Day Two

One year on: The momentum behind Jigsaw’s Sensemaking AI

How our suite of AI tools is helping public leaders bridge the gap between individual voices and concrete policy decisions

Sensemaking AI | Jigsaw

Sensemaking is a suite of AI-powered tools designed to help public leaders understand public opinion quickly. By combining AI interviewing with automated reporting, we bridge the gap between individual voices and actionable policy insights.

Sensemaking and Science Publishing | Ronen Tamari

Sensemaking as a new framework for understanding why science publishing/peer review is broken, and how to fix it

The Sensemaking Scenius

...

Sensors, not just bookmarks [Patterns of Sensemaking #1] - Cosmik Labs

First of a new series on sensemaking patterns we're observing on Semble

Sensors, not just bookmarks [Patterns of Sensemaking #1] - Cosmik Labs

First of a new series on sensemaking patterns we're observing on Semble

interviews.md at main · sensemaker.computer/home

Sensemaker's home repository for public resources.

Sense-making reconsidered: large language models and the blind spot of embodied cognition

Large Language Models (LLMs) demonstrate a kind of linguistic competence that theories of embodied and enactive cognition have long deemed impossible for systems lacking the meaningful perspective of a living being, i.e., the capacity for sense-making. Facing up to this unexpected technological development requires confronting what I propose to call the “AI dilemma”: either frontier LLMs are capable of sense-making despite lacking biological embodiment, or the kind of linguistic competence they exhibit does not necessarily require sense-making. In their chapter on cognition, Frank, Thompson, and Gleiser (2024) maintain that no AI system comes close to realizing relevance, a position that derives much of its motivation from past practical failures. However, frontier LLMs have effectively overcome Dreyfus’ commonsense knowledge problem, such that their dismissal as categorically mindless risks undermining Frank et al.’s central claim that human cognition is deeply intertwined with lived experience. I therefore argue in favor of the alternative side of the AI dilemma: human-level linguistic competence of LLMs should be recognized as a novel non‑biological form of sense‑making, based on a technologically‑mediated embodiment whose enabling properties are in need of further theoretical analysis. This reorientation invites enactive theory to clarify which aspects of sense-making may be universal and which aspects are specifically contingent on organic life, thereby advancing its conceptual framework in dialogue with contemporary AI.

Part 2: Sensemaking Networks Project Plan - Cosmik Labs

Integrating fragmented science social media and reducing the barriers to semantic publishing with NLP

Cosmik

Tools and networks for collective sensemaking