English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We consider regret minimization in repeated games with a very large number ofactions. Such games are inherent in the setting of AI Safety via Debate\cite{irving2018ai}, and more generally games whose actions are language-based.Existing algorithms for online game playing require per-iteration computationpolynomial in the number of actions, which can be prohibitive for large games.  We thus consider oracle-based algorithms, as oracles naturally model accessto AI agents. With oracle access, we characterize when internal and externalregret can be minimized efficiently. We give a novel efficient algorithm forsimultaneous external and internal regret minimization whose regret dependslogarithmically on the number of actions. We conclude with experiments in thesetting of AI Safety via Debate that shows the benefit of insights from ouralgorithmic analysis.

Playing Large Games with Oracles and AI Debate