Lifeboat Foundation

Just a couple of years earlier, in 1963, New Zealand mathematician Roy Kerr found a solution to Einstein’s equation for a rotating black hole. This was a “game changer for black holes,” Giorgi noted in a public lecture given at the virtual 2022 International Congress of Mathematicians. Rotating black holes were much more realistic astrophysical objects than the non-spinning black holes that Karl Schwarzschild had solved the equations for.

“Physicists really had believed for decades that the black hole region was an artifact of symmetry that was appearing in the mathematical construction of this object but not in the real world,” Giorgi said in the talk. Kerr’s solution helped establish the existence of black holes.

In a nearly 1,000-page paper, Giorgi and colleagues used a type of “proof by contradiction” to show that Kerr black holes that rotate slowly (meaning they have a small angular momentum relative to their mass) are mathematically stable. The technique entails assuming the opposite of the statement to be proved, then discovering an inconsistency. That shows that the assumption is false. The work is currently undergoing peer review. “It’s a long paper, so it’s going to take some time,” Giorgi says.

Argentinian-born mathematician Luis Caffarelli has won the 2023 Abel Prize — one of the most coveted awards in mathematics — for his work on equations that are important for describing physical phenomena, such as how ice melts and fluids flow. He is the first person born in South America to win the award.

Caffarelli’s results “are technically virtuous, covering many different areas of mathematics and its applications”, says a statement by Helge Holden, a mathematician at the Norwegian University of Science and Technology in Trondheim who chairs the Abel Committee.

The winner says receiving the news was an emotional moment, because “it shows that people have some appreciation for me and for my science”.

Since the recent announcements of OpenView’s ChatGPT, Google’s Bard, and Baidu’s ChatBot, the industry has been in a frenzy advancing Generative AI products and solutions. Brainy Insights estimates that the generative AI market will grow from USD $8.65 billion in 2022 and reach USD 4188.62 billion by 2032. This translates to over 36% CAGR making generative AI one of the next hottest areas to elevate AI innovations. The software segment will account for the highest revenue share of 65.0% in 2021 and is expected to retain its position over the forecast period.

What is Generative AI?

Generative AI is a form of AI that produce various types of content including text, imagery, audio and synthetic data. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds. Although not a new technology, the introduction of generative adversarial networks, or GANs which is a type of machine learning algorithm has advanced the innovations in using this form of AI.

Continue reading “COQUI : A Generative AI Speech Innovation Will Revolutionize This Market” »

Brainoids — tiny clumps of human brain cells — are being turned into living artificial intelligence machines, capable of carrying out tasks like solving complex equations. The team finds out how these brain organoids compare to normal computer-based AIs, and they explore the ethics of it all.

Sickle cell disease is now curable, thanks to a pioneering trial with CRISPR gene editing. The team shares the story of a woman whose life has been transformed by the treatment.

We can now hear the sound of the afterglow of the big bang, the radiation in the universe known as the cosmic microwave background. The team shares the eerie piece that has been transposed for human ears, named by researchers The Echo of Eternity.

To effectively tackle everyday tasks, robots should be able to detect the properties and characteristics of objects in their surroundings, so that they can grasp and manipulate them accordingly. Humans naturally achieve this using their sense of touch and roboticists have thus been trying to provide robots with similar tactile sensing capabilities.

A team of researchers at the University of Hong Kong recently developed a new soft tactile sensor that could allow robots to detect different properties of objects that they are grasping. This sensor, presented in a paper pre-published on arXiv, is made up of two layers of weaved optical fibers and a self-calibration algorithm.

“Although there exist many soft and conformable tactile sensors on robotic applications able to decouple the normal force and shear forces, the impact of the size of object in contact on the force calibration model has been commonly ignored,” Wentao Chen, Youcan Yan, and their colleagues wrote in their paper.

Algorithms are complex mathematical formulas used to perform tasks in our digital world. They are programmed to process information, make decisions, and take actions. Algorithms are used in various applications, such as search engines, social media, autonomous vehicles, and digital assistants.

But not all algorithms are innocent. Some algorithms have a sinister #scary side that poses a threat to our privacy, our freedom, and our humanity… #aiscarystories #aihorrorstories #scarystories #scarystory #horrorstories #horrorstory #realstories #realhorrorstories #realscarystories #truestories #truestory #creapystories #AIScarystory #AIHorror #artificialintelligence #scaryai #scaryartificialintelligence #trueaiscarystories #truescarystories.

Continue reading “Sinister Algorithms: The dark side of our future” »

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Experts at the Technical University of Munich (TUM) have pioneered the world’s first ethical algorithm for autonomous vehicles, which could see autonomous driving become the norm globally.

The researchers’ ethical algorithm is significantly more advanced than its predecessors, as it fairly distributes levels of risks instead of operating on an either/or principle. The algorithm has been tested in 2,000 scenarios of critical conditions in various settings, such as streets in Europe, the US, and China. The innovation could improve the safety and uptake of autonomous vehicles worldwide.

A new algorithm can organize hundreds of atoms into pristine patterns—including a honeycomb lattice, a fractal called a Sierpiński triangle, and a lion’s head.

Black box optimization methods are used in every domain, from Artificial Intelligence and Machine Learning to engineering and finance. These methods are used to optimize functions when an algebraic model is absent. Black box optimization looks into the design and analysis of algorithms for those problem statements where the structure of the objective function or the limitations defining the set is not known or explainable. Given a set of input parameters, black box optimization methods are designed to evaluate the optimal value of a function. This is done by iteratively assessing the function at multiple points in the input space so as to find the point that generates the optimal output.

Though gradient descent is the most used optimization approach for deep learning models, it is unsuitable for every problem. In cases where gradients cannot be calculated directly or where an objective function’s accurate analytical form is unknown, other approaches like Evolution Strategies (ES) are used. Evolution strategies come from evolutionary algorithms, which refer to a division of population-based optimization algorithms inspired by natural selection. Basically, Evolution Strategies (ES) is a type of Black Box Optimization method that operates by refining a sampling distribution based on the fitness of candidates and updating rules based on equations.

In a new AI paper, researchers from Deepmind, have introduced and developed a new way to use machine learning to learn the update rules from data, called meta-black-box optimization (MetaBBO), to make ES more flexible, adaptable, and scalable. MetaBBO works by meta-learning a neural network parametrization of a BBO update rule. The researchers have used MetaBBO to discover a new type of ES called learned evolution strategy (LES). The learned evolution strategy LES is a type of Set Transformer that updates its solutions based on the fitness of candidates and not depending upon the ordering of candidate solutions within the Black box evaluations. After meta-training, the LES can learn to choose the best-performing solution or update solutions based on a moving average.