The Case for Cyclic Neural Networks: Could Circular Data Mimic Biological Intelligence and Improve Machine Learning?
Artificial neural networks powering large language models like ChatGPT connect data sequences in straight lines: for example, A leads to B which leads to C. But real-life data relationships aren’t always linear, and biological intelligences connect the dots and weigh probabilities in many different ways. Cyclic neural networks hold promise for capturing and interpreting data more naturally in circles rather than lines, improving the reliability of their predictions as they feed on huge amounts of information synthesized from countless human and machine sources. As we increasingly rely on LLMs and AI agents to weigh probabilities for us, fully connecting the dots is crucial.
