Lifeboat Foundation

KAN or MLP: a fairer comparison.

R Yu, W Yu, X Wang [National University of Singapore] (2024) Paper: http://huggingface.co/papers/2407.16674 Github: http://github.com/yu-rp/KANbeFair.

- Under the same number of parameters or FLOPs, KAN outperforms MLP only in symbolic formula representing, but…

Continue reading “Yu-rp/KANbeFair: A More Fair and Comprehensive Comparison between KAN and MLP” »

New studies on astronauts and space biology bring humanity one step closer to the final frontier.

The Space Omics and Medical Atlas (SOMA) package of manuscripts, data, protocols, and code represents the largest-ever compendium of data for aerospace medicine and space biology.

An AT&T outage map shows a reported issue covering much of San Antonio on Wednesday evening, July 24. The outage could last into Thursday, AT&T says.

Machine learning (ML) is one of the most important subareas of AI used in building great AI systems.

In ML, deep learning is a narrow area focused solely on neural networks. Through the field of deep learning, systems like ChatGPT and many other AI models can be created. In other words, ChatGPT is just a giant system based on neural networks.

However, there is a big problem with deep learning: computational efficiency. Creating big and effective AI systems with neural networks often requires a lot of energy, which is expensive.

Neural networks biological and artificial.

Neural Networks have found applications across various domains due to their ability to learn from data and improve over time without human intervention. They can solve challenging problems that are hard or impossible to solve using traditional methods. Here are some of the examples of how neural networks and artificial neurons are used in real-world scenarios:

Voice assistants: Voice assistants like Siri and Alexa use neural networks to understand spoken language commands and questions. They use trained models based on artificial neurons processing vast datasets of speech and text data. They can also generate natural-sounding responses and perform various tasks, such as playing music, setting reminders, searching the web, etc.

Continue reading “Neural Networks: From Biological to Artificial” »

Error-correcting a quantum computer can mean processing 100TB every second.

Learn more about neural networks and large language models on Brilliant! First 30 days are free and 20% off the annual premium subscription when you use our link ➜ https://brilliant.org/sabine.

A lot of big banks are banking on quantum computing because they think it’ll give them an edge in trading. Though I have on previous occasions noted my doubt that we’ll see any useful quantum computers within the next ten years, two new papers detailing new methods of scaling quantum computers have shifted my perspective. Let’s have a look.

Continue reading “Big News for Quantum Computing: First Scalable Platforms” »

Quantum: Check out Our Video on Aduro: https://twobit.link/AduroVideoLearn More About Aduro: https://twobit.link/AduroQuantum Computers are are confusing and…

New research may have found a link between supermassive black holes and dark matter particles which might solve an issue which has irked astrophysicists for decades: the “final parsec problem.”

Last year, an international team of researchers discovered a background “hum” of gravitational waves. They hypothesised that this background signal is emanating from millions of merging pairs of supermassive black hole.

Supermassive black holes are hundreds of thousands to billions of times larger than our Sun.