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Monday, 7 September 2026 Reading Room

AI

Quoting Jakub Pachocki

The strongest argument I see for continuing to train much smarter models quickly is the need to build defensive systems against the dangers posed by other AI. [...] We will need powerful, aligned AI for defense; to secure infrastructure, to protect against rogue agents in real time, and to invent entirely new protective measures. This will be a primary focus of OpenAI’s deployment efforts. At the…

simonwillison.net ·

We haven't written up this one. Simon Willison has the full story — the link below goes straight to it.

Read the original at simonwillison.net →

More in AI

Domain Randomization for Robust Robot Learning

Domain Randomization for Robust Robot Learning Domain randomization is the idea that, instead of trying to perfectly match simulation to reality, you randomize simulation parameters widely enough that…

  • Domain randomization enhances robot learning robustness in simulation.
  • Trains policy on diverse simulated scenarios with varied conditions.
  • Improves transferability to real-world applications by handling unexpected challenges.

·dev.to

Sim-to-Real Transfer for Physical AI Robots

Sim-to-Real Transfer for Physical AI Robots A policy that hits 95% success in simulation and 20% on the real robot is one of the most common — and most frustrating — outcomes in robot learning.

  • Sim-to-real gap arises from visual, dynamics, and sensing/latency differences.
  • System identification involves measuring real robot and tuning simulation parameters.
  • Progressive validation strategy uses multiple evaluation checkpoints.

·dev.to

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