kalomaze: fluid generalization is composing independent skills across domains end-to-end
kalomaze · x · 2026-09-07
kalomaze proposes a conceptual model of deep transfer learning: fluid generalization across domains is primarily a function of needing to compose independent skills from distinct domains together in an end-to-end compositional process. If you're trained on real-world skills, every weird problem unlike any professional playbook becomes a bottleneck — so models should generalize over a broader compositional space of constraints, even unrealistic ones, since specific environments rarely exercise this skill composition.
More from AGI Musings
- Economist Bharat Chandar named Research Director for AI Economics at Stanford Digital Economy Lab — RishiBommasani · 2026-10-27
- Aeon's former EVP: AI agents are the most impactful innovation of my lifetime — akumar · 2026-10-27
- Physicists Let AI Pick Its Own Problem—It Wrote a Publishable Paper in 2 Hours — burny_tech · 2026-10-27
- Deep learning veteran warns: don't let entrenched disbelief derail prep for the next big tech — jachiam0 · 2026-10-27
- Should platforms serve third-party AI agents? The antitrust question — sjgadler · 2026-10-27
- Musk: robot RAM demand will dwarf everything else, and 100% of superintelligence compute goes to deep space — elonmusk · 2026-10-27