Formalizing AI computation in terms of algorithmic complexity can offer a formal way to quantify AI systems while offering a principled foundation to build more algorithmically capable systems in the future.
Blog: research.ibm.com/blog/ai-algo...
arXiv: arxiv.org/abs/2411.05943

While using AI models to generate code is commonplace these days, we still do not fully understand the limits of the complexity of the code these models can formulate.
3/n

Using circuits to formalize algorithmic problems for AI models (e.g., depth as time complexity, size as space complexity), we can quantify the complexity of circuit computations (algorithmic complexity) an AI model can perform.
2/n

And relatedly, Felix wrote a good piece on the stress and anxiety currently affecting many people who work in AI due to the current climate in the industry:

docs.google.com/document/d/1...

If only more folks in AI were gentle and introspective like this...

📌 Poster Session:
⏰ When: TODAY, Thu, Dec 12, 4:30 p.m. – 7:30 p.m. PST
📍 Where: East Exhibit Hall A-C, #3705
📄 What: Geometry of Naturalistic Object Representations in Recurrent Neural Network Models of Working Memory

Hope to see you there!
@bashivan.bsky.social @takuito.bsky.social