Are AI Models Becoming a Hivemind?

Ask different AI models the same open-ended question and you might expect different answers. But what if they all start sounding the same?

Let's read and discuss on "Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)" which examines the "Artificial Hivemind" effect across thousands of real-world, open-ended prompts.

Lets unpack how the researchers measure diversity, where this homogeneity shows up, and what happens when seemingly similar AI outputs meet human preferences that are far more varied.

Then we'll take the question beyond the paper: Do we actually want AI systems to agree? Bring your own perspectives as we discuss when disagreement is useful, whose values AI should reflect, and how much diversity we actually want from the systems we use.

❗Please Read Before You Register❗

This isn't a seminar or lecture. It's a discussion, and everyone in the room is part of it.

No expertise required. Just curiosity and a willingness to think out loud together. Whether you're AI-curious, work with AI, or simply interested in the topic, you're welcome.

We’ll do the bulk of the reading during the session together! Then we’ll use it as a shared starting point for the discussion, so coming prepared will help everyone get more out of the conversation.

Bring your own perspectives. If you have relevant experiences, examples from your work, or other research that challenges or adds to the paper, we'd love to hear them.

More About the Host

Jared Cheang is an AI Safety researcher with a background in data science, software engineering, and building AI solutions for the public sector. His current research interests span AI control, alignment, and safety-by-design, with a particular interest in how increasingly capable AI systems can remain reliable and aligned with human values. Jared also teaches and mentors across the local tech community, and holds a Master's in Business Analytics from NUS.

More About the Series

Paper Club is Lorong AI’s community-driven initiative where members gather to discuss and analyze academic papers, research articles, or key developments in AI.

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