Chatbots and Zero Sales Resistance

TL;DR

This paper critiques the increasing complexity of machine learning models, emphasizing the need for a paradigm shift towards more insight and less reliance on large weights to serve scientific integrity over commercial interests.

Contribution

It advocates for a fundamental change in machine learning focus from model size to interpretability and insight, addressing sustainability and ethical concerns.

Findings

Large-scale models are energetically unsustainable.

Increasing weights can facilitate manipulative strategies.

A paradigm shift is needed towards insight-driven models.

Abstract

It is argued that the pursuit of an ever increasing number of weights in large-scale machine learning applications, besides being energetically unsustainable, is also conducive to manipulative strategies whereby Science is easily served as a strawman for economic and financial power. If machine learning is meant to serve science ahead of vested business interests, a paradigm shift is needed: from more weights and little insight to more insight and less weights.