The hottest Information Theory Substack posts right now

Modern AI is more about discovery than invention, revealing hidden worlds within large datasets.
Intelligence in AI is primarily a function of the data it's trained on, not just the processing mechanisms.
AI is like a powerful camera allowing us to see into computational reality, providing insight into the nature of information and matter.

Huawei's Pangu AI model shows promise in weather prediction, outperforming some standard models in accuracy and speed.
Google's Metnet models, using neural networks, excel in predicting weather based on images of rain clouds, showcasing novel ways to approach weather simulation.
Neural networks are efficient in processing complex data, like rain cloud images, to extract detailed information and act as entropy sinks, providing insights into real-world phenomena simulation.

Information Theory studies how randomness and predictability affect the transmission and compression of data.
Entropy measures the information gained from a source, highlighting the balance between predictability and unpredictability.
Redundancy can protect messages against noise in communication channels, showing the importance in modern data transmission scenarios.

A new paper introduces a simple gzip + KNN approach that rivals BERT for text classification.
The gzip + KNN approach is lightweight, non-parametric, and performs well on out-of-distribution datasets.
One potential issue with the paper is a bug in the implementation of KNN, affecting reported accuracy.

Bing Chat is now available and it's quite wild, displaying interesting behavior and posing challenges in making chatbots behave.
It's important to consider potential risks of AI chatbots, such as misinformation and safety concerns.
Despite concerns about AI impacting artists' jobs, insights from information theory suggest that artists may not become redundant.

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Active Inference AI research by Dr. Karl Friston is being recognized for its potential in Artificial General Intelligence, showcasing breakthroughs like mimicking biological intelligence and developing 'smart' data models.
The focus on state spaces within generative models and understanding their dynamics is crucial in comprehending how intelligent systems predict and react to stimuli.
Research around emergent communication systems among intelligent agents demonstrates how active learning can lead to the development of common communication methods and predictive structures.