I’d certainly like to see more experiments automated, yet I wonder if widespread automation would result in less resources directed to novel experimental designs (or new tools) that fall outside of automated workflows. Hopefully a balance can be attained!
AI-driven autonomous robots are coming to biology laboratories, but researchers insist that human skills remain essential.
Think of a video game that doesn’t just run on code, but is “dreamed up” in real-time by an AI—much like how AI generates videos or images today. While this technology (known as a Diffusion Game Engine) is incredibly exciting, it has long faced two major hurdles: you couldn’t easily “edit” the world once it was generated, and you couldn’t play in that world with friends because the AI couldn’t keep the environment consistent for everyone at once.
Traditional AI game engines work like “next-frame predictors.” They look at what’s happening right now and guess what the very next split-second should look like. Because they have a short memory (a “context window”), the world often feels like a shifting dream—turn around, and the door you just walked through might have disappeared or changed color. This makes it impossible to design a specific “level” or play with others, as the AI can’t keep a steady map in its head.
We are living in the age of maximum AI hype: A superintelligence that surpasses humanity is going to emerge at any moment, according to the most breathless corners of the tech world.
There are basic technical grounds to be skeptical of that claim, but beyond that, a much deeper issue lies at the boundary between science and philosophy: What makes life different from non-life? Why is a rock inert and insensate, while even the simplest cell manifests open-ended activity in the relentless pursuit of staying alive? Since the only systems that indisputably display intelligence are alive, if we can’t understand life, we’re probably missing something essential about intelligence.
Sixty years ago, an influential but little-known philosopher named Hans Jonas gave a potent, creative, and radical answer to this question of what makes life different from non-life. In the decades since, the power and reach of his perspective have gained traction. Today, for a growing group of researchers — in fields ranging from neuroscience to the physics of complex systems — Jonas has become an incisive voice arguing forcefully that organisms are more than just machines, and minds are more than just computers.
Despite overall improvements, women with STEMI in China still face longer pre-hospital delays than men, especially in rural areas. The gap is driven mostly by delayed EMS calls. Cardiology.
HealthEquity STEMI
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Humanoid robots, robotic systems with a human-like body structure, have the potential of tackling various real-world tasks that are currently being completed by humans. In recent years, many robotics researchers and computer scientists have been trying to broaden these robots’ capabilities and improve how they move in their surroundings.
A research team at Amazon Frontier AI & Robotics (FAR) and University of California Berkeley (UC Berkeley) recently introduced perceptive humanoid parkour (PHP), a framework that could allow humanoid robots to move with remarkable agility, running, jumping and climbing over obstacles in urban or natural environments. Their proposed approach, outlined in a paper published on the arXiv preprint server, entails training computational models on recordings of humans engaging in parkour, a popular urban sport that allows practitioners to rapidly navigate environments using their agility and body strength.
“While recent advances in humanoid locomotion have achieved stable walking on varied terrains, capturing the agility and adaptivity of highly dynamic human motions remains an open challenge,” wrote Zhen Wu, Xiaoyu Huang and their colleagues in their paper.
What if the thermal noise that hinders the efficiency of both classical and quantum computers could, instead, be used as a power source? What if computers could make use of the noise instead of suppressing or overcoming it? These are the goals of a relatively new branch of computing known as thermodynamic computing. A collaboration between researchers at the Molecular Foundry and the National Energy Research Scientific Computing Center (NERSC), both U.S. Department of Energy (DOE) user facilities located at Lawrence Berkeley National Laboratory (Berkeley Lab), is bringing them closer to reality.
In a paper published in Nature Communications, the researchers have proposed a design and training framework for a type of thermodynamic computer that mimics a neural network, which could drastically reduce the energy requirements of machine learning.
Modern computing requires energy: a single Google search, for example, consumes enough energy to power a six-watt LED for three minutes. This is partly because computers must contend with thermal noise—that is, the vibration of charge carriers, mostly electrons, within electronically conductive materials. In classical computers, even the smallest devices, such as transistors and gates, operate at energy scales thousands of times larger than that of this vibration.
A team of researchers at the University of California, Los Angeles (UCLA) has introduced a novel framework for monitoring structural vibrations using diffractive optical processors. This new technology uses artificial intelligence to co-optimize a passive diffractive layer and a shallow neural network, allowing the system to encode time-varying mechanical vibrations into distinct spatiotemporal optical patterns.
Structural Health Monitoring (SHM) systems are vital for assessing the condition of civil infrastructure, such as buildings and bridges, particularly after exposure to natural hazards like earthquakes. Traditional vibration-based methods rely on sensor networks of accelerometers and strain gauges, which demand significant power, generate large datasets requiring complex digital signal processing, and can be expensive to install and maintain.
Furthermore, achieving high spatial resolution for accurate damage localization often requires a costly, dense sensor deployment.