

CuOpt dynamically evaluates billions of variables — inventory levels, factory output, shipping delays, fuel costs, risk factors and regulations — and delivers the best move in near real time.

Unlike traditional optimization methods that navigate solution spaces sequentially or with limited parallelism, cuOpt taps into GPU acceleration to evaluate millions of possibilities simultaneously — finding optimal solutions exponentially faster for specific instances.

It doesn’t replace existing techniques — it enhances them. By working alongside traditional solvers, cuOpt rapidly identifies high-quality solutions, helping CPU-based models discard bad paths faster.

International Iberian Nanotechnology Laboratory (INL) researchers have developed a neuromorphic photonic semiconductor neuron capable of processing optical information through self-sustained oscillations. Exploring the use of light to control negative differential resistance (NDR) in a micropillar quantum resonant tunneling diode (RTD), the research indicates that this approach could lead to highly efficient light-driven neuromorphic computing systems.

Neuromorphic computing seeks to replicate the information-processing capabilities of biological neural networks. Neurons in rely on rhythmic burst firing for sensory encoding, , and network synchronization, functions that depend on oscillatory activity for signal transmission and processing.

Existing neuromorphic approaches replicate these processes using electrical, mechanical, or thermal stimuli, but optical-based systems offer advantages in speed, energy efficiency, and miniaturization. While previous research has demonstrated photonic synapses and artificial afferent nerves, these implementations require additional circuits that increase power consumption and complexity.

About 100 million metric tons of high-density polyethylene (HDPE), one of the world’s most commonly used plastics, are produced annually, using more than 15 times the energy needed to power New York City for a year and adding enormous amounts of plastic waste to landfills and oceans.

Cornell chemistry researchers have found ways to reduce the environmental impact of this ubiquitous —found in milk jugs, shampoo bottles, playground equipment and many other things—by developing a machine-learning model that enables manufacturers to customize and improve HDPE materials, decreasing the amount of material needed for various applications. It can also be used to boost the quality of recycled HDPE to rival new, making recycling a more practical process.

“Implementation of this approach will facilitate the design of next-generation commodity materials and enable more efficient polymer recycling, lowering the overall impact of HDPE on the environment,” said Robert DiStasio Jr., associate professor of chemistry and chemical biology in the College of Arts and Sciences (A&S).

James Fodor discusses what he is researching, mind uploading etc.

As of 2020, James Fodor, is a student at the Australian National University, in Canberra, Australia. James’ studies at university have been rather diverse, and have at different times included history, politics, economics, philosophy, mathematics, computer science, physics, chemistry, and biology. Eventually he hopes to complete a PhD in the field of computational neuroscience.

James also have a deep interest in philosophy, history, and religion, which he periodically writes about on his blog, which is called The Godless Theist. In addition, James also has interests in and varying levels of involved in skeptical/atheist activism, effective altruism, and transhumanism/emerging technologies. James is a fan of most things sci-fi, including Star Trek, Dr Who, and authors such as Arthur C. Clarke and Isaac Asimov.

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Contemplate a future where tiny, energy-efficient brain-like networks guide autonomous machines—like drones or robots—through complex environments. To make this a reality, scientists are developing ultra-compact communication systems where light, rather than electricity, carries information between nanoscale devices.

In this study, researchers achieved a breakthrough by enabling direct on-chip communication between tiny light-sensing devices called InP nanowire photodiodes on a silicon chip. This means that light can now travel efficiently from one nanoscale component to another, creating a faster and more energy-efficient network. The system proved robust, handling signals with up to 5-bit resolution, which is similar to the information-processing levels in biological neural networks. Remarkably, it operates with minimal energy—just 0.5 microwatts, which is lower than what conventional hardware needs.

S a quadrillionth of a joule!) and allow one emitter to communicate with hundreds of other nodes simultaneously. This efficient, scalable design meets the requirements for mimicking biological neural activity, especially in tasks like autonomous navigation. + In essence, this research moves us closer to creating compact, light-powered neural networks that could one day drive intelligent machines, all while saving space and energy.

The future of AI is here—and it’s running on human brain cells! In a groundbreaking development, scientists have created the first AI system powered by biological neurons, blurring the line between technology and biology. But what does this mean for the future of artificial intelligence, and how does it work?

This revolutionary AI, known as “Brainoware,” uses lab-grown human brain cells to perform complex tasks like speech recognition and decision-making. By combining the adaptability of biological neurons with the precision of AI algorithms, researchers have unlocked a new frontier in computing. But with this innovation comes ethical questions and concerns about the implications of merging human biology with machines.

In this video, we’ll explore how Brainoware works, its potential applications, and the challenges it faces. Could this be the key to creating truly intelligent machines? Or does it raise red flags about the ethical boundaries of AI research?

What is Brainoware, and how does it work? What are the benefits and risks of AI powered by human brain cells? How will this technology shape the future of AI? This video answers all these questions and more. Don’t miss the full story—watch until the end!

#ai.
#artificialintelligence.
#ainews.

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Guiding light around dynamic regions of a scattering object by means of propagating light through the most ‘stable’ channel within a moving scattering medium is demonstrated, potentially advancing fields such as deep imaging in living biological tissue and optical communications through turbulent air and underwater.

Using a systems and synthetic biology approach to study the molecular determinants of conversion, Wang et al. find that proliferation history and TF levels drive cell fate in direct conversion to motor neurons.

Researchers at the University of Adelaide have performed the first imaging of embryos using cameras designed for quantum measurements.

The University’s Center of Light for Life academics investigated how to best use ultrasensitive technology, including the latest generation of cameras that can count individual packets of light energy at each pixel, for life sciences.

Center director Professor Kishan Dholakia said the sensitive detection of these packets of light energy, termed photons, is vitally important for capturing in their natural state—allowing researchers to illuminate with gentle doses of light.

Sulfate-reducing bacteria break down a large proportion of the organic carbon in the oxygen-free zones of Earth, and in the seabed in particular. Among these important microbes, the Desulfobacteraceae family of bacteria stands out because its members are able to break down a wide variety of compounds—including some that are poorly degradable—to their end product, carbon dioxide (CO2).

A team of researchers led by Dr. Lars Wöhlbrand and Prof. Dr. Ralf Rabus from the University of Oldenburg, Germany, has investigated the role of these microbes in detail and published the findings of their comprehensive study in the journal Science Advances.

The team reports that the bacteria are distributed across the globe and possess a complex metabolism that displays modular features. All the studied strains possess the same central metabolic architecture for harvesting energy, for example.