Lifeboat Foundation

The universe is governed by four known fundamental forces: gravity, electromagnetism, the weak force, and the strong force. The strong force is responsible for dynamics on an extremely small scale, within and between the individual nucleons of atomic nuclei and between the constituents – quarks and gluons – that make up those nucleons. The strong force is described by a theory called Quantum Chromodynamics (QCD). One of the key details of this theory, known as “asymptotic freedom”, is responsible for both the subatomic scale of the strong force and the significant theoretical difficulties that the strong force has presented to physicists over the past 50 years.

Given the complexity of the strong force, experimental physicists have often led the research frontier and made discoveries that theorists are still trying to describe. This pattern is distinct from many other areas of physics, where experimentalists mostly search for and confirm, or exclude, theoretical predictions. One of the QCD areas where experimentalists have led progress is in the description of the collective behavior of systems with many bodies interacting via the strong force. An example of such a system is the quark-gluon plasma (QGP). A few microseconds after the Big Bang, the universe is supposed to have existed in such a state. The way the universe evolved in these brief moments and the structure that subsequently developed over billions of years is studied, in part, through experimental research on collective QCD effects. This briefing describes a recent exciting development in that research. To better understand the results, we begin with a series of analogies.

Imagine you are on a large university campus. You observe student movements in the middle of a busy exam period and find that the number of students entering the library in the morning is related to the number of students leaving in the evening. Perhaps this indicates some conserved quantity, like the number of students at the school. Each student in the library wants enough room to lay out their supplies and textbooks and get comfortable while studying. The library is nearly full and the students are evenly distributed across all the floors and halls of the library to ensure they have ample space. Recognizing and quantifying correlations like these can be useful for studying collective systems. By counting students “here” you can predict how many students are “there”, or by counting students “now” you can predict how many students you will get “later”. In this example, you may have insight into basic temporal and spatial correlations.

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.

See why new discoveries about water on Mars and water on the Moon are great news for the future of space settlement!

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Japan’s Moon Snipper Landed on the Moon making Japan the fifth nation to accomplish a lunar landing and Astrobiotic’s Peregrine lunar lander reenters Earth’s atmosphere.

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Eno, the career-spanning documentary about Brian Eno that premieres at the Sundance Film Festival, is a bracing dive into the brain of one of the most transformative musicians, producers and sound pioneers of the past half century.

‘Eno’ uses generative AI to create an always-changing, but compelling story about musician and producer Brian Eno.

A genome-wide association study in 293,723 individuals identifies 74 genetic variants associated with educational attainment, which, although only explaining a small proportion of the variation in educational attainment, highlights candidate genes and pathways for further study.

Its performance matches the smartest high school mathematicians and is much stronger than the previous state-of-the-art system.

Google DeepMind has created an AI system that can solve complex geometry problems.

(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.

In Neuromorphic Computing Part 2, we dive deeper into mapping neuromorphic concepts into chips built from silicon. With the state of modern neuroscience and chip design, the tools the industry is working with we’re working with are simply too different from biology. Mike Davies, Senior Principal Engineer and Director of Intel’s Neuromorphic Computing Lab, explains the process and challenge of creating a chip that can replicate some of the form and functions in biological neural networks.

Mike’s leadership in this specialized field allows him to share the latest insights from the promising future in neuromorphic computing here at Intel. Let’s explore nature’s circuit design of over a billion years of evolution and today’s CMOS semiconductor manufacturing technology supporting incredible computing efficiency, speed and intelligence.

Continue reading “Architecture All Access: Neuromorphic Computing Part 2” »