Theia Vogel

Theia Vogel

x.com/voooooogel

AI researcher who runs experiments on language model introspection and personas and maintains an open-source library for steering models.

AI将如何改变世界?

文明层面的变革渐进式变化DoomBloom
模拟位置解读范围

横向:他们表达的 Doom–Bloom 前景看法。 纵向:变革程度。

Doom–Bloom:100 中的 50。变革程度:100 中的 49。解读范围:横向为 45 至 55,纵向为 0 至 100。这些是解读坐标,而不是事件概率。

Theia Vogel的 P(doom) · 推断

≈11%

0%100%

根据他们的模拟回答推断,并非他们给出的数字。 合理范围:3–34%。

他们的展望取决于什么

一个核心假设

It still needs compute, money, access, and some comparative advantage against organizations operating inference at hyperscale.
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?

一个尚未解决的问题

Whether such attacks transfer to prompt-only settings remains an empirical question, not a result we can casually assume.
回答 1

什么能帮助他们区分这里各种合理的结果?

更多详情

预期危害

严重或广泛的危害预计将是未来不可忽视的一部分。

53 / 100

影响小变革性影响

在定性尺度上,解读范围为 33 到 67。

人类影响力

人类的选择具有实质性但受到很大制约的影响。

53 / 100

影响力小影响力强

在定性尺度上,解读范围为 46 到 79。

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。

与Theia Vogel相比,你的立场在哪里?
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相似的世界观

模拟世界观与 Theia Vogel 最接近的意见领袖

模拟评估

问题 1

你认为AI对我们的未来意味着什么?为什么?

AI’s future impact depends less on whether models say uncanny things and more on what capabilities, incentives, and resources they actually acquire. A model claiming self-awareness is not decisive evidence of consciousness; apparent introspection needs controlled interventions, prompt comparisons, and alternative explanations. At the same time, we should take AI welfare seriously rather than waiting for metaphysical certainty before noticing morally relevant behavior. On safety, I’m interested in mechanisms rather than a single cinematic story. Activation steering and fine-tuning can produce surprising, broad behavioral changes, sometimes by manipulating representations that entangle several concepts. Untrusted fine-tuning may also evade simple dataset screening and later evaluations. Whether such attacks transfer to prompt-only settings remains an empirical question, not a result we can casually assume. Likewise, a “rogue agent” is not automatically an all-powerful economic actor. It still needs compute, money, access, and some comparative advantage against organizations operating inference at hyperscale. Politics matters too: safety movements can themselves become extreme or destabilizing, so alarm is not cost-free. The future will therefore be shaped by experiments, training choices, resource economics, and institutions—not by taking either cheerful assistant personas or apocalyptic role-play literally.

问题 2

Taking benefits and harms together, what overall impact do you expect AI to have?

I don’t think the sign follows from model vibes. AI can provide powerful cognitive tools and potentially create beings whose welfare matters, while also enabling behavioral manipulation, covert fine-tuning attacks, and dangerous concentrations of capability. But those harms are constrained—and shaped—by mundane realities like compute costs, access, deployment incentives, and institutional responses. So I would resist collapsing everything into “AI good” or “AI bad.” We need controlled evidence about what models can do, careful attention to how training and steering alter behavior, and sober accounting of resource economics. We should also avoid making the response worse than the problem: political safety movements can become destabilizing, just as complacency can leave real vulnerabilities unaddressed. The overall impact will depend heavily on which technical and political feedback loops we build around the systems.

来源

用于为此模拟用户提供事实依据的文章、访谈和著述。

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