I greatly regret writing this. Yud’s work is not easily distilled, it’s not written such that large amounts of distillation (~50%) adds value, unless the person doing it was extremely competent. Hypothetically, it’s very possible for a human to do, but empirically, everyone who has tried with this doc has failed (including me). For example, the clarifications/examples are necessary in order for the arguments to be properly cognitively operationalized; anything less is too vague. You could argue that the vast majority this post is just one big clarification.
Summarized versions of these arguments clearly belong in other papers, especially papers comprehensible for policymakers, tech executives, or ML researchers. But I’m now pessimistic about the prospects of creating a summarized version of this post.
I think one can write a variant of it that’s fairly shorter and is even better at conveying the underlying gears-level model. My ideal version of it would start by identifying the short number of “background” core points that inform a lot of the individual entries on this list, comprehensively outlining them, then showing how various specific failures/examples mentioned here happen downstream of these core points; with each downstream example viscerally shown as the nigh-inevitable consequence of the initial soundly-established assumptions.
But yeah, it’s a lot of work, and there are few people I’d trust to do it right.
I greatly regret writing this. Yud’s work is not easily distilled, it’s not written such that large amounts of distillation (~50%) adds value, unless the person doing it was extremely competent. Hypothetically, it’s very possible for a human to do, but empirically, everyone who has tried with this doc has failed (including me). For example, the clarifications/examples are necessary in order for the arguments to be properly cognitively operationalized; anything less is too vague. You could argue that the vast majority this post is just one big clarification.
Summarized versions of these arguments clearly belong in other papers, especially papers comprehensible for policymakers, tech executives, or ML researchers. But I’m now pessimistic about the prospects of creating a summarized version of this post.
I think one can write a variant of it that’s fairly shorter and is even better at conveying the underlying gears-level model. My ideal version of it would start by identifying the short number of “background” core points that inform a lot of the individual entries on this list, comprehensively outlining them, then showing how various specific failures/examples mentioned here happen downstream of these core points; with each downstream example viscerally shown as the nigh-inevitable consequence of the initial soundly-established assumptions.
But yeah, it’s a lot of work, and there are few people I’d trust to do it right.