Saturday Morning NeuroSim, 6-15

An overview of topics from the reading queue. Including: developmental and bio-inspired AI. Conceptions of intelligence. Emergence and ways to computationally characterize emergent phenomena. Spreadsheets and ML/DL interpretability, building a LLM transformer in a spreadsheet. Neuroscience and the falsifiability of theories. Making your science community-driven (communicating results and incorporating feedback).
References:
Crockett (2024). How to Raise Your Artificial Intelligence: A Conversation with Alison Gopnik and Melanie Mitchell. LA Review of Books. May 31.
lareviewofbook...
Numenta. Thousand Brains project
www.numenta.co...
Holme (2023). A Better way of thinking about emergence. Petter Holme blog, May 31.
petterhol.me/2...
Rosas et.al (2024). Software in the natural world: A computational approach to hierarchical emergence. arXiv, 2402.09090.
Ball (2024). The New Math of How Large-Scale Order Emerges. June 10.
dabochen. Spreadsheet is all you need. Github. github.com/dab...
Lubick (2022). Building a ML Transformer in a Spreadsheet. dev.to, March 13.
dev.to/kjlubic...
LLM Visualization: bbycroft.net/llm
Lindsay (2024). Must a theory be falsifiable to contribute to good science? The Transmitter, June 10.
www.thetransmi...