Lifeboat Foundation

Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

In recent years, artificial intelligence (AI) and deep learning models have advanced rapidly, becoming easily accessible. This has enabled people, even those without specialized expertise, to perform various tasks with AI. Among these models, generative adversarial networks (GANs) stand out for their outstanding performance in generating new data instances with the same characteristics as the training data, making them particularly effective for generating images, music, and text.

GANs consist of two neural networks, namely, a generator that creates new data distributions starting from random noise, and a discriminator which checks whether the generated data distribution is “real” (matching the training data) or “fake.” As training progresses, the generator improves at generating realistic distributions, and the discriminator at identifying the generated data as fake.

GANs use a loss function to measure differences between the fake and real distributions. However, this approach can cause issues like gradient vanishing and unstable learning, directly impacting stability and efficiency. Despite considerable progress in improving GANs, including structural modifications and loss function adjustments, challenges such as gradient vanishing and mode collapse, where the generator produces a limited variety, continue to limit their applicability.

Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

There are four essential characteristics of human intelligence that current AI systems donft possess: reasoning, planning, persistent memory, and understanding the physical world. Once we have systems with such capabilities, it will still take a while before we bring them up to human level.

Approximately 41 000 years ago, Earth’s magnetic field briefly reversed during what is known as the Laschamp event. During this time, Earth’s magnetic field weakened significantly—dropping to a minimum of 5% of its current strength—which allowed more cosmic rays to reach Earth’s atmosphere.

Scientists at the Technical University of Denmark and the German Research Centre for Geosciences used data from ESA’s Swarm mission, along with other sources, to create a sounded visualisation of the Laschamp event. They mapped the movement of Earth’s magnetic field lines during the event and created a stereo sound version which is what you can hear in the video.

Continue reading “Sound of Earth’s magnetic flip 41 000 years ago” »

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