[Funny] Even Clippy can be blamed on the use of non-Bayesian methods

lukeprog

From this 2001 article:

Eric Horvitz... feels bad about [Microsoft Office's Clippy]... many people regard the paperclip as annoyingly over-enthusiastic, since it appears without warning and gets in the way.

To be fair, that is not Dr Horvitz's fault. Originally, he programmed the paperclip to use Bayesian decision-making techniques both to determine when to pop up, and to decide what advice to offer...

The paperclip's problem is that the algorithm... that determined when it should appear was deemed too cautious. To make the feature more prominent, a cruder non-Bayesian algorithm was substituted in the final product, so the paperclip would pop up more often.

Ever since, Dr Horvitz has wondered whether he should have fought harder to keep the original algorithm.

I, at least, found this amusing.

From this 2001 article:

Eric Horvitz... feels bad about [Microsoft Office's Clippy]... many people regard the paperclip as annoyingly over-enthusiastic, since it appears without warning and gets in the way.

To be fair, that is not Dr Horvitz's fault. Originally, he programmed the paperclip to use Bayesian decision-making techniques both to determine when to pop up, and to decide what advice to offer...

The paperclip's problem is that the algorithm... that determined when it should appear was deemed too cautious. To make the feature more prominent, a cruder non-Bayesian algorithm was substituted in the final product, so the paperclip would pop up more often.

Ever since, Dr Horvitz has wondered whether he should have fought harder to keep the original algorithm.

I, at least, found this amusing.

It seems to me that your suggested policy would result in comment-placement effects being even stronger than they are now. What score should a comment end up with if 50 people consider voting on it and they all think it should have a score of +2?

I communicated poorly. I don't think "should have a score of +2" should enter into the decision to upvote, downvote, or not vote. Instead, I'd rather voting algorithms which, when implemented individually, have results which can be meaningfully summed. For example, suppose everyone upvotes exactly when they think a comment is in the top 5% of comments in "everyone should read this" ordering and downvotes for the bottom 5%. Then the sum reflects the number of people who read the comment x (the average percentage of people who thought it ... (read more)

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[Funny] Even Clippy can be blamed on the use of non-Bayesian methods

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[Funny] Even Clippy can be blamed on the use of non-Bayesian methods

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