TODAY’s Sheinelle Jones sits down with Nakia Boykin, the great-granddaughter of legendary NASA mathematician Katherine Johnson. Boykin shares how Johnson inspired her academically and the lasting legacy she left behind for generations. “I don’t know if I’m going to work at NASA or anything like she did, but math definitely will always be with me as I get older,” she says.
Posted in education, existential risks, mathematics, robotics/AI
We — educators, scientists, psychologists — started an educational non-profit Earthlings Hub, to help out the kids, affected by the war. We talk to them about STEM, but also about the complexity of the world, philosophy of science, future, and existential risks. We also offer psychological help to their parents. Our advisory board includes NASA astronaut Greg Chamitoff, lead AI researcher Joscha Bach, Professor of Learning and Cognition, author of Netlogo language Uri Wilensky, lead early math educator Maria Droujkova and others. Please share, participate, donate! https://www.earthlingshub.org/
The algorithm estimates how weights will need to be altered on the forward pass, and the estimates perform comparably to backpropagation.
Humans are usually pretty good at recognizing when they get things wrong, but artificial intelligence systems are not. According to a new study, AI generally suffers from inherent limitations due to a century-old mathematical paradox.
Like some people, AI systems often have a degree of confidence that far exceeds their actual abilities. And like an overconfident person, many AI systems don’t know when they’re making mistakes. Sometimes it’s even more difficult for an AI system to realize when it’s making a mistake than to produce a correct result.
Researchers from the University of Cambridge and the University of Oslo say that instability is the Achilles’ heel of modern AI and that a mathematical paradox shows AI’s limitations. Neural networks, the state of the art tool in AI, roughly mimic the links between neurons in the brain. The researchers show that there are problems where stable and accurate neural networks exist, yet no algorithm can produce such a network. Only in specific cases can algorithms compute stable and accurate neural networks.
How can Einstein’s theory of gravity be unified with quantum mechanics? This is a challenge that could give us deep insights into phenomena such as black holes and the birth of the universe. Now, a new article in Nature Communications, written by researchers from Chalmers University of Technology, Sweden, and MIT, USA, presents results that cast new light on important challenges in understanding quantum gravity. Credit: Chalmers University of Technology / Yen Strandqvist.
How can Einstein’s theory of gravity be unified with quantum mechanics? It is a challenge that could give us deep insights into phenomena such as black holes and the birth of the universe. Now, a new article in Nature Communications, written by researchers from Chalmers University of Technology 0, Sweden, and MIT 0, USA, presents results that cast new light on important challenges in understanding quantum gravity.
A grand challenge in modern theoretical physics is to find a ‘unified theory’ that can describe all the laws of nature within a single framework – connecting Einstein’s general theory of relativity, which describes the universe on a large scale, and quantum mechanics, which describes our world at the atomic level. Such a theory of ‘quantum gravity’ would include both a macroscopic and microscopic description of nature.
Dude, what if everything around us was just … a hologram?
The thing is, it could be—and a University of Michigan physicist is using quantum computing and machine learning to better understand the idea, called holographic duality.
Holographic duality is a mathematical conjecture that connects theories of particles and their interactions with the theory of gravity. This conjecture suggests that the theory of gravity and the theory of particles are mathematically equivalent: what happens mathematically in the theory of gravity happens in the theory of particles, and vice versa.
This video covers the world in 2,300 and its future technologies. Watch this next video about the world in 2200: https://bit.ly/3htaWEr.
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• The Future of Humanity (Michio Kaku): https://amzn.to/3Gz8ffA
• The Singularity Is Near: When Humans Transcend Biology (Ray Kurzweil): https://amzn.to/3ftOhXI
• Physics of the Future (Michio Kaku): https://amzn.to/33NP7f7
• https://science.howstuffworks.com/science-vs-myth/everyday-m…tation.htm.
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How can Einstein’s theory of gravity be unified with quantum mechanics? It is a challenge that could give us deep insights into phenomena such as black holes and the birth of the universe. Now, a new article in Nature Communications, written by researchers from Chalmers University of Technology, Sweden, and MIT, U.S., presents results that cast new light on important challenges in understanding quantum gravity.
A grand challenge in modern theoretical physics is to find a “unified theory” that can describe all the laws of nature within a single framework—connecting Einstein’s general theory of relativity, which describes the universe on a large scale, and quantum mechanics, which describes our world at the atomic level. Such a theory of “quantum gravity” would include both a macroscopic and microscopic description of nature.
“We strive to understand the laws of nature and the language in which these are written is mathematics. When we seek answers to questions in physics, we are often led to new discoveries in mathematics too. This interaction is particularly prominent in the search for quantum gravity—where it is extremely difficult to perform experiments,” explains Daniel Persson, Professor at the Department of Mathematical Sciences at Chalmers university of technology.
Over the past decade or so, many researchers worldwide have been trying to develop brain-inspired computer systems, also known as neuromorphic computing tools. The majority of these systems are currently used to run deep learning algorithms and other artificial intelligence (AI) tools.
Researchers at Sandia National Laboratories have recently conducted a study assessing the potential of neuromorphic architectures to perform a different type of computations, namely random walk computations. These are computations that involve a succession of random steps in the mathematical space. The team’s findings, published in Nature Electronics, suggest that neuromorphic architectures could be well-suited for implementing these computations and could thus reach beyond machine learning applications.
“Most past studies related to neuromorphic computing focused on cognitive applications, such as deep learning,” James Bradley Aimone, one of the researchers who carried out the study, told TechXplore. “While we are also excited about that direction, we wanted to ask a different and complementary question: can neuromorphic computing excel at complex math tasks that our brains cannot really tackle?”
