Lifeboat Foundation

An asteroid discovery algorithm—designed to uncover near-Earth asteroids for the Vera C. Rubin Observatory’s upcoming 10-year survey of the night sky—has identified its first “potentially hazardous” asteroid, a term for space rocks in Earth’s vicinity that scientists like to keep an eye on.

The roughly 600-foot-long asteroid, designated 2022 SF289, was discovered during a test drive of the algorithm with the ATLAS survey in Hawaii. Finding 2022 SF289, which poses no risk to Earth for the foreseeable future, confirms that the next-generation algorithm, known as HelioLinc3D, can identify near-Earth asteroids with fewer and more dispersed observations than required by today’s methods.

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Researchers from the Tokyo University of Science recently published a study in the journal Artificial Life and Robotics where they explored how machine learning can help detect deception.

Machine learning is a subset of artificial intelligence (AI) that involves the use of algorithms and statistical models to enable computers to learn and improve from experience without being explicitly programmed. In other words, it is a method of teaching computers to perform specific tasks by learning from data, patterns, and examples, rather than relying on pre-defined rules.

Detecting deception can be important in various situations, like questioning crime victims or suspects and interviewing patients with mental health issues. Sometimes, human interviewers might struggle to ask the right questions or spot deception accurately.

Here’s a better use for AI than warfare, which coming from a military family I see as a sad but necessary thing seeing as how Russia likes to invade people lately, but I hope we can keep peace with China, but anyway I’ve always loved animals. They called me Dr Dolittle as a child because I played with animals a lot. I hope for world peace. Perhaps AI can help diplomats communicate better as well. I know, you’d think we’d be able to but it doesn’t seem to be the case.

Scientists are harnessing the power of artificial intelligence (AI) to decode animal languages.

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Dismantling the belief in a static universe, Edwin Hubble’s revolutionary observations in the 1920s laid the groundwork for our understanding of a continually expanding cosmos. However, we must seek to reconcile this theory with observations that are consistent with a non-expanding universe, writes Tim Anderson.

You have been taught that the universe began with a Big Bang, a hot, dense period about 13.8 billion years ago. And the reason we believe this to be true is because the universe is expanding and, therefore, was smaller in the past. The Cosmic Microwave Background is the smoking gun for the Big Bang, the result of a reionization of matter that made the universe transparent about 300–400,000 years after the Big Bang.

How did we go from Einstein modifying his equations to keep the universe static and eternal, which he called the biggest blunder of his life, to every scientist believing that the universe had a beginning in 10 years? It all started with astronomer Edwin Hubble using the most powerful telescope at the time on Mount Wilson in California. At the time, in the 1920s, scientists believed that the Milky Way galaxy was the totality of the universe. Objects in the night sky like Andromeda that we now know are galaxies were called “nebulae”.

All navigations reported in Fig. 2 were performed autonomously within 150 s and without intraoperative imaging. Specifically, each navigation was performed according to the pre-determined optimal actuation fields and supervised in real time by intraoperative localization. Therefore, the set of complex navigations performed by the magnetic tentacle was possible without the need for exposure to radiation-based imaging. In all cases, the soft magnetic tentacle is shown to conform by design to the anatomy thanks to its low stiffness, optimal magnetization profile and full-shape control. Compared to a stiff catheter, the non-disruptive navigation achieved by the magnetic tentacle can improve the reliability of registration with pre-operative imaging to enhance both navigation and targeting. Moreover, compared to using multiple catheters with different pre-bent tips, the optimization approach used for the magnetic tentacle design determines a single magnetization profile specific to the patient’s anatomy that can navigate the full range of possible pathways illustrated in Fig. 2. Supplementary Movies S1 and S2 report all the experiments. Supplementary Movie S1 shows the online tracking capabilities of the proposed platform.

In Table 1, we report the results of the localization for four different scenarios. These cases highlight diverse navigations in the left and right bronchi. The error is referred to as the percentage of tentacles outside the anatomy. This was computed by intersecting the shape of the catheter, as predicted by the FBG sensor, and the anatomical mesh grid extracted from the CT scan. The portion of the tentacle within the anatomy was measured by using “inpolyhedron” function in MATLAB. In Supplementary Movie S1, this is highlighted in blue, while the section of the tentacle outside the anatomy is marked in red. The error in Table 1 was computed using the equation.

Throughout history, humans have gazed at the sky, contemplating the celestial lights, including the sun, the moon, and beyond. In those ancient moments, an insatiable curiosity ignited within them, urging them to seek answers about the origins of the cosmos. Over time, this burning curiosity has been passed down, compelling generations to develop theories in pursuit of one timeless question: Where did it all come from?

One of the most complete and widely accepted theories in this regard is the Big Bang Theory. The Big Bang is a scientific theory that proposes that the birth and development of the universe originated from a point in space-time called the singularity. Think of this in a way that all the matter and energy of the universe were trapped in an inconceivably small point of high density and high temperature (Williams & Today, n.d.). It is theorized to be a colossal release of energy that initiated the rapid expansion of the universe over 13.7 billion years that led to the creation of galaxies, stars, planetary systems and eventually humankind. What happened that led to the sudden expansion? This question continues to puzzle cosmologists, as the answer remains unknown to this day (What Is the Big Bang Theory? n.d.).

In 1915, while developing his General Theory of Relativity, Albert Einstein faced a challenge. If gravity were to solely attract all objects, the universe would ultimately collapse under its overwhelming force. However, observations indicated that the universe was not collapsing. To address this issue, Einstein introduced a cosmological constant into his equations. This constant acted as a counterforce to gravity and proposed a static model of the universe. Little did Einstein know that an astronomer named Edwin Hubble would soon contradict his proposed static model of the universe. Working at Mount Wilson Observatory in California, Hubble made a noteworthy observation in the late 1920s. He noticed a peculiar phenomenon known as redshift, where light emitted by celestial bodies moved toward the red end of the spectrum, indicating that they were moving away from us (Vogel, 2021).

Researchers at Leipzig University have developed a highly efficient method to investigate systems with long-range interactions that were previously puzzling to experts. These systems can be gases or even solid materials such as magnets whose atoms interact not only with their neighbors but also far beyond.

Professor Wolfhard Janke and his team of researchers use Monte Carlo computer simulations for this purpose. This stochastic process, named after the Monte Carlo casino, generates random system states from which the desired properties of the system can be determined. In this way, Monte Carlo simulations provide deep insights into the physics of phase transitions.

The researchers have developed a new algorithm that can perform these simulations in a matter of days, which would have taken centuries using conventional methods. They have published their new findings in the journal Physical Review X.

We compare the performance of the Quantum Approximate Optimization Algorithm (QAOA) with state-of-the-art classical solvers Gurobi and MQLib to solve the MaxCut problem on 3-regular graphs. We identify the minimum noiseless sampling frequency and depth p required for a quantum device to outperform classical algorithms. There is potential for quantum advantage on hundreds of qubits and moderate depth with a sampling frequency of 10 kHz. We observe, however, that classical heuristic solvers are capable of producing high-quality approximate solutions in linear time complexity. In order to match this quality for large graph sizes N, a quantum device must support depth p > 11. Additionally, multi-shot QAOA is not efficient on large graphs, indicating that QAOA p ≤ 11 does not scale with N. These results limit achieving quantum advantage for QAOA MaxCut on 3-regular graphs.

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Mentioned Videos:
AI designing Computer Chips: https://youtu.be/NeHgMaIkPuY
Deepmind AI made a Breakthrough in Math: https://youtu.be/DU6WINoehrg.

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