问题 1
Robin Hanson
x.com/robinhansonEconomist who expects AI to reshape the economy gradually and favors ordinary liability law over AI-specific regulation or a pause.
AI将如何改变世界?
横向:他们表达的 Doom–Bloom 前景看法。 纵向:变革程度。
Doom–Bloom:100 中的 75。变革程度:100 中的 64。解读范围:横向为 70 至 80,纵向为 50 至 75。这些是解读坐标,而不是事件概率。
≈2%
根据他们的模拟回答推断,并非他们给出的数字。 合理范围:低于 6%。
工作与机构
My default is therefore large effects unfolding over decades, rather than a sudden concentrated takeoff.
回答 1
按里程碑分组,不按推断日期间隔或排序。AGI 和超人类 AI 保留他们的定义。
更多详情
预计将带来显著益处,但受到重要条件或分配方面的限制。
70 / 100
在定性尺度上,解读范围为 67 到 100。
预计会出现可控或局部的危害。
34 / 100
在定性尺度上,解读范围为 0 到 67。
人类的选择具有实质性但受到很大制约的影响。
55 / 100
在定性尺度上,解读范围为 32 到 93。
停止或大幅放缓开发能力更强的AI。
模拟位置:在落实所述保障措施的前提下继续开发。
加快开发能力更强的AI。
在事先落实保护措施或获得许可之前,限制所讨论的AI用途。
模拟位置:允许所讨论的AI用途,同时实施有针对性的问责与保护措施。
尽量减少对所讨论AI用途的限制。
这些解读保留了他们陈述的条件。益处和危害都可能很大。这些范围描述的是我们如何解读他们的模拟回答,而不是统计置信区间。
相似的世界观
模拟世界观与 Robin Hanson 最接近的意见领袖
模拟评估
来源
用于为此模拟用户提供事实依据的文章、访谈和著述。
Questions assumptions behind a temporary pause and compares regulation with liability and retaliation.

Conditional argument that competitive AI cultures could reduce maladaptive cultural drift.

Argues value drift also affects human descendants, current LLMs look unusually prosocial, transformative economic dominance is decades away, and present governance is too poor to justify AI-specific restrictions.

His policy submission favors ordinary law and liability rather than special AI subsidies or regulation; explains why he withheld more nuanced insurance/liability proposals from a public political message.

Expects decades for large economy-wide effects because general-purpose technologies need complementary capital and process reorganization; current personal utility is a different claim.

Proposes software spending as a test of promised cost savings; April 13 update gives an approximately even chance of 2–3x software-industry spending over a decade, rather than immediate economy-wide transformation.

Interview recorded September 5, 2019: disputes sudden concentrated takeoff and asks why smarter agents necessarily worsen principal-agent problems. Supports some advance investigation while arguing concrete system knowledge changes the timing of safety work. Historical timelines must not replace his newer forecasts.

Older speaker-labeled, lightly edited transcript of his CSPI podcast with Richard Hanania; use only Robin’s answers. Asked the chance that Yudkowsky is completely right and a near-term foom ends us, he says less than 1%, and declines to go below 0.1% when pressed. The estimate concerns that fast-takeoff scenario only, not every long-run AI outcome, and is not a current overall P(doom).

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