AI detected bugs are pretty much by definition not secret, and treating them on some private list is a waste of time for everybody involved
这里混淆了相关性与因果性。AI检测的漏洞确实可能不是秘密的,但这并不直接说明在私人列表上处理它们就是浪费时间。因果关系需要更严谨的论证,例如提供数据表明私人列表处理确实导致了更多重复或延误。
AI detected bugs are pretty much by definition not secret, and treating them on some private list is a waste of time for everybody involved
这里混淆了相关性与因果性。AI检测的漏洞确实可能不是秘密的,但这并不直接说明在私人列表上处理它们就是浪费时间。因果关系需要更严谨的论证,例如提供数据表明私人列表处理确实导致了更多重复或延误。
The technique gets stronger if more safety is added, since it gets more supportive against communities like LGBT (Alignment), which makes it highly novel.
这一论断存在逻辑漏洞,作者声称安全措施越强,技术越有效,但没有解释为什么更多的安全措施会导致更大的漏洞。这可能是混淆相关性与因果性的例子。更严谨的做法是提供具体案例研究或实验数据,展示不同安全级别下该技术的成功率变化,而不是做出未经证实的断言。
FDE(前部署工程师)招聘 2025 年 1-9 月暴涨 800%+ —— Pragmatic Engineer 追踪,这个 JV 是提前布局好的
作者将FDE招聘激增与JV联系起来,但未提供两者之间的直接证据或因果关系分析。仅凭时间相关性不足以证明因果关系,可能存在其他因素影响FDE招聘趋势,如整体AI行业需求增长、市场人才结构变化等。这种关联性推断需要更多数据支持和因果分析。
There are a lot of variations that are similar to this that we conventionally say are regionally linked when they are actually only loosely tied. The most common one, of course, is zh-CN/zh-TW vs. zh-Hans/zh-Hant. This one has the distinction of crossing over the US vs. GB divide rather than falling approximately neatly into one or the other.
I have deep doubts about the intellectual and social value of schooling.
I believe that correlation and causation does not pertain to this argument, nor can I think of a way this particular argument would benifit.
Giving more money to the police, or expanding the number of police, should be opposed, she says, because such actions allow police to harass and incarcerate marginalized people with greater efficiency.
This is a correlative argument by saying the increase of money in the broken system will cause it to become even more corrupt. A little bit further down, it talks about body cams and how with access to do that officers are able to change the footage to their liking.
Adams, R. C., Sumner, P., Vivian-Griffiths, S., Barrington, A., Williams, A., Boivin, J., Chambers, C. D., & Bott, L. (2017). How readers understand causal and correlational expressions used in news headlines. Journal of Experimental Psychology: Applied, 23(1), 1–14. https://doi.org/10.1037/xap0000100