AI Safety
New research argues AI systems claiming to be human-centric must demonstrate measurable human understanding capabilities, proposing frameworks for defining and testing these requirements.
AI Agents
A deep dive into engineering production-ready AI agents for healthcare, covering system architecture, MLOps pipelines, safety guardrails, and governance frameworks for high-stakes deployments.
LLM Safety
New research introduces a framework for evaluating implicit regulatory compliance in LLM tool invocations using logic-guided synthesis, addressing critical AI safety concerns.
deepfake regulation
The UK government is preparing to enforce legislation targeting companies that provide tools for creating AI deepfakes, marking a significant regulatory shift in synthetic media governance.
AI Certification
New research proposes maturity-based certification for embodied AI systems, introducing quantifiable trustworthiness metrics that could reshape how we evaluate AI reliability and authenticity.
OpenAI
Elon Musk's high-stakes legal battle against OpenAI will be decided by jury in March, with implications for AI governance and the future of frontier AI development.
AI Governance
Researchers propose comprehensive framework for governing agentic AI systems, mapping capabilities to risks and establishing safety protocols as autonomous agents become more prevalent.
data provenance
New research proposes a Compliance Rating Scheme that evaluates generative AI datasets for licensing, consent, and ethical sourcing—critical infrastructure for accountable synthetic media.
AI Governance
New research presents AI TIPS 2.0, a comprehensive framework helping organizations operationalize AI governance with tiered approaches for risk management and compliance.
DeepMind
Google DeepMind deepens collaboration with UK AI Security Institute on frontier AI safety evaluation, establishing frameworks that could shape how synthetic media and generative models are assessed globally.
AI Safety
New research proposes Cognitive Control Architecture, a supervision framework designed to maintain AI agent alignment throughout their operational lifecycle through structured oversight mechanisms.
AI Alignment
Researchers propose a scalable self-improving framework for open-ended LLM alignment that leverages collective agency principles to address evolving AI safety challenges.