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Could AI Agents Change What Middle Managers Do?

Impact

Could AI Agents Change What Middle Managers Do?

Explore how AI agents reshape middle management: automating coordination, shifting decision rights, and pushing managers toward judgment, accountability, and system design.

Nancy Miller
How Kids Feel About AI, in Their Own Words

Impact

How Kids Feel About AI, in Their Own Words

How kids describe AI in their own words—what they think it is, where they learn it, and how hopes, fears, rules, and fairness shift from ages 7 to 17.

Christin Shatzman
AI in Court: Why Are These Cases So Difficult to Resolve?

Impact

AI in Court: Why Are These Cases So Difficult to Resolve?

AI in court cases are hard to resolve because they split into model, data, workflow, and contract issues—plus reliability proof, bias evidence, discovery fights, and responsibility.

Madison Evans
AI Agents Are Not Your “Coworkers”

Impact

AI Agents Are Not Your “Coworkers”

AI agents aren’t coworkers: learn why the metaphor misleads, the risks of delegating judgment, and how to set boundaries, permissions, reviews, and autonomy.

Paula Miller
Debates Over AI Consciousness Are a Trap

Basics Theory

Debates Over AI Consciousness Are a Trap

Why AI consciousness debates hijack discussion, and what to do instead: focus on capability thresholds, evidence, and governance to reduce real-world AI harms.

Korin Kashtan
The Path to Artificial Superintelligence

Basics Theory

The Path to Artificial Superintelligence

Explore artificial superintelligence (ASI): what it is, the capability ladder to agents, scaling drivers, real bottlenecks, and alignment, control, and misuse risks.

Juliana Daniel
Roundtables: Can AI Learn to Understand the World?

Basics Theory

Roundtables: Can AI Learn to Understand the World?

Explore whether AI can truly understand the world—world models, causality, embodied experience, and hard-to-game tests that measure reliable reasoning.

Triston Martin
What Makes a Multimodal Model Useful Across Different Tasks?

Technologies

What Makes a Multimodal Model Useful Across Different Tasks?

Learn what makes a multimodal model useful across tasks: shared concepts, better prompt packaging, grounded perception-to-action reasoning, realistic evaluation, and ops constraints.

Darnell Malan

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