Lifeboat Foundation

They then used QUARTZ to analyze retinal images from 7,411 more people, these aged 48 to 92, and combined this data with information about their health history (such as smoking, statin use, and previous heart attacks) to predict their risk of heart disease. Participants’ health was tracked for seven to nine years, and their outcomes were compared to Framingham risk score (FRS) predictions.

A common tool for estimating heart disease risk, the FRS looks at age, gender, total cholesterol, high density lipoprotein cholesterol, smoking habits, and systolic blood pressure to estimate the probability someone will develop heart disease within a given span of time, usually 10 to 30 years.

The QUARTZ team compared their data to 10-year FRS predictions and said the algorithm’s accuracy was on par with that of the conventional tool.

Circa 2020 Basically this means a magnetic transistor can have not only quantum properties but also it can have nearly infinite speeds for processing speeds. Which means we can have nanomachines with near infinite speeds eventually.

Abstract The discovery of spin superfluidity in antiferromagnetic superfluid 3He is a remarkable discovery associated with the name of Andrey Stanislavovich Borovik-Romanov. After 30 years, quantum effects in a magnon gas (such as the magnon Bose–Einstein condensate and spin superfluidity) have become quite topical. We consider analogies between spin superfluidity and superconductivity. The results of quantum calculations using a 53-bit programmable superconducting processor have been published quite recently[1]. These results demonstrate the advantage of using the quantum algorithm of calculations with this processor over the classical algorithm for some types of calculations. We consider the possibility of constructing an analogous (in many respecys) processor based on spin superfluidity.

High-Risk, High-Payoff Bio-Research For National Security Challenges — Dr. David A. Markowitz, Ph.D., IARPA

Dr. David A. Markowitz, Ph.D. (https://www.markowitz.bio/) is a Program Manager at the Intelligence Advanced Research Projects Activity (IARPA — https://www.iarpa.gov/) which is an organization that invests in high-risk, high-payoff research programs to tackle some of the most difficult challenges of the agencies and disciplines in the U.S. Intelligence Community (IC).

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Continuous-time neural networks are one subset of machine learning systems capable of taking on representation learning for spatiotemporal decision-making tasks. Continuous differential equations are frequently used to depict these models (DEs). Numerical DE solvers, however, limit their expressive potential when used on computers. The scaling and understanding of many natural physical processes, like the dynamics of neural systems, have been severely hampered by this restriction.

Inspired by the brains of microscopic creatures, MIT researchers have developed “liquid” neural networks, a fluid, robust ML model that can learn and adapt to changing situations. These methods can be used in safety-critical tasks such as driving and flying.

However, as the number of neurons and synapses in the model grows, the underlying mathematics becomes more difficult to solve, and the processing cost of the model rises.

Algorithms that create art, text, and code are spreading fast—but legal challenges could throw a wrench in the works.

Deep Learning AI Specialization: https://imp.i384100.net/GET-STARTED
Nuclear fusion researchers have created a machine learning AI algorithm to detect and track the existence of plasma blobs that build up inside the tokamak for prediction of plasma disruption, the diagnosis of plasma using spectroscopy and tomography, and the tracking of turbulence inside of the fusion reactor. New AI supercomputer with over 13.5 million processor cores and over 1 exaflop of compute power made be Cerebras. A new study reveals an innovative neuro-computational model of the human brain which could lead to the creation of conscious AI or artificial general intelligence (AGI).

AI News Timestamps:
0:00 Breakthrough AI Runs A Nuclear Fusion Reactor.
3:07 New AI Supercomputer.
6:19 New Brain Model For Conscious AI

#ai #ml #nuclear

This could help achieve even world peace ✌️ called equilibrium theory by John Nash.

Game theory mathematics is used to predict outcomes in conflict situations. Now it is being adapted through big data to resolve highly contentious issues between people and the environment.

Game theory is a mathematical concept that aims to predict outcomes and solutions to an issue in which parties with conflicting, overlapping or mixed interests interact.

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Its why we should reverse engineer lab rat brains, crow brains, pigs, and chimps, ending on fully reverse engineering the human brain. even if its a hassle. i still think could all be done by end of 2025.

Last year, MIT researchers announced that they had built “liquid” neural networks, inspired by the brains of small species: a class of flexible, robust machine learning models that learn on the job and can adapt to changing conditions, for real-world safety-critical tasks, like driving and flying. The flexibility of these “liquid” neural nets meant boosting the bloodline to our connected world, yielding better decision-making for many tasks involving time-series data, such as brain and heart monitoring, weather forecasting, and stock pricing.

But these models become computationally expensive as their number of neurons and synapses increase and require clunky computer programs to solve their underlying, complicated math. And all of this math, similar to many physical phenomena, becomes harder to solve with size, meaning computing lots of small steps to arrive at a solution.

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Researchers solved a differential equation behind the interaction of two neurons through synapses, creating a faster AI algorithm.

Artificial intelligence uses a technique called artificial neural networks (ANN) to mimic the way a human brain works. A neural network uses input from datasets to “learn” and output its prediction based on the given information.

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