AI

The dream of generating a fully realized, minute-long video from a short text prompt has always run aground on three reefs: disjointed narratives, visual glitches, and characters that morph inexplicably between shots. MAViS (Multi-Agent framework for long-sequence Video Storytelling) takes aim at all three by treating video creation not as a single monolithic AI task, but as a disciplined production pipeline staffed by specialized AI “crew members.” The Problem with One-Shot Generators Single-pass text-to-video systems shine in short clips but crumble under the demands of long-form storytelling. They repeat motions, lose scene continuity, and often rely on users to do the heavy lifting—writing scripts, designing shots, and manually training models for character consistency. This is not just a technical shortcoming; it’s a workflow bottleneck that makes creative scaling impossible. ...

Most warnings about algorithmic collusion tell the same story: sellers using AI to set prices end up coordinating—without explicit communication—to keep prices higher than competition would allow. This is what regulators fear: supra-competitive prices, reduced consumer welfare, and harder-to-detect anti-competitive behavior. A new study, however, flips the narrative on its head. By analyzing multi-dimensional decision-making—where reinforcement learning (RL) agents set both prices and advertising bids on a platform like Amazon—the authors uncover a surprising outcome: in markets with high consumer search costs, algorithmic “collusion” can lower prices below competitive benchmarks. ...

In the world of Retrieval-Augmented Generation (RAG), most systems still treat document retrieval like a popularity contest — fetch the most relevant-looking text and hope the generator can stitch the answer together. But as any manager who has tried to merge three half-baked reports knows, relevance without completeness is a recipe for failure. A new framework, Compositional Answer Retrieval (CAR), aims to fix that. Instead of asking a retrieval model to find a single “best” set of documents, CAR teaches it to think like a strategist: break the question into its components, retrieve for each, and then assemble the pieces into a coherent whole. ...

A Hidden Cost of AI Efficiency When AI takes over routine tasks, companies often see immediate productivity gains. Senior staff can accomplish more without relying on juniors, costs go down, and short-term profits rise. But beneath these benefits lies a risk that most boardrooms overlook: the erosion of tacit knowledge—the hands-on expertise that only develops through years of guided practice. Tacit skills aren’t in manuals or knowledge bases. They’re the intuition of a surgeon who adapts mid-procedure, the judgment of a lawyer during negotiations, the troubleshooting instincts of an engineer. These skills pass from experts to novices mainly through direct collaboration on real work. Remove the entry-level work, and you cut the ladder that builds tomorrow’s experts. ...

When business leaders ask for a “quick chart,” they rarely expect to become detectives in the aftermath—trying to work out why the AI picked that chart type, grouped the data that way, or left out important categories. Yet that’s exactly the frustration with most Natural Language to Visualization (NL2VIS) tools today: they generate results like a magician pulling a rabbit from a hat, with no insight into how the trick was done. ...

In AI development, removing humans from the training loop has long been a holy grail — not because people aren’t valuable, but because human labeling is expensive, slow, and fundamentally limited. R-Zero, a new framework from Tencent AI Seattle Lab, takes a decisive step in that direction: no seed dataset, no human annotations, and no external verifier. Just two AI roles — Challenger and Solver — locked in an evolutionary arms race. ...

The most radical idea in Michael I. Jordan’s latest manifesto isn’t a new model, a benchmark, or even a novel training scheme. It’s a reorientation. He argues that we’ve misdiagnosed the nature of intelligence—and in doing so, we’ve built AI systems that are cognitively brilliant yet socially blind. The cure? Embrace a collectivist, economic lens. This is not techno-utopianism. Jordan—a towering figure in machine learning—offers a pointed critique of both the AGI hype and the narrow symbolic legacy of classical AI. The goal shouldn’t be to build machines that imitate lone geniuses. It should be to construct intelligent collectives—systems that are social, uncertain, decentralized, and deeply intertwined with human incentives. In short: AI needs an economic imagination. ...

The search for the elusive optimal portfolio has always been a balancing act between signal and noise. Covariance matrices, central to risk estimation, are notoriously fragile in high dimensions. Classical fixes like shrinkage, spectral filtering, or factor models have all offered partial answers. But a new paper by Bongiorno, Manolakis, and Mantegna proposes something different: a rotation-invariant, end-to-end neural network that learns the inverse covariance matrix directly from historical returns — and does so better than the best analytical techniques, even under realistic trading constraints. ...

From Ballots to Bots: Reprogramming Democracy for the AI Era Cognaptus Insights Democracy, at its core, is a decision-making system designed to fairly resolve conflicts and distribute resources in society. Historically, it has depended on human political agents—elected representatives who negotiate on behalf of their constituents. But as artificial intelligence matures, this centuries-old mechanism may be heading for a systemic rewrite. A Brief History of Democratic Pitfalls From Athenian direct democracy to parliamentary representation and constitutional republics, political systems have evolved to solve the problem of collective decision-making. Yet across cultures and eras, common systemic pitfalls emerge: ...

In the expanding domain of artificial intelligence, creativity is no longer a human-only endeavor. From music composition to visual art and storytelling, AI agents are taking on increasingly creative roles. But as these systems become more proactive, one question looms large: who’s really in control? Enter MOSAAIC — a framework developed to guide the design of co-creative systems by managing autonomy, initiative, and authority in shared human-AI decision-making. The Three Pillars: Autonomy, Initiative, and Authority The authors define three interrelated yet distinct aspects of control: ...