Lifeboat Foundation

Cosmologists love universe simulations. Even models covering hundreds of millions of light years can be useful for understanding fundamental aspects of cosmology and the early universe. There’s just one problem – they’re extremely computationally intensive. A 500 million light year swath of the universe could take more than 3 weeks to simulate… Now, scientists led by Yin Li at the Flatiron Institute have developed a way to run these cosmically huge models 1000 times faster. That 500 million year light year swath could then be simulated in 36 minutes.

Older algorithms took such a long time in part because of a tradeoff. Existing models could either simulate a very detailed, very small slice of the cosmos or a vaguely detailed larger slice of it. They could provide either high resolution or a large area to study, not both.

To overcome this dichotomy, Dr. Li turned to an AI technique called a generative adversarial network (GAN). This algorithm pits two competing algorithms again each other, and then iterates on those algorithms with slight changes to them and judges whether those incremental changes improved the algorithm or not. Eventually, with enough iterations, both algorithms become much more accurate naturally on their own.

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Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster.

Many people with diabetes endure multiple, painful finger pricks each day to measure their blood glucose. Now, researchers reporting in ACS Sensors have developed a device that can measure glucose in sweat with the touch of a fingertip, and then a personalized algorithm provides an accurate estimate of blood glucose levels.

According to the American Diabetes Association, more than 34 million children and adults in the U.S. have diabetes. Although self-monitoring of blood glucose is a critical part of diabetes management, the pain and inconvenience caused by finger-stick blood sampling can keep people from testing as often as they should.

The researchers made a touch-based sweat glucose sensor with a polyvinyl alcohol hydrogel on top of an electrochemical sensor, which was screen-printed onto a flexible plastic strip. When a volunteer placed their fingertip on the sensor surface for 1 minute, the hydrogel absorbed tiny amounts of sweat. Inside the sensor, glucose in the sweat underwent an enzymatic reaction that resulted in a small electrical current that was detected by a hand-held device.

Researchers have discovered the most precise way to control individual ions using holographic optical engineering technology.

The new technology uses the first known holographic optical engineering device to control trapped ion qubits. This technology promises to help create more precise controls of qubits that will aid the development of quantum industry-specific hardware to further new quantum simulation experiments and potentially quantum error correction processes for trapped ion qubits.

“Our algorithm calculates the hologram’s profile and removes any aberrations from the light, which lets us develop a highly precise technique for programming ions,” says lead author Chung-You Shih, a Ph.D. student at the University of Waterloo’s Institute for Quantum Computing (IQC).

Paper references for Levine’s Phenotypic Age calculator and aging.ai:

An epigenetic biomarker of aging for lifespan and healthspan:
https://pubmed.ncbi.nlm.nih.gov/29676998/

Continue reading “Quantifying Biological Age: Blood Test #2 in 2021” »

Protocol to reverse engineer Hamiltonian models advances automation of quantum devices.

Scientists from the University of Bristol ’s Quantum Engineering Technology Labs (QETLabs) have developed an algorithm that provides valuable insights into the physics underlying quantum systems — paving the way for significant advances in quantum computation and sensing, and potentially turning a new page in scientific investigation.

In physics, systems of particles and their evolution are described by mathematical models, requiring the successful interplay of theoretical arguments and experimental verification. Even more complex is the description of systems of particles interacting with each other at the quantum mechanical level, which is often done using a Hamiltonian model. The process of formulating Hamiltonian models from observations is made even harder by the nature of quantum states, which collapse when attempts are made to inspect them.

Machine learning is capable of doing all sorts of things as long as you have the data to teach it how. That’s not always easy, and researchers are always looking for a way to add a bit of “common sense” to AI so you don’t have to show it 500 pictures of a cat before it gets it. Facebook’s newest research takes a big step toward reducing the data bottleneck.

The company’s formidable AI research division has been working for years now on how to advance and scale things like advanced computer vision algorithms, and has made steady progress, generally shared with the rest of the research community. One interesting development Facebook has pursued in particular is what’s called “semi-supervised learning.”

Generally when you think of training an AI, you think of something like the aforementioned 500 pictures of cats — images that have been selected and labeled (which can mean outlining the cat, putting a box around the cat or just saying there’s a cat in there somewhere) so that the machine learning system can put together an algorithm to automate the process of cat recognition. Naturally if you want to do dogs or horses, you need 500 dog pictures, 500 horse pictures, etc. — it scales linearly, which is a word you never want to see in tech.

Scientists from the University of Bristol’s Quantum Engineering Technology Labs (QETLabs) have developed an algorithm that provides valuable insights into the physics underlying quantum systems—paving the way for significant advances in quantum computation and sensing, and potentially turning a new page in scientific investigation.

Consciousness remains scientifically elusive because it constitutes layers upon layers of non-material emergence: Reverse-engineering our thinking should be done in terms of networks, modules, algorithms and second-order emergence — meta-algorithms, or groups of modules. Neuronal circuits correlate to “immaterial” cognitive modules, and these cognitive algorithms, when activated, produce meta-algorithmic conscious awareness and phenomenal experience, all in all at least two layers of emergence on top of “physical” neurons. Furthermore, consciousness represents certain transcendent aspects of projective ontology, according to the now widely accepted Holographic Principle.

#CyberneticTheoryofMind

Continue reading “The Science of Consciousness: Towards the Cybernetic Theory of Mind” »