What Spectroscopy Was to the 1800s, Embeddings Are to Science Now
3 months ago
- #AI
- #Embeddings
- #Neural Networks
- Embeddings represent a new methodology for examining complex systems beyond current theoretical frameworks.
- Neural networks can predict chaotic dynamics further into the future than previously thought possible, challenging classical predictability measures.
- Variational autoencoders learn latent spaces (embeddings) that capture system interactions, useful for clustering, supervised learning, and generating new data.
- Embeddings enable diverse applications, from weather simulation to mapping Earth's surface with high accuracy.
- The 'Platonic Representation Hypothesis' suggests neural networks converge to a shared statistical model of reality in their representation spaces.
- Embeddings may reveal universal information structures, analogous to how spectroscopy revealed fundamental physics in the 19th century.
- A key question is whether embeddings converge to the universe's information structure or human representations of it.
- AI for science could lead to discoveries about the universe's workings, beyond reconstructing known phenomena.