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While current treatments for ailments related to aging and diseases like type 2 diabetes, Alzheimer’s, and Parkinson’s focus on managing symptoms, Texas A&M researchers have taken a new approach to fight the battle at the source: recharging mitochondrial power through nanotechnology.
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When we need to recharge, we might take a vacation or relax at the spa. But what if we could recharge at the cellular level, fighting against aging and disease with the microscopic building blocks that make up the human body?
When we need to recharge, we might take a vacation or relax at the spa. But what if we could recharge at the cellular level, fighting against aging and disease with the microscopic building blocks that make up the human body?
The ability to recharge cells diminishes as humans age or face diseases. Mitochondria, often called the powerhouse of the cell, are central to energy production. When mitochondrial function declines, it leads to fatigue, tissue degeneration, and accelerated aging. Activities that once required minimal recovery now take far longer, highlighting the role that these organelles play in maintaining vitality and overall health.
While current treatments for ailments related to aging and diseases like type 2 diabetes, Alzheimer’s, and Parkinson’s focus on managing symptoms, Texas A&M researchers have taken a new approach to fight the battle at the source: recharging mitochondrial power through nanotechnology.
In this episode of The Cognitive Revolution, Nathan interviews Samo Burja, founder of Bismarck Analysis, on the strategic dynamics of artificial intelligence through a geopolitical lens. They discuss AI’s trajectory, the chip supply chain, US-China relations, and the challenges of AI safety and militarization. Samo brings both geopolitical expertise and technological sophistication to these critical topics, offering insights on balancing innovation, security, and international cooperation.
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In this special crossover episode of The Cognitive Revolution, Nathan Labenz joins Robert Wright of the Nonzero newsletter and podcast to explore pressing questions about AI development. They discuss the nature of understanding in large language models, multimodal AI systems, reasoning capabilities, and the potential for AI to accelerate scientific discovery. The conversation also covers AI interpretability, ethics, open-sourcing models, and the implications of US-China relations on AI development.
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The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given by biologically plausible spiking neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically realistic models of neurons to carry out the computation. Due to their functional similarity to the biological neural network, spiking neural networks can embrace the sparsity found in biology and are highly compatible with temporal code. Our contributions in this work are: (i) we give a comprehensive review of theories of biological neurons; (ii) we present various existing spike-based neuron models, which have been studied in neuroscience; (iii) we detail synapse models; (iv) we provide a review of artificial neural networks; (v) we provide detailed guidance on how to train spike-based neuron models; (vi) we revise available spike-based neuron frameworks that have been developed to support implementing spiking neural networks; (vii) finally, we cover existing spiking neural network applications in computer vision and robotics domains. The paper concludes with discussions of future perspectives.
Keywords: spiking neural networks, biological neural network, autonomous robot, robotics, computer vision, neuromorphic hardware, toolkits, survey, review.
Futurist Raymond Kurzweil predicts humans may soon live up to 1,000 years by merging biotechnology, AI, and nanobots.
Year 2020 Nanobots that take the plaque out of arteries is here. face_with_colon_three
Single-walled carbon nanotubes can restore phagocytotic properties of macrophages in artherosclerotic plaques to promote plaque clearance and combat artherosclerosis.
A star wiggling oddly around in space may be the signpost to one of the most sought-after objects in the galaxy.
Some 5,825 light-years from Earth, a red giant star has been spotted moving as though in a slow orbital dance with a binary companion. The problem? There’s absolutely no light coming from the place where the binary companion should be.
It gets more interesting. Based on the behavior of the red giant, astronomers led by Song Wang of the Chinese Academy of Sciences have determined that the mass of the invisible object is just 3.6 times the mass of the Sun. There’s only one thing it could be: a black hole, one with a petite size that’s smack bang in the middle of a mysterious void in the data known as the lower mass gap.
