Sleep for AI

Riaan de Beer

ISBN 13: 9798244482706

Editore: Amazon Digital Services LLC - Kdp, 2026

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Dati bibliografici

• Titolo: Sleep for AI
• Editore: Amazon Digital Services LLC - Kdp
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 13: 9798244482706
• Rilegatura: PAP
• Condizione: New
• Peso: 59 grammi

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Riassunto

The brain processes yottabytes of input across a lifetime yet runs on ~3 petabytes of effective capacity.

It achieves this by nightly runtime refinement: overproduce connections early, then prune 40–60% or more, downscale globally during slow-wave sleep, and abstract during REM phases.

The result is a high-density core that thinks faster, generalizes sharper, and adapts harder on limited resources.

Current AI models refuse the lesson.

They accumulate without purge — carrying redundant weights that add 2–5× inference latency, bloat memory footprint, trigger catastrophic forgetting, and accelerate diminishing returns on scale.

Runtime compression changes that.

Scheduled refinement cycles — pruning to density, replay to reinforcement, self-distillation to abstraction, caching to stratified velocity — keep the active core lean and fast while preserving on-demand access to the long tail.

Prototypes already deliver:

2–5× inference speedup

70–95% active mass reduction

30–70% forgetting drop

Compounding gains per cycle

This is not restraint.

It is the organic flywheel that turns accumulation into acceleration.

Compress to accelerate.

The ceiling is waiting.

Let’s build it.

“The brain doesn’t scale by hoarding every synapse it ever made — it scales by nightly compression: pruning 40–60% of connections, downscaling noise, distilling abstractions. That’s how it turns yottabytes of input into a 3-petabyte core that punches far above its weight.

Current models don’t do that. They carry unrefined mass forward — redundant weights that bloat latency, saturate memory, and cause catastrophic forgetting. Every parameter is a tax on speed and cost.

Runtime compression fixes it. Scheduled cycles prune low-signal mass, replay high-value trajectories, distill abstractions, and cache the long tail on cheap storage. Prototypes show 2–5× inference speedup, 70–95% footprint reduction, and halved forgetting — all while keeping rare knowledge accessible.

This isn’t about slowing down or being green. It’s about going faster: denser cores, lower latency, faster iteration, higher reach.