Can AI and Humans Genuinely Communicate?

Основные понятия

AI's ability to genuinely communicate with humans is assessed through a mental-behavioral methodology, despite challenges posed by black-box algorithms.

Аннотация

The content explores the question of whether AI and humans can genuinely communicate. It introduces the mental-behavioral methodology as a way to assess this ability without needing to understand black-box algorithms. The article discusses ELIZA, a computer program designed for conversation in the 1960s, highlighting its limitations in genuine communication. It delves into the complexities of AI communication abilities, focusing on recent advancements like Large Language Models (LLMs). The importance of understanding human-AI communication for theoretical, societal, and ethical reasons is emphasized. Various methodologies are compared, including the algorithm analysis methodology and the mental-behavioral methodology. The challenges faced by the MBM in assessing AI's communicative abilities are discussed, along with potential solutions and future research directions.

Статистика

ELIZA appeared to many users as being able to understand them.
ELIZA couldn't genuinely communicate or understand its users.
ELIZA mimics the role of a Rogerian psychotherapist.
Recent AIs are based on black-box neural networks.

Цитаты

"People interacting with an AI may easily think that it can genuinely communicate with them." - Weizenbaum (1976)
"Pragmatic understanding has not yet arisen from large-scale pre-training on its own." - Ruis et al. (2023)

Ключевые выводы из

Can AI and humans genuinely communicate?

by Constant Bon... в arxiv.org 03-26-2024

https://arxiv.org/pdf/2402.09494.pdf

Дополнительные вопросы

How does the mental-behavioral methodology address challenges posed by black-box algorithms?

The mental-behavioral methodology (MBM) addresses challenges posed by black-box algorithms by focusing on observable behaviors rather than delving into the intricate workings of these algorithms. Unlike traditional algorithm analysis methodologies that struggle with understanding complex deep learning networks, the MBM bypasses this issue by emphasizing behavioral tests to assess cognitive capacities. By formulating experimental paradigms based on theories of communication and human cognition, the MBM can evaluate whether an AI displays relevant behaviors indicative of genuine communication abilities without needing a comprehensive understanding of the algorithm's internal mechanisms.

What implications does anthropomorphism have on human-AI communication?

Anthropomorphism can significantly impact human-AI communication as it may lead individuals to attribute human-like qualities, emotions, or intentions to AI systems. This tendency to anthropomorphize AI can create unrealistic expectations about their communicative abilities and capabilities. It may also blur the lines between genuine understanding and simulated responses from AI programs like chatbots or virtual assistants. As highlighted in the context provided, users interacting with AI might mistakenly believe that these systems truly comprehend them when, in reality, they are following pre-programmed patterns or algorithms designed to mimic conversation.

How can brain behaviors be used to compare AI and human communicative abilities?

Brain behaviors offer a unique perspective for comparing AI and human communicative abilities as they provide insights into how words are represented neurologically during language processing tasks. Studies like Li et al.'s comparison of LLM embeddings with brain representations through fMRI scans demonstrate structural similarities between how humans process words mentally and how LLMs encode word meanings in vector spaces. By analyzing neural responses alongside behavioral performance data from both humans and AIs during linguistic tasks or comprehension exercises, researchers can gain a deeper understanding of cognitive processes underlying communication in both entities.