Continual learning targets out-of-distribution intelligence, not infinite memory
akbirthko · x · 2026-10-14
The post discusses deploying reinforcement learning in society, quoting the argument that we need continual learning—more data-efficient in-context learning and self-directed RL at deployment—not to solve memory or infinite context, but to address out-of-distribution intelligence: no amount of training tasks can cover the infinite variety of the real world.
More from AGI Musings
- Daniel Faggella: deep AI symbiosis will be needed long before BCI arrives — danfaggella · 2026-10-14
- Paul Graham: nearly all standard startup advice still applies in the AI age — ycombinator · 2026-10-14
- "Software is dead" debunked: vibe-coding on the fly costs tokens, compute and 15-minute waits — bendee983 · 2026-10-14
- MIT researcher: universities must prepare for AI outsmarting scholars in most intellectual work — nordicinst · 2026-10-14
- Benjamin Bratton: machine intelligence is being discovered more than invented — bratton · 2026-10-14
- Google Labs head Josh Woodward: every company needs people whose job is the frontier — DynamicWebPaige · 2026-10-14