Regulation

Why This Matters Now As organisations rush to deploy AI agents in messy, multi‑stakeholder environments, a familiar problem resurfaces: whose truth does the system act on? Compliance teams, product owners, regulators, domain experts — each brings their own logic, their own priorities, and often their own contradictions. In the real world, knowledge isn’t just incomplete; it’s perspectival. And default assumptions rarely hold universally. ...

Opening — Why This Matters Now Science is drowning in its own success. Papers multiply, datasets metastasize, and research teams now resemble micro‑startups juggling tools, protocols, and—yes—LLMs. The shift is subtle but seismic: AI is no longer a computational assistant. It’s becoming a workflow partner. That raises an uncomfortable question for institutions built on slow, deliberative peer review: what happens when science is conducted at machine speed? ...

Opening — Why this matters now The AI world is rediscovering an old truth: when agents learn to play many games, they learn to reason. General Game Playing (GGP) has long promised this—training systems that can pick up unfamiliar environments, interpret rules, and adapt. Elegant in theory, painfully slow in practice. The new Regular Games (RG) formalism aims to change that. It proposes a simple idea wrapped in an almost provocatively pragmatic design: make games run fast again. And for anyone building AI agents or simulations—from RL researchers to automation developers—the implications ripple far beyond board games. ...

Opening — Why This Matters Now Autonomous systems are finally leaving the sandbox. But the industry remains stuck in a familiar feedback loop: spectacular simulation breakthroughs paired with equally spectacular real‑world failures. Every demo reel looks perfect—until someone asks the painful question: “But will it work outside of Redwood City?” The real bottleneck isn’t compute or cleverness. It’s validation. Specifically, the endless hours required to re‑stage simulation failures in the physical world just to see if a perception model behaves the same way. ...

Opening — Why this matters now Artificial intelligence is finally discovering gravity — literally. After a decade of treating the world as a clean matrix of tokens, vectors, and latent spaces, the industry is colliding with a harder truth: intelligence that cannot touch the world cannot govern it. From collaborative robots to autonomous care systems, businesses now face a reality in which AI must not only reason, but balance weight, sense resistance, and modulate force. ...

Opening — Why this matters now Multi-agent systems are quietly becoming the backbone of modern automation: warehouse fleets, financial trading bots, supply-chain optimizers, and—if you believe the more excitable research labs—proto-agentic AI organizations. Yet there’s a peculiar, recurring problem: when you ask agents to improve by playing against each other, they sometimes discover that the fastest route to “winning” is to make sure nobody wins. ...

Opening — Why this matters now The AI industry is celebrating multimodal models as if they can already do things. Look at a picture, generate a plan, and—supposedly—convert visual understanding into executable action. But when you swap the glossy demos for a domain that demands fine-grained, symbolic precision—like crochet—the illusion cracks. CrochetBench, a new benchmark evaluating whether vision‑language models can move from describing to doing, is far more than a quirky dataset. It is a stress test for the kind of procedural reasoning that underpins robotics, manufacturing automation, and any AI system meant to execute real-world workflows. ...

Opening — Why this matters now The AI world is getting bolder — talking about agentic workflows, self-directed automation, multimodal copilots, and the eventual merging of reasoning engines with operational systems. Yet beneath the hype lies a sobering question: Can today’s most powerful LLMs actually plan? Not philosophically, but in the cold, formal sense — step-by-step, verifiable, PDDL-style planning. ...

Opening — Why this matters now Generative AI has become a prolific factory of synthetic text, code, images—and occasionally, trouble. As models scale, so do the ways they can fail. Some failures are visible (toxic text, factual errors), but others are engineered to be invisible: steganography buried in an innocent paragraph, subtle security vulnerabilities in model‑generated code, or quietly embedded backdoor triggers. ...

Opening — Why this matters now Every scientific field has its own version of the same quiet frustration: we can model what we already understand, but what about the structure we don’t? As AI systems spread into physics, astronomy, biology, and high‑dimensional observation pipelines, they dutifully compress the data we give them—while just as dutifully baking in our blind spots. ...