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Category: mathematics – Page 121

In some ways, learning to program a computer is similar to learning a new language. It requires learning new symbols and terms, which must be organized correctly to instruct the computer what to do. The computer code must also be clear enough that other programmers can read and understand it.

In spite of those similarities, MIT neuroscientists have found that reading computer code does not activate the regions of the brain that are involved in language processing.

Instead, it activates a distributed network called the multiple demand network, which is also recruited for complex cognitive tasks such as solving math problems or crossword puzzles.

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This week, I had some amazing discussions with Navajo Nation Math Circle leaders — Dave Auckly and Henry Fowler. The idea of starting a math circle on Navajo land was initially brought up by a wonderful math educator and mathematician raised in Kazakhstan, Tatiana Shubin. Here is a small tribute to their efforts:

Project activities were launched in the Fall of 2012. A team of distinguished mathematicians from all over the US, as well as local teachers and community members, work together to run the outreach. Navajo Nation Math Circles present math in the context of Navajo culture, helping students develop their identity as true Navajo mathematicians. “We want to find kids who would not have discovered their talents without our project, to help them realize that they can change the world,” says Fowler. Having introduced Navajo children to the joy of mathematics, the project also yielded a book, Inspiring Mathematics: Lessons from the Navajo Nation Math Circles, which contain lesson plans, puzzles and activities, and other insights for parents and teachers to embrace.

An extension of Navajo Nation Math Circles is an annual two-week Baa Hózhó summer math camp at Navajo Technical University. “Baa Hózhó” means “balance and harmony,” tying together the ideas of mathematical equilibrium with the way of life embraced by Navajo people. The summer camp is widely popular with parents and children; the older students come back as counselors, making everyone feel like one big family. It is preceded by an annual student-run math festival in local schools across the Navajo Nation, where students share their passion for mathematics with families and friends.

Fowler’s ultimate goal is to create a Mathematical Research institute on Navajo land, where local and international researchers could exchange math ideas and study the best ways of teaching mathematics to Indigenous people, enriching worldwide mathematical sciences. Hopefully, the great strides in the Navajo Nation math education will encourage leading high-tech companies to support the rise of a new generation of diverse, talented and passionate Native American STEM professionals.

Continue reading “How The Navajo Nation Is Transforming Math Education” | >

(Checks math.)

Scientists have new evidence that Earth’s many periodic mass extinctions follow a cycle of about 27 million years, connecting the five major mass extinctions with more minor ones occurring throughout Earth’s life-fostering timespan. The artificial intelligence analysis could also shift how evolutionary scientists think about the aftermath of mass extinctions.

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Neuroscientists find that interpreting code activates a general-purpose brain network, but not language-processing centers.

In some ways, learning to program a computer is similar to learning a new language. It requires learning new symbols and terms, which must be organized correctly to instruct the computer what to do. The computer code must also be clear enough that other programmers can read and understand it.

In spite of those similarities, MIT neuroscientists have found that reading computer code does not activate the regions of the brain that are involved in language processing. Instead, it activates a distributed network called the multiple demand network, which is also recruited for complex cognitive tasks such as solving math problems or crossword puzzles.

Read more | >

“People want to know what makes someone a good programmer,” Liu said. “If we know what kind of neuro mechanisms are activated when someone is programming, we might be able to find a better training program for programmers.” By mapping the brain activity of expert computer programmers while they puzzled over code, Johns Hopkins University scientists have found the neural mechanics behind this increasingly vital skill.

Though researchers have long suspected the for computer programming would be similar to that for math or even language, this study revealed that when seasoned coders work, most happens in the network responsible for logical reasoning, though in the left brain region, which is favored by language.

“Because there are so many ways people learn programming, everything from do-it-yourself tutorials to formal courses, it’s surprising that we find such a consistent brain activation pattern across people who code,” said lead author Yun-Fei Liu, a Ph.D. student in the university’s Neuroplasticity and Development Lab. “It’s especially surprising because we know there seems to be a crucial period that usually terminates in for , but many people learn to code as adults.”

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Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used functional magnetic resonance imaging to investigate two candidate brain systems: the multiple demand (MD) system, typically recruited during math, logic, problem solving, and executive tasks, and the language system, typically recruited during linguistic processing. We examined MD and language system responses to code written in Python, a text-based programming language (Experiment 1) and in ScratchJr, a graphical programming language (Experiment 2); for both, we contrasted responses to code problems with responses to content-matched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments, whereas the language system responded strongly to sentence problems, but weakly or not at all to code problems. Thus, the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.

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Fast spinning black holes could have features different from those predicted by general relativity.

General relativity is a profoundly complex mathematical theory, but its description of black holes is amazingly simple. A stable black hole can be described by just three properties: its mass, its electric charge, and its rotation or spin. Since black holes aren’t likely to have much charge, it really takes just two properties. If you know a black hole’s mass and spin, you know all there is to know about the black hole.

This property is often summarized by the no-hair theorem. Specifically, the theorem asserts that once matter falls into a black hole, the only characteristic that remains is mass. You could make a black hole out of a Sun’s worth of hydrogen, chairs, or those old copies of National Geographic from Grandma’s attic, and there would be no difference. Mass is mass as far as general relativity is concerned. In every case the event horizon of a black hole is perfectly smooth, with no extra features. As Jacob Bekenstein said, black holes have no hair.

But with all its predictive power, general relativity has a problem with quantum theory. This is particularly true with black holes. If the no-hair theorem is correct, the information held within an object is destroyed when it crosses the event horizon. Quantum theory says that information can never be destroyed. So the valid theory of gravity is contradicted by the valid theory of the quanta. This leads to problems such as the firewall paradox, which can’t decide whether an event horizon should be hot or cold.

Continue reading “Black Holes Gain new Powers When They Spin Fast Enough” | >

Researchers have found a way to protect highly fragile quantum systems from noise, which could aid in the design and development of new quantum devices, such as ultra-powerful quantum computers.

The researchers, from the University of Cambridge, have shown that microscopic particles can remain intrinsically linked, or entangled, over long distances even if there are random disruptions between them. Using the mathematics of quantum theory, they discovered a simple setup where entangled particles can be prepared and stabilized even in the presence of noise by taking advantage of a previously unknown symmetry in .

Their results, reported in the journal Physical Review Letters, open a new window into the mysterious quantum world that could revolutionize future technology by preserving in , which is the single biggest hurdle for developing such technology. Harnessing this capability will be at the heart of ultrafast quantum computers.

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I have the honor of being a guest on the USTP Enlightenment Salon today, many thanks to Gennady and David for the invitation.
I was a Linux sys/net admin.
I was never interested in politics until it became IMPOSSIBLE to avoid. Every action or inaction is now a political statement in some people’s minds. That’s a terrible state of affairs that has been imposed on us. So I put my hacker hat on and went to work to discover why there exists an abject division on truth and morals and how politics became the catalyst for the phenomenon.
I’ll be discussing the roots of my theory: Physix, a mathematical model for thought and behavior. The political derivative is the Q-vote. It’s a novel approach to democracy.
Nell Watson (https://www.nellwatson.com/) will be using a derivative of Physix for machine learning and ethics on https://www.ethicsnet.org/, but I think the most interesting quality of Physix is it’s commercial value. It codifies the decision process: Q-Logic.
Every action or thought can be assigned one of 525 unique patterns on this 5×5 grid. 13,125 if you add voice. Economics, psychology, philosophy, religion, politics and every conceivable imaginary or spacetime event fits. Psychohistory. The matrix has been hacked.

Physix gives AI a finite vocabulary to analyze the infinite chaos of life and imagination. The patterns can be compared to both physical and psychological results, solve for the most preferred.
It’s odd to me that Youtube HASN’T thought of color ratings to highlight videos, Zoom could integrate it with their platform to rate conversations and for meetings. It could be used with any human interaction to rate quality of communication.
The key to real world solutions is that I present this as Open Source Human Nature. Free. Where there is commercial value derived, 10% of the profit/efficiency gained will go to a fund where the money will be spent 100% publicly and tracked (using the same polling system) over time to find the most efficient way to make people happy. I’m looking forward to working with the USTP on the political side, on the AI side Nell Watson is just dipping her toe in the water, I’m looking for a capable AI group to integrate the idea. David Kelley from what I can tell has done all the deep research, my idea is just a different tool to bring it together. I have a programmer, an investor waiting to hear from someone in the field to say they are interested in tackling this.
From all the interactions I’ve had, the USTP is the most progressive, rationally minded group I’ve found. I believe the people involved with this Party would be the best to understand the implications and help me navigate the shark infested waters of politics, NGO’s, Big Tech and Academia.
It’s a new world, AI has a new tool to analyze us and become an ally, this renders the current political paradigm an ancient, sclerotic remnant of brute force mass persuasion for power and money.
It’s time for a paradigm shift of consciousness, aided by AI. The USTP is uniquely suited to bring this to the political forefront.
USTP: Let’s go.

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Coherence times in quantum computing have increased by orders of magnitude since the early 2000s. If this exponential progress continues, coherence times measured in seconds or even minutes could be achieved in the near future.

When discussing the latest quantum computers, most people tend to focus on the number of quantum bits (or qubits) in a system. However, while qubit counts are a very important factor, another key metric is coherence time, which measures how long a qubit can hold information.

In order to generate complex mathematical calculations, a qubit needs to hold information for as long as possible. That requires physical qubits to remain highly isolated from the surrounding environment. When a qubit is disrupted by external stimuli – such as background noise from vibrations, temperature changes or stray electromagnetic fields – information about the state of that qubit “leaks out” in a process known as decoherence. This can ruin the ability to exploit any quantum effects. Longer coherence times enable more quantum gates to be utilised before this occurs, resulting in more complex calculations.

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