Intellectual hub of the topic
The concept of direct human involvement in automated processes extends far beyond the boundaries of classical programming. In this collection, we explore scenarios where human experience, intuition, and ethical judgment remain a critical link in the operation of complex systems. We view the interaction between the biological and the technological not as a conflict, but as a method for verification and fine-tuning results that cannot be achieved through purely algorithmic means.
Google DeepMind has investigated how AI can influence people's decisions, especially in vulnerable situations, and has developed measures to protect against manipulation.
AI: Events
Research
LG AI Research has introduced AEGIS – an industrial anomaly detection system that learns from experts during operation and adapts to real manufacturing conditions.
We explore why AI agents don't guarantee consistent results and what can be done to make them trustworthy.
OpenAI has developed the IH-Challenge approach, which helps language models correctly prioritize instructions from different sources.
AI: Events
Development
GitHub explained how the Squad system works – an AI agent coordinator that operates directly within a repository and makes multi-step tasks transparent.
GitHub has shared its approach to securing agentic workflows, relying on isolation, limited permissions, and detailed logs to ensure AI agents operate safely.
A look at how modern speech recognition and intent analysis systems are transforming contact centers and why it matters to every customer.
Lab
Computer Science
How feminist principles and collaborative workshops are changing the approach to data annotation – and why it matters for fair AI.
AI: Events
Security
We explore the threats posed by autonomous AI agents and how engineers are building defenses to prevent these systems from getting out of control.
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