Barbosa, Stein et al. show that rather than operating independently, PFC persistent activity and ‘activity-silent’ mechanisms interact dynamically to produce serial effects in working memory, consistent with attractor models with synaptic plasticity.
One major reason why it has been difficult to develop an effective HIV vaccine is that the virus mutates very rapidly, allowing it to evade the antibody response generated by vaccines.
Several years ago, MIT researchers showed that administering a series of escalating doses of an HIV vaccine over two weeks could help overcome a part of that challenge by generating larger quantities of neutralizing antibodies.
However, a fast multidose vaccine regimen is not practical for mass vaccination campaigns.
Posted in alien life, mathematics, physics, robotics/AI
This conversation between Max Tegmark and Joel Hellermark was recorded in April 2024 at Max Tegmark’s MIT office. An edited version was premiered at Sana AI Summit on May 15 2024 in Stockholm, Sweden.
Max Tegmark is a professor doing AI and physics research at MIT as part of the Institute for Artificial Intelligence \& Fundamental Interactions and the Center for Brains, Minds, and Machines. He is also the president of the Future of Life Institute and the author of the New York Times bestselling books Life 3.0 and Our Mathematical Universe. Max’s unorthodox ideas have earned him the nickname “Mad Max.”
Joel Hellermark is the founder and CEO of Sana. An enterprising child, Joel taught himself to code in C at age 13 and founded his first company, a video recommendation technology, at 16. In 2021, Joel topped the Forbes 30 Under 30. This year, Sana was recognized on the Forbes AI 50 as one of the startups developing the most promising business use cases of artificial intelligence.
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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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RECOMMENDED PODCAST: History 102
Every week, creator of WhatifAltHist Rudyard Lynch and Erik Torenberg cover a major topic in history in depth — in under an hour. This season will cover classical Greece, early America, the Vikings, medieval Islam, ancient China, the fall of the Roman Empire, and more. Subscribe on.
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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.
