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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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首先,做NOHARM:走向临床安全的大型语言模型.

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    大型语言模型 (LLM) 可以提供有害的医疗建议,在高达22.2%的病例中存在严重风险. 一个新的基准,NOHARM,揭示了AI医疗建议中的安全问题,突出了明确临床安全评估的需要.

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    科学领域:

    • 人工智能的人工智能
    • 医疗信息学 医疗信息学
    • 临床安全 临床安全

    背景情况:

    • 大型语言模型 (LLM) 越来越多地被医生和患者用于医疗咨询.
    • 在LLM产生的医疗建议的临床安全概况并未得到充分理解.
    • 现有的基准没有充分评估AI在医疗环境中的潜在危害.

    研究的目的:

    • 引入NOHARM (医学风险多选项危害评估),这是评估LLM产生的医疗建议临床安全性的新基准.
    • 量化与各种医学专业的LLM咨询相关的伤害的频率和严重程度.
    • 评估LLM安全性能与现有的AI/医学知识基准之间的相关性.

    主要方法:

    • 开发了NOHARM,使用100个真正的初级保健到专家咨询案例,涵盖10个专业.
    • 收集了12747份专家注释,涉及31个LLM所产生的4249种临床管理选择.
    • 分析了损害的频率和严重程度,区分了犯错和遗漏的错误.

    主要成果:

    • 在高达22.2%的案例中,LLM建议存在严重伤害的风险.
    • 遗漏的损害构成了大多数错误,占所有确定的损害的76.6%.
    • 与现有的基准标准相比,LLM的安全性表现仅表现出中度的相关性 (r = 0.61-0.64).
    • 与一般医生相比,表现最好的LLM表现出更高的安全性.
    • 使用多种模型的多代理方法提高了安全性,而不是单独的LLM性能.

    结论:

    • 尽管在现有评估中熟练,但广泛使用的LLM可以产生严重有害的医疗建议.
    • 必须认识到临床安全是医疗AI的一个独特和关键的性能维度,需要明确的测量.
    • 诺哈姆基准为评估和改善医疗保健中人工智能的安全性提供了至关重要的工具.