mirror of
https://github.com/jackyzha0/quartz.git
synced 2025-12-20 11:24:05 -06:00
19 lines
1.6 KiB
Markdown
19 lines
1.6 KiB
Markdown
---
|
|
title: The model-able space of user identity is enormous
|
|
date: 05.11.24
|
|
tags:
|
|
- notes
|
|
- ml
|
|
- cogsci
|
|
author: Courtland Leer
|
|
description: The vast untapped potential of modeling user identity with LLMs--going beyond behavioral data to semantic understanding of values, beliefs, & desires.
|
|
---
|
|
While large language models are exceptional at [imputing a startling](https://arxiv.org/pdf/2310.07298v1) amount from very little user data--an efficiency putting AdTech to shame--the limit here is [[ARCHIVED; User State is State of the Art|vaster than most imagine]].
|
|
|
|
Contrast recommender algorithms (which are impressive!) needing mountains of activity data to back into a single preference with [the human connectome](https://www.science.org/doi/10.1126/science.adk4858) containing 1400 TB of compressed representation in one cubic millimeter.
|
|
|
|
LLMs give us access to a new class of this data going beyond tracking the behavioral, [[LLMs excel at theory of mind because they read|toward the semantic]]. They can distill and grok much 'softer' physiological elements, allowing insight into complex mental states like value, belief, intention, aesthetic, desire, history, knowledge, etc.
|
|
|
|
There's so much to do here though, that plug-in-your docs/email/activity schemes, user surveys are laughably limited in scope. We need ambient methods running social cognition, like [Honcho](https://honcho.dev).
|
|
|
|
As we asymptotically approach a fuller accounting of individual identity, we can unlock more positive sum application/agent experiences, richer than the exploitation of base desire we're used to. |