The new AI system, called o3, is expected to launch early next year.
Category: robotics/AI – Page 70
Certain cells in the brain create a nurturing environment, enhancing the health and resilience of their neighbors, while others promote stress and damage. Using spatial transcriptomics and AI, researchers at Stanford’s Knight Initiative for Brain Resilience discovered these interactions playing out across the lifespan—suggesting local cellular interactions may significantly influence brain aging and resilience.
A new study was published in Nature in an article titled, “Spatial transcriptomic clocks reveal cell proximity effects in brain aging.”
“What was exciting to us was finding that some cells have a pro-aging effect on neighboring cells while others appear to have a rejuvenating effect on their neighbors,” said Anne Brunet, the Michele and Timothy Barakett Endowed Professor in Stanford’s department of genetics and co-senior investigator of the new study.
Novel Physical Reservoir Computing Device Mimics Human Synaptic Behavior for Efficient Edge AI Processing by Tokyo University of Science
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Artificial intelligence (AI) is becoming increasingly useful for the prediction of emergency events such as heart attacks, natural disasters, and pipeline failures. This requires state-of-the-art technologies that can rapidly process data. In this regard, reservoir computing, specially designed for time-series data processing with low power consumption, is a promising option.
It can be implemented in various frameworks, among which physical reservoir computing (PRC) is the most popular. PRC with optoelectronic artificial synapses that mimic human synaptic elements are expected to have unparalleled recognition and real-time processing capabilities akin to the human visual system.
However, PRC based on existing self-powered optoelectronic synaptic devices cannot handle time-series data across multiple timescales, present in signals for monitoring infrastructure, natural environment, and health conditions.
In-plane magnetic fields are responsible for inducing anomalous Hall effect in EuCd2Sb2 films, report researchers from the Institute of Science Tokyo. By studying how these fields change electronic structures, the team discovered a large in-plane anomalous Hall effect.
These findings, published in Physical Review Letters on December 3, 2024, pave the way for new strategies for controlling electronic transport under magnetic fields, potentially advancing applications in magnetic sensors.
The Hall effect is a fundamental phenomenon in material science. It occurs when a material carrying an electric current is exposed to a magnetic field, producing a voltage perpendicular to both the current and the magnetic field. This effect has been extensively studied in materials under out-of-plane magnetic fields. However, research on how in-plane magnetic fields induce this phenomenon has been very limited.
Apptronik will combine its iterative design experience and Apollo humanoid in testing with Google DeepMind’s AI platforms.
“The challenge is applying agentic AI in the enterprise setting or in innovation-driven industries, like materials science R&D or pharma, where there is higher uncertainty and risk,” said Connell. “These more complex environments require a very nuanced understanding by the agent in order to make trustworthy, reliable decisions.”
Also: What is Google’s Project Mariner? This AI agent can navigate the web for you.
As with analytical and gen AI, data — particularly real-time data — is at the core of agentic AI success. It’s important “to have an understanding of how agentic AI will be used and the data that is powering the agent, as well as a system for testing,” said Connell. “To build AI agents, you need clean and, for some applications, labeled data that accurately represents the problem domain, along with sufficient volume to train and validate your models.”
Johns Hopkins computer scientists have created an artificial intelligence system capable of “imagining” its surroundings without having to physically explore them, bringing AI closer to humanlike reasoning.
The new system—called Generative World Explorer, or GenEx—needs only a single still image to conjure an entire world, giving it a significant advantage over previous systems that required a robot or agent to physically move through a scene to map the surrounding environment, which can be costly, unsafe, and time-consuming. The team’s results are posted to the arXiv preprint server.
“Say you’re in an area you’ve never been before—as a human, you use environmental cues, past experiences, and your knowledge of the world to imagine what might be around the corner,” says senior author Alan Yuille, the Bloomberg Distinguished Professor of Computational Cognitive Science at Johns Hopkins.
Genesis supports parallel simulation, making it ideal for training reinforcement learning (RL) locomotion policies efficiently. In this tutorial, we will walk you through a complete training example for obtaining a basic locomotion policy that enables a Unitree Go2 Robot to walk. With Genesis, you will be able to train a locomotion policy that’s deployable in real-world in less than 26 seconds (benchmarked on a RTX 4090).
Acknowledgement: This tutorial is inspired by and builds several core concepts from Legged Gym.
Your safety framework must include content filtering, output validation, rate limiting and detailed audit logging. I’ve found that implementing circuit breakers—automatic capability disablers triggered by anomalies—prevents small issues from becoming major incidents. For example, if an agent starts generating an unusual number of error responses, the system should automatically restrict its capabilities and alert the operations team.
Last year, I spoke to a tech company whose AI assistant became a victim of its own success. The system that flawlessly handled 1,000 daily requests crashed when usage jumped to 100,000 requests after a successful product launch. This taught us the importance of building for scale from day one. Even well-established companies like Netflix occasionally face challenges with scale, as seen during the recent live-streaming outages for the Jake Paul vs. Mike Tyson fight.
A production-ready architecture needs several key components working in harmony. The core engine should be modular, making updates and maintenance straightforward. Your integration layer should connect smoothly with enterprise systems through standardized APIs. Comprehensive monitoring helps you spot issues before they impact users, and robust memory management ensures consistent context handling across interactions.