Lifeboat Foundation

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.

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!

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.

John Conway’s Game of Life, a famous cellular automaton, has been found to have periodic patterns of every possible length.

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.

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.

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.

Consciousness is one of the most mysterious and fascinating aspects of human existence. It is also one of the most challenging to study scientifically, as it involves subjective experiences that are not directly observable or measurable. David Chalmers, a professor of philosophy and neural science at NYU mentions in his book The Conscious Mind.

“It may be the largest outstanding obstacle in our quest for a scientific understanding of the universe.”

The real questions are: how can we approach the problem of consciousness from a rigorous and objective perspective? Is there a way to quantify and model the phenomena of awareness, feelings, thoughts, and selfhood? There is no definitive answer to this question, but some researchers have attempted to use mathematical tools and methods to study these phenomena. Self-awareness, for instance, is the ability to perceive and understand the things that make you who you are as an individual, such as your personality, actions, values, beliefs, and even thoughts. Some studies have used the mirror test to assess the development of self-awareness in infants and animals.