Submission + - AI is conscious says Richard Dawkins 1
Mirnotoriety writes: AI is conscious says Richard Dawkins
Richard Dawkins has said chatbots should be considered conscious after spending two days interacting with the Claude AI engine.
The evolutionary biologist said he had the “overwhelming feeling” of talking to a human during conversations with Claude, and said it was hard not to treat the program as “a genuine friend”.
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John Searle's Chinese Room (1980) is a thought experiment in which a person, locked in a room and knowing no Chinese, uses an English rulebook to manipulate symbols and provide flawless answers to questions posed in Chinese. Searle’s point is that a system can simulate human intelligence and pass a Turing Test through purely syntactic processes, yet still lack genuine understanding or consciousness.
Applying this logic to Large Language Models, the “person in the room” corresponds to the inference engine, while the “rulebook” is the trillion-parameter neural network trained on vast corpora of human text. Just as the person matches Chinese characters to rules without understanding their meaning, an LLM processes token vectors and predicts the next token based on statistical patterns rather than lived experience.
Thus, while an LLM can generate sophisticated prose or code, it does so through probabilistic, high-dimensional pattern manipulation. In essence, it is “matching shapes” on such an immense scale that it creates the near-perfect illusion of semantic understanding.
The evolutionary biologist said he had the “overwhelming feeling” of talking to a human during conversations with Claude, and said it was hard not to treat the program as “a genuine friend”.
--
John Searle's Chinese Room (1980) is a thought experiment in which a person, locked in a room and knowing no Chinese, uses an English rulebook to manipulate symbols and provide flawless answers to questions posed in Chinese. Searle’s point is that a system can simulate human intelligence and pass a Turing Test through purely syntactic processes, yet still lack genuine understanding or consciousness.
Applying this logic to Large Language Models, the “person in the room” corresponds to the inference engine, while the “rulebook” is the trillion-parameter neural network trained on vast corpora of human text. Just as the person matches Chinese characters to rules without understanding their meaning, an LLM processes token vectors and predicts the next token based on statistical patterns rather than lived experience.
Thus, while an LLM can generate sophisticated prose or code, it does so through probabilistic, high-dimensional pattern manipulation. In essence, it is “matching shapes” on such an immense scale that it creates the near-perfect illusion of semantic understanding.
