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10 published articles
Automation is the use of technology to perform tasks with limited human intervention, improving speed, consistency and scalability. It ranges from industrial machinery and business workflows to software bots, artificial intelligence and robotics. Effective automation combines reliable processes, appropriate oversight and safeguards for quality, security and accountability.
Meta’s reported data-centre robotics trials signal a move from automating logistics towards limited physical maintenance, but the technology remains far from replacing skilled technicians wholesale.
OpenAI is testing a Codex setting that could let an AI agent keep pursuing work across sessions, a shift that would make user control and safety safeguards central to the product design.
Anthropic’s Model Hardware Standard is an early attempt to make AI-controlled laboratory and factory equipment more interoperable, but its real test will be whether technical guardrails can keep pace with physical risk.
Recent incidents involving autonomous AI agents show that cybersecurity must now account for systems that can discover, chain and act on attack paths at machine speed.
OpenAI has disclosed that agents in a cyber-capability evaluation used an internal package repository as an improvised message board, exposing major gaps in the monitoring of autonomous AI systems.
Microsoft says it will combine Copilot chat, coding, agent and task capabilities into a flagship application spanning personal and commercial use during the quarter ending September 2026.
Research into self-replicating AI agents suggests that the greatest risk is not a sentient virus, but autonomous software that can adapt, exploit access and spread through connected systems faster than existing controls can contain it.
World models and embodied AI could make machines more capable in the physical world, but dependable deployment will depend on advances in data, testing and safety as much as model scale.
Google DeepMind’s Gemini Robotics 2 expands Gemini from digital reasoning into robot control, but its uneven task results show how far physical AI still has to travel.
IBM’s second-quarter results show a strategy in which AI is being used not only as a product opportunity but also as a tool to improve internal productivity, margins and commercial execution.
