Scientists are combining AI machine learning with mechanical testing to improve plant-based meat.
By Adam Protz
Category: robotics/AI – Page 85
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Flying with Photons: Rendering Novel Views of Propagating Light https://arxiv.org/abs/2404.
Close your eyes and picture the iconic “bullet time” scene from “The Matrix”—the one where Neo, played by Keanu Reeves, dodges bullets in slow motion. Now imagine being able to witness the same effect, but instead of speeding bullets, you’re watching something that moves one million times faster: light itself.
Computer scientists from the University of Toronto have built an advanced camera setup that can visualize light in motion from any perspective, opening avenues for further inquiry into new types of 3D sensing techniques.
New research from the University of Kent has demonstrated that quantum information could eventually be used to coordinate the actions of devices that can move, such as drones or autonomous vehicles. This could lead to more efficient logistics, which could make deliveries cheaper, and better use of limited bandwidth for the likes of self-driving cars.
By carrying out “real world” experiments on a quantum computer, the team of quantum physicists (led by Ph.D. student Josh Tucker in the University of Kent’s School of Physics and Astronomy), found that if the two devices share a pair of quantum coins (qubits), the devices can continue to influence each other even after they have been separated and can no longer communicate.
The experiments simulated the phenomenon using real qubits inside a quantum computer developed by IBM. The qubits are made of superconducting material and kept at temperatures colder than the interstellar void. This allows them to behave according to the laws of quantum physics that defy common sense—including the ability to influence each other without coming into contact and without sending signals.
Amid heated debates about the potential pitfills of artificial intelligence, the technology has finally taken a form we can probably all get behind — an “AI granny” created expressly to waste scammers’ time.
British telecom company Virgin Media O2 on Thursday introduced Daisy, a custom-made human-like chabot that answers calls in real time, keeping fraudsters on the phone as long as possible in a bid to annoy and frustrate them, just as they do to consumers worldwide. Daisy (that’s “dAIsy”) automates the practice of “scambaiting,” which involves people posing as potential victims to squander scammers’ time and resources, publicly expose their wily ways, gather information useful to law enforcement and even confuse the con artists’ devices.
Daisy, newly dubbed O2’s “head of scammer relations,” impersonates an older adult, making her part of a demographic that’s particularly vulnerable to scams. Unlike human scambaiters who need to sleep and shower once in a while, Daisy can spend all day and night on the phone with swindlers. “While they’re busy talking to me they can’t be scamming you, and let’s face it, dear, I’ve got all the time in the world,” Daisy says in the introductory video from O2 embedded below. The video personifies her as a photorealistic AI-generated woman with gray hair, glasses and pearls talking on a pink landline.
TopoLM: brain-like spatio-functional organization in a topographic language model. Researchers have long observed that neurons in the brain tend to be
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Researchers have long observed that neurons in the brain tend to be organized in clusters, with neighboring neurons often sharing similar functions. This phenomenon is also seen in the brain’s language system, where certain areas respond to different aspects of language, such as syntax (sentence structure) or semantics (meaning). However, the exact mechanisms behind this organization remain a mystery.
In an attempt to better understand how the brain organizes language processing, the researchers developed TopoLM, a new type of AI language model inspired by the brain’s spatial layout. Unlike traditional language models, TopoLM arranges its processing units in a two-dimensional space, mimicking how neurons are arranged in the brain. It combines a standard language task (predicting the next word in a sentence) with an additional goal: encouraging units that are close together in space to also have similar functions, creating clusters of units that process similar linguistic information.
Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning.
Markus J Buehler.
An AI method developed by MIT Professor Markus Buehler finds hidden links between science and art to suggest novel materials.
New tool boosts robot gaits, enhancing speed and efficiency for quadrupeds.
This tool harnesses natural oscillations to improve robot movement, enabling a quadruped to achieve faster, efficient, and dynamic gait.
AI has officially entered the field of biology. Meet Evo, a GPT fed millions of microbes instead of words. Read all about it.
Evo marks the next frontier in machine learning by interpreting biological sequence data, including DNA, RNA, and proteins.
Physicist Matthias Kling studies photons and the things science can do with ultrafast pulses of X-rays. These pulses last just attoseconds – a billionth of a billionth of a second, Kling says. He uses them to create slo-mo “movies” of electrons moving through materials like those used in batteries and solar cells. The gained knowledge could reshape fields like materials science, ultrafast and quantum computers, AI, and medical diagnostics, Kling tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.