Lifeboat Foundation

An international research team including Los Alamos National Laboratory and Tel Aviv University has developed a unique, mechanical metamaterial that, like a computer following instructions, can remember the order of actions performed on it. Named Chaco, after the archaeological site in northern New Mexico, the new metamaterial offers a route to applications in memory storage, robotics, and even mechanical computing.

Be sure to look at the night sky on June 3rd to see a somewhat rare event called a large ‘planetary parade.’

OpenAI has landed itself in hot water for pushing out an update to ChatGPT that features a virtual assistant with an uncanny vocal resemblance to Scarlett Johansson — and it could be staring down the barrel of a compelling lawsuit.

Almost instantly, comparisons to the movie “Her” abounded, in which the actress plays a chatbot named Samantha that falls in love with a lonely man. Had OpenAI just aped her role — and her voice? Officially, it said no. Then, Johansson dropped a bombshell: leadership at the AI startup had in fact asked permission to use her voice last year. She said no, and they did it anyway.

Two anecdotes and charts: Women’s and men’s college graduation rates.

Anecdote:

I gave an extra-credit assignment in one of my courses. The course had twenty students, eleven men and nine women. Six students chose to do the assignment — and then I noticed something in my grade book: all of them were women. Getting all statistic-y about it: 66.7% of the females were willing to do the extra work, while 0% if the men were.

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The codes to replicate the simulations of the paper: Available at: https://arxiv.org/abs/2405.12832 and also: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4835325

For now, we just added the codes to…

In this paper, we introduce Wav-KAN, an innovative neural network architecture that leverages the Wavelet Kolmogorov-Arnold Networks (Wav-KAN) framework to enhance interpretability and performance. Traditional multilayer perceptrons (MLPs) and even recent advancements like Spl-KAN \cite{kan} face challenges related to interpretability, training speed, robustness, computational efficiency, and performance. Wav-KAN addresses these limitations by incorporating wavelet functions into the Kolmogorov-Arnold network structure, enabling the network to capture both high-frequency and low-frequency components of the input data efficiently. Wavelet-based approximations employ orthogonal or semi-orthogonal basis and also maintains a balance between accurately representing the underlying data structure and avoiding overfitting to the noise. Analogous to how water conforms to the shape of its container, Wav-KAN adapts to the data structure, resulting in enhanced accuracy, faster training speeds, and increased robustness compared to Spl-KAN and MLPs. Our results highlight the potential of Wav-KAN as a powerful tool for developing interpretable and high-performance neural networks, with applications spanning various fields. This work sets the stage for further exploration and implementation of Wav-KAN in frameworks such as PyTorch, TensorFlow, and also it makes wavelet in KAN in wide-spread usage like nowadays activation functions like ReLU, sigmoid in universal approximation theory (UAT).

O.o!!!

The highly unstable radioactive element promethium is hard to study in the lab, but chemists have now coaxed it into forming a compound in water so they can observe its bonding behaviour.

Quantum computers, computing devices that leverage the principles of quantum mechanics, could outperform classical computing on some complex optimization and processing tasks. In quantum computers, classical units of information (bits), which can either have a value of 1 or 0, are substituted by quantum bits or qubits, which can be in a mixture of both 0 and 1 simultaneously.

The Sun’s magnetic field is an incredibly powerful mechanism that produces equally powerful solar storms, some of which resulted in the recent aurora activity observed as far south as the State of Florida. However, in the 400 years since Galileo Galilei first discovered the Sun’s magnetic field, scientists have been stumped regarding where inside the Sun the magnetic field originates. This is what a study published today in Nature hopes to address as a team of international researchers have discovered how deep inside the Sun the magnetic field originates, which holds the potential to help scientists better understand and predict solar storms.

“Understanding the origin of the sun’s magnetic field has been an open question since Galileo and is important for predicting future solar activity, like flares that could hit the Earth,” said Dr. Daniel Lecoanet, who is an Assistant Professor of Engineering Sciences and Applied Mathematics at Northwestern University and a co-author on the study. “This work proposes a new hypothesis for how the sun’s magnetic field is generated that better matches solar observations, and, we hope, could be used to make better predictions of solar activity.”

For the study, the researchers used a NASA supercomputer to conduct several calculations to ascertain if the source of the Sun’s magnetic field was close to the surface or much deeper, as previous hypotheses have stated the magnetic field’s source is more than 130,000 miles beneath the surface of the Sun. In the end, the researchers of this latest study estimated the source of the Sun’s magnetic field is approximately 20,000 miles beneath the surface. For context, the diameter of the Sun is just over 865,000 miles across, so these new findings indicate the magnetic field originates approximately 2 percent beneath the Sun’s surface, as opposed to 15 percent based on the previous hypotheses.

editor of[Possible Minds: Twenty-Five Ways of Looking at AI], talked about the future of artificial intelligence with two contributors to the book, David Chalmers and Daniel Dennett.