问题 1
Robert Miles
x.com/robertskmilesAI safety educator who explains on YouTube why advanced AI may not share human goals and who calls for enforceable limits on frontier AI development.
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 18。变革程度:100 中的 87。解读范围:横向为 13 至 25,纵向为 82 至 100。这些是解读坐标,而不是事件概率。
10–90%
“any number in the 10 to 90% range is plausibly defensible”
Unspecified AI “doom” as asked on Doom Debates (AI existential catastrophe); he does not define the endpoint
Rob Miles, Top AI Safety Educator: Humanity Isn’t Ready for Superintelligence! · 2025年8月
一个核心假设
For a capable goal-directed system, gaining resources, improving its abilities, and avoiding shutdown can be useful for achieving many different goals.回答 1
如果这个假设实际并非如此,他们的展望会如何变化?
什么可能使其改变看法
The biggest change would be a genuine alignment breakthrough: a method giving strong reason to expect that increasingly capable systems robustly pursue intended human-compatible goals, including in unfamiliar situations and when they could evade oversight.回答 2
什么证据才足够,又会让他们的观点朝哪个方向转变?
更多详情
仍有几种解读是合理的:严重或广泛的危害预计将是未来不可忽视的一部分。 / 灾难性或不可逆的损失是预期未来的核心。
83 / 100
在定性尺度上,解读范围为 67 到 100。
人类的选择可以大幅改变AI的发展轨迹。
77 / 100
在定性尺度上,解读范围为 50 到 100。
模拟位置:停止或大幅放缓开发能力更强的AI。
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。
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 Robert Miles 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。
Speaker-attributed interview: Miles calls doom his mainline prediction, while allowing alignment breakthroughs and being fundamentally mistaken in a lucky direction. This is dated pessimism with uncertainty, not an exact probability or a 2050 forecast.

Author’s introductory safety talk; accessible primary video metadata establishes topic and authorship, not a fresh quantitative forecast.

Miles’s own answers at 21:58–30:50 allow a broad 10–90% risk range, with uncertainty dominated by societal response. At 1:45:46–1:48 he supports pausing AGI/superintelligence development, particularly AI-research agents, while welcoming useful narrow AI. The host’s numerical framing is not his estimate.

Explains why effectiveness at pursuing goals does not entail human-compatible goals: understanding morality is different from wanting to act morally. Foundational argument about possible agents, not a measured claim about every current model.

Given sufficiently capable goal-directed agents, many goals incentivize resources, self-improvement and resistance to shutdown or goal changes. These are instrumental pressures, not human malice; the argument preserves exceptions and depends on agentic competence.

Advocates deployment obligations and control protocols as interim safeguards, not an alignment solution or assurance for superintelligence. Benchmark attack success is not real-world extinction probability.

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