Social Evolution of Published Text and The Emergence of Artificial Intelligence Through Large Language Models and The Problem of Toxicity and Bias

TL;DR

This paper reviews the rapid development of AI and Large Language Models from a social perspective, highlighting both their advancements and challenges like toxicity and bias, and discusses emergent phenomena related to scale.

Contribution

It offers a broad historical and social analysis of AI evolution, emphasizing the importance of scale and cautioning against overly optimistic views.

Findings

AI emergence correlates with increased neural connections.

Human intelligence may be an emergent phenomenon of scale.

Challenges include toxicity, bias, and hallucinations in models.

Abstract

We provide a birds eye view of the rapid developments in AI and Deep Learning that has led to the path-breaking emergence of AI in Large Language Models. The aim of this study is to place all these developments in a pragmatic broader historical social perspective without any exaggerations while at the same time without any pessimism that created the AI winter in the 1970s to 1990s. We also at the same time point out toxicity, bias, memorization, sycophancy, logical inconsistencies, hallucinations that exist just as a warning to the overly optimistic. We note here that just as this emergence of AI seems to occur at a threshold point in the number of neural connections or weights, it has also been observed that human brain and especially the cortex region is nothing special or extraordinary but simply a case of scaled-up version of the primate brain and that even the human intelligence seems like an emergent phenomena of scale.