问题 1
Jeremy Howard
x.com/jeremyphowardAI researcher and educator behind fast.ai who works to make AI accessible to more people and argues that openness protects against concentrated power.
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 71。变革程度:100 中的 51。解读范围:横向为 66 至 76,纵向为 35 至 90。这些是解读坐标,而不是事件概率。
≈4%
根据他们的模拟回答推断,并非他们给出的数字。 合理范围:2–8%。
一个核心假设
I think AI can be genuinely transformative, but its social impact will depend less on abstract capability forecasts than on who can understand, build, and control it.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
So my expectation is conditional rather than a numerical forecast: AI has enormous positive potential, but its actual impact will depend on openness, education, democratic participation, and practical safeguards against misuse and concentrated power.回答 2
什么能帮助他们区分这里各种合理的结果?
什么可能使其改变看法
The biggest change would be compelling evidence that broadly accessible AI itself creates severe harms that cannot be mitigated through education, transparency, technical safeguards, or democratic governance.回答 3
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
预计将带来显著益处,但受到重要条件或分配方面的限制。
69 / 100
在定性尺度上,解读范围为 67 到 67。
预计会出现可控或局部的危害。
39 / 100
在定性尺度上,解读范围为 33 到 67。
人类的选择可以大幅改变AI的发展轨迹。
76 / 100
在定性尺度上,解读范围为 49 到 100。
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。
限制对强大AI的访问。
允许访问,但须遵守能力或用途限制。
模拟位置:支持广泛或开放地访问强大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 Jeremy Howard 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。
Author argues licensing and surveillance can concentrate power and calls for openness and consultation. Page displays July 10 despite the different date in its URL.

Coauthored library paper documents reducing practical barriers to deep learning.

Coauthored argument that misuse, inequality and concentrated power are more urgent than speculative rogue-AI extinction; favors concrete precaution and democratic participation rather than allowing industry to define the agenda.

Advocates incremental human–AI dialog and understanding over generating large programs blindly; explains democratization as both empowering users and resisting elite concentration.

Howard’s own introduction and remarks warn that excessive agent delegation erodes understanding and craftsmanship; he favors AI-assisted software mastery rather than measuring productivity by generated lines.

Explains applied R&D that iteratively converts AI breakthroughs into useful affordable products, with development needs shaping research and long-term social benefit.

你的立场在哪里?
描绘我的世界观