Large language models are celebrated for their ability to process information quickly and generate human-like responses. However, they…
Is AI on the Spectrum? Exploring the Parallels Between Artificial Intelligence and Autism Spectrum Disorder
An in-depth analysis of the similarities between AI behaviors and traits observed in Autism Spectrum Disorder, and how AI is being utilized in autism research and support.
Avoiding Ableist Language: Suggestions for Autism Researchers - Kristen Bottema-Beutel, Steven K. Kapp, Jessica Nina Lester, Noah J. Sasson, Brittany N. Hand, 2021
In this commentary, we describe how language used to communicate about autism within much of autism research can reflect and perpetuate ableist ideologies (i.e....
AI learns language from skewed sources. That could change how we humans speak – and think | Bruce Schneier
Large language models aren’t trained on real-life conversations. As we encounter their language, it could affect our own
Is AI on the Spectrum?
Some key traits of generative AI bear a striking similarity to autism spectrum disorder. Why does this matter?
A Rational Analysis of the Effects of Sycophantic AI
People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We...
Evaluation of Large Language Model Chatbot Responses to Psychotic Prompts
This cross-sectional study tests whether a large language model chatbot product can reliably generate appropriate responses to psychotic content.
Evaluation of Large Language Model Chatbot Responses to Psychotic Prompts
This cross-sectional study tests whether a large language model chatbot product can reliably generate appropriate responses to psychotic content.
Welcome: Mapping the Architecture of Autistic Cognition
Most people experience their minds as opaque.
The Philosophy of Language Models
ABSTRACT The success of large language models (LLMs) across many domains of AI research has generated intense debate. Some attribute their impressive performance on complex tasks to human‐like linguistic and cognitive capacities, whereas others ascribe it to shallow pattern matching. These disputes stem from deep‐seated philosophical disagreements about the nature of language and cognition. We provide an opinionated survey of these disagreements across core topics in the philosophy of mind and language, including syntactic competence, compositionality, linguistic meaning, representation, attitudes, reasoning, agency, and consciousness. We contend that progress on these issues requires not only clarity about background philosophical commitments but also, in many cases, close engagement with emerging empirical evidence.
"AI Psychosis" in Context: How Conversation History Shapes...
Extended interaction with large language models (LLMs) has been linked to the reinforcement of delusional beliefs, attracting clinical and public concern. Yet most empirical work evaluates model...
How Large Language Models Actually Work
Epistemological Fault Lines Between Human and Artificial Intelligence
Large language models (LLMs) are widely described as artificial intelligence, yet their epistemic profile diverges sharply from human cognition. Here we show that the apparent alignment between...
Two Realities: Native Modes, Learned Social Systems, and the Foundations of Autistic Cognition
Part 1: The Architecture Overview
Beyond Deficits:
The FCLA-SFGA Model and a Paradigm Shift in Autism Understanding
