All 3D models created with Meshy AI
https://www.meshy.ai/?utm_source=youtube&utm_medium=fimcrux.
The sublime is an emotion described as equal parts awe and terror; a perfect description of our universe.
We created this short film concept showcasing a future vision of how humans might continue exploring that universe.
We used Meshy AI to generate all the 3D models in this film, like the ships, space probes and asteroids.
All the other VFX were created by us without the use of AI.
Figuring out certain aspects of a material’s electron structure can take a lot out of a computer—up to a million CPU hours, in fact. A team of Yale researchers, though, are using a type of artificial intelligence to make these calculations much faster and more accurately. Among other benefits, this makes it much easier to discover new materials. Their results are published in Nature Communications.
In the field of materials science, exploring the electronic structure of real materials is of particular interest, since it allows for better understanding of the physics of larger and more complex systems, such as moiré systems and defect states. Researchers typically will use a method known as density functional theory (DFT) to explore electronic structure, and for the most part it works fine.
“But the issue is that if you’re looking at excited state properties, like how materials behave when they interact with light or when they conduct electricity, then DFT really isn’t sufficient to understand the properties of the material,” said Prof. Diana Qiu, who led the study.
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.
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.”