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Category: mathematics – Page 66

DARPA’s 2nd Tools Competition Focuses on AI Tools for Adult STEM, Data Science Learning

The Defense Advanced Research Projects Agency launched a second iteration of its Tools Competition to discover artificial intelligence-enabled technologies that can aid data science and other forms of adult learning.

The agency said Monday that the new program aims to upskill and reskill adults in science, technology, engineering and mathematics and similarly complex areas, preparing them for the 21st century labor landscape.

The opportunity is open to digital learning platform experts, technologists, researchers, students and educators who can propose AI tools that can provide feature tutoring and self-directed learning. The resulting platform may leverage AI or large language models.

Brain-based computing chips not just for AI anymore

With the insertion of a little math, Sandia National Laboratories researchers have shown that neuromorphic computers, which synthetically replicate the brain’s logic, can solve more complex problems than those posed by artificial intelligence and may even earn a place in high-performance computing.

The findings, detailed in a recent article in the journal Nature Electronics, show that neuromorphic simulations employing the statistical method called random walks can track X-rays passing through bone and soft tissue, disease passing through a population, information flowing through social networks and the movements of financial markets, among other uses, said Sandia theoretical neuroscientist and lead researcher James Bradley Aimone.

“Basically, we have shown that neuromorphic hardware can yield computational advantages relevant to many applications, not just artificial intelligence to which it’s obviously kin,” said Aimone. “Newly discovered applications range from radiation transport and molecular simulations to computational finance, biology modeling and particle physics.”

Google makes breakthrough in one of the hardest tests for AI

Google Deepmind says that a new artificial intelligence system has made a major breakthrough in one of the most difficult tests for AI.

The company says that it has created a new AI system that can solve geometry problems at the level of the very top high-school students.

Geometry is one of the oldest branches of mathematics, but has proven particularly difficult for AI systems to work with. It has been difficult to train them because of a lack of data, and succeeding requires building a system that can take on difficult logical challenges.

The Invisible Dance Of Particles

In 1,827, botanist Robert Brown studied pollen particles’ motion as they were suspended in water. These little grains seemed to jitter around randomly. Brown performed as variety of tests on them and realized that all small particles, not just pollen, exhibited the same motion when suspended in water. Something other than the presence of life was causing these little particles to move around. Mathematicians took note and quickly developed a theory describing this process and named it Brownian Motion in his honor.

This theory has expanded well beyond its original context and become a beautiful subfield of mathematics called Stochastic Processes. Nowhere was this influence illustrated better than in 1905 when Albert Einstein used the theory of Brownian Motion to verify the existence of atoms. The makeup of our universe’s tiniest particles was highly debated at the time, and Einstein’s work helped solidify atomic theory.

Wow, that’s quite the leap! In order to understand how we got from pollen grains to confirming atomic theory, we’re going to have to learn some background about Brownian Motion. In this article, I’ll spend some time talking about the basics. This includes some cool videos that demonstrate the patterns of Brownian Motion and the statistics going on behind the scenes. We’ll then dive into Einstein’s version which came as one of his extremely influential series of papers in 1905. There’s a lot of ground to cover, so let’s get started!

Ultimate Computing: Biomolecular Consciousness and NanoTechnology

The possibility of direct interfacing between biological and technological information devices could result in a merger of mind and machine — Ultimate Computing. This book, a thorough consideration of this idea, involves a number of disciplines, including biochemistry, cognitive science, computer science, engineering, mathematics, microbiology, molecular biology, pharmacology, philosophy, physics, physiology, and psychology.

Unveiling Evolution’s Secrets: Scientists Discover Mathematical Connection Between Chickens, Frogs, and Fish

One of the fundamental and timeless questions of life concerns the mechanics of its inception. Take human development, for example: how do individual cells come together to form complex structures like skin, muscles, bones, or even a brain, a finger, or a spine?

Although the answers to such questions remain unknown, one line of scientific inquiry lies in understanding gastrulation — the stage at which embryo cells develop from a single layer to a multidimensional structure with a main body axis. In humans, gastrulation happens around 14 days after conception.

It’s not possible to study human embryos at this stage, so researchers at the University of California San Diego, the University of Dundee (UK), and Harvard University were able to study gastrulation in chick embryos, which have many similarities to human embryos at this stage.

We’ve Been Misreading a Major Law of Physics For The Last 300 Years

When Isaac Newton inscribed onto parchment his now-famed laws of motion in 1,687, he could have only hoped we’d be discussing them three centuries later.

Writing in Latin, Newton outlined three universal principles describing how the motion of objects is governed in our Universe, which have been translated, transcribed, discussed and debated at length.

But according to a philosopher of language and mathematics, we might have been interpreting Newton’s precise wording of his first law of motion slightly wrong all along.

DeepMind’s Latest AI System, AlphaGeometry, Aces High-School Math

(Bloomberg) — Google DeepMind, Alphabet Inc.’s research division, said it has taken a “crucial step” towards making artificial intelligence as capable as humans. It involves solving high-school math problems. Most Read from BloombergWall Street Dials Back Fed Wagers After Solid Data: Markets WrapMusk Pressures Tesla’s Board for Another Massive Stock AwardChina’s Economic Growth Disappoints, Fueling Stimulus CallsChina Population Extends Record Drop on Covid Deaths, Low BirthsApple to Allow Outsi.

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