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/

Global: Zendy Tools annual

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

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Autonomous systems that operate around humans will likely always rely on killswitches that stop their execution and allow them to be remote-controlled forthe safety of humans or to prevent damage to the system. It is theoreticallypossible for an autonomous system with sufficient sensor and effectorcapability that learn online using reinforcement learning to discover that thekill switch deprives it of long-term reward and thus learn to disable theswitch or otherwise prevent a human operator from using the switch. This isreferred to as the big red button problem. We present a technique that preventsa reinforcement learning agent from learning to disable the kill switch. Weintroduce an interruption process in which the agent's sensors and effectorsare redirected to a virtual simulation where it continues to believe it isreceiving reward. We illustrate our technique in a simple grid worldenvironment.

Enter the Matrix: Safely Interruptible Autonomous Systems via Virtualization