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In order for robots to effectively partake in search and rescue operations, they need to effectively navigate obstacles in their way. One area that is particularly common and difficult to venture into is vegetation.

Robots typically use a combination of sensors to perceive their surroundings such as ultrasonic sensors, Lidar (Light Detection and Ranging), infrared sensors and camera systems. However, these are not often enough to allow robots to actually bypass the vegetation so commonly found in real outdoor environments.

That’s why engineers at Carnegie Mellon University are working on solving this particular dilemma.

These systems are based on nuclear fusion.

How do we get beyond our solar system? Current technologies simply can’t support this type of travel. However, speaking to Universe Today.

Shigemi Numazawa/ Project Daedalus.

Continue reading “How magnetic fusion plasma engines can take us to outer space” »

Instead of looking at individual neurons, they look at combinations of neurons that form patterns or features.

Artificial neural networks are like digital versions of our brains. They learn from data, not rules, and they can perform extraordinary tasks, from translating languages to playing chess. But how do they do it? What is the logic behind their calculations? And how can we trust them to be safe and reliable?

XH4D/iStock.

Continue reading “What makes AI brains tick? A study shows how to find out” »

The nation is looking into emissions-free travel.

According to a report by Reuters.

“It will be on trial for next week, hopefully for the next few months. We will have the first hydrogen train in the Middle East,” the government official said during the UN MENA Climate Week in Riyadh.

“Using the new quantum ruler to study how the circular orbits vary with magnetic field, we hope to reveal the subtle magnetic properties of these moiré quantum materials”

Graphene, a single-atom-thick sheet of carbon, is renowned for its exceptional electrical conductivity and mechanical strength.

However, when two or more layers of graphene are stacked with a slight misalignment, they become moiré quantum matter, opening the door to a world of exotic possibilities. Depending on the angle of twist, these materials can generate magnetic fields, become superconductors with zero electrical resistance, or transform into perfect insulators.

The study explains how variation in male traits and female preferences is maintained and evolved over time.

What makes a male animal irresistible to a female? Is it his looks, smell, skills, or genes? Scientists have been trying to answer this question for a long time. However, they have not been able to explain why some males are more attractive than others or why female preferences change over time and across species.

STidwell/iStock.

Continue reading “Study says female animals teach each other to choose male partners” »

Have you recently been thinking about the Roman Empire? According to a viral social media trend, the answer is decidedly yes, assuming that you are a man. The backstory is that an online video postulated that men daily tend to think about the Roman Empire and a follow-up by women asking their male friends, partners, or relatives began to flood the Internet. Seemingly, most men insisted that they did indeed have frequent thoughts about the Roman Empire. A hashtag associated with the Roman Empire has ballooned to incurring over a billion hits.

Before I get into some further details on the contentious hubbub, a question that immediately struck me and has now been rattling around in the AI… More.

A viral trend online is that men are supposedly thinking daily about the Roman Empire. If so, this begs the question of whether generative AI might be doing likewise.

Continue reading “The Question Is That If Men Are Daily Thinking About The Roman Empire Is Generative AI Doing Likewise” »

Understanding causality can’t come from passive observation, because the relevant counterfactuals often do not arise. If X is followed by Y, no matter how regularly, the only way to really know that is a causal relation is to intervene in the system: to prevent X and see if Y still happens. The hypothesis has to be tested. Causal knowledge thus comes from causal intervention in the world. What we see as intelligent behavior is the payoff for that hard work.

The implication is that artificial general intelligence will not arise in systems that only passively receive data. They need to be able to act back on the world and see how those data change in response. Such systems may thus have to be embodied in some way: either in physical robotics or in software entities that can act in simulated environments.

Artificial general intelligence may have to be earned through the exercise of agency.

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