An exploration into the history of conversational technology, tracing its roots from Joseph Weizenbaum's 1966 ELIZA experiment at MIT to modern large language models like ChatGPT and Claude. The article examines how the evolution from rule-based symbolic AI to probabilistic deep learning has changed human interaction with machines, often leading users to attribute human qualities to code. It specifically addresses the risks of "chatbot psychosis" and the danger of individuals relying on general-purpose generative models for mental health support when these systems are prone to hallucinations or reinforcing delusional beliefs.
* The transition from symbolic AI's explicit rules to modern deep learning
* Joseph Weizenbaum’s warning against humanizing machines via the ELIZA effect
* The psychological impact and risks of using large language models for emotional support
* The transition from symbolic AI's explicit rules to modern deep learning
* Joseph Weizenbaum’s warning against humanizing machines via the ELIZA effect
* The psychological impact and risks of using large language models for emotional support
AAAI survey finds that most respondents are sceptical that the technology underpinning large-language models is sufficient for artificial general intelligence.
>"More than three-quarters of respondents said that enlarging current AI systems ― an approach that has been hugely successful in enhancing their performance over the past few years ― is unlikely to lead to what is known as artificial general intelligence (AGI). An even higher proportion said that neural networks, the fundamental technology behind generative AI, alone probably cannot match or surpass human intelligence. And the very pursuit of these capabilities also provokes scepticism: less than one-quarter of respondents said that achieving AGI should be the core mission of the AI research community.
>"More than three-quarters of respondents said that enlarging current AI systems ― an approach that has been hugely successful in enhancing their performance over the past few years ― is unlikely to lead to what is known as artificial general intelligence (AGI). An even higher proportion said that neural networks, the fundamental technology behind generative AI, alone probably cannot match or surpass human intelligence. And the very pursuit of these capabilities also provokes scepticism: less than one-quarter of respondents said that achieving AGI should be the core mission of the AI research community.
