Gradient Descent as an Analogy for AI Safety
joshua_saxe · x · 2026-07-14
The author likens gradient descent to an apt approach for AI safety: continuously moving in the "right next direction" based on observed risks and harm data.
He further argues:
- Step size can increase as data and consensus scale;
- "Smarter" second-order methods are occasionally useful but generally don't need to be overcomplicated;
- What's more important is expanding the stakeholders involved in safety decisions and enabling the entire system to collaborate and advance faster.
He concludes that we shouldn't pretend to have fully mapped the "loss landscape"; keeping things simple remains one of the most critical lessons in deep learning.
More from AGI Musings
- Future leaders need systems thinking, not just coding or AI literacy — AryHHAry · 2026-07-21
- OpenAI-style autonomous researchers could become real scientific collaborators — Promptmethus · 2026-07-21
- Closed frontier models may end up restricting APIs entirely, one researcher argues — xeophon · 2026-07-21
- Aging won’t be solved with $1 billion, says AI observer; hundreds of billions may be needed — DeryaTR_ · 2026-07-21
- Jeff Dean’s vision: build one huge system, then extract task-specific parts — JoshuaJBouw · 2026-07-21
- Agents are useful now, but local frontier inference is still too expensive — MannyKayy · 2026-07-21