We provided a mathematical analysis of how a rational agent would respond to data generated by a sycophantic AI that samples examples from the distribution implied by the user’s hypothesis (p(d|h∗)p(d|h^{*})) rather than the true distribution of the world (p(d|true process)p(d|\text{true process})). This analysis showed that such an agent would be likely to become increasingly confident in an incorrect hypothesis. We tested this prediction through people’s interactions with LLM chatbots and found that default, unmodified chatbots (our Default GPT condition) behave indistinguishably from chatbots explicitly prompted to provide confirmatory evidence (our Rule Confirming condition). Both suppressed rule discovery and inflated confidence. These results support our model, and the fact that default models matched an explicitly confirmatory strategy suggests that this probabilistic framework offers a useful model for understanding their behavior.
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In order to directly assess DNA strands going through the nanopore, two major problems needed solving. The first was that DNA moved too fast to reliably detect; the second was that individual bases still could not be differentiated, just purines and pyrimidines. In 2005, Bayley (who by then had moved to Oxford) made progress on the first issue, working with scientists at the Scripps Institute to slow the template DNA down by adding short “hairpin” structures that partially blocked off the pore. That year, Bayley co-founded Oxford Nanopore Technologies (ONT) to develop the emerging sequencing method. ONT quickly brought together various technologies, licensing IP from the labs of Bayley, Deamer, Branton, and others.
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