Comment by davrosthedalek 4 hours ago
I think it's a trick. It seems to be the article is just a series of ad-hoc assumptions and hypotheses without any support. The language aims to hide this, and makes you think about the language instead of its contents. Which is logically unsound: In a sharp peak, micro optimizations would give you a clearer signal where the optimum lies since the gradient is steeper.
I wouldn't jump to call it a trick, but I agree, the author sacrificed too much clarity in a try for efficiency.
The author set up an interesting analogy but failed to explore where it breaks down or how all the relationships work in the model.
My inference about the author's meaning was such: In a sharp peak, searching for useful moves is harder because you have fewer acceptable options as you approach the peak.
Fewer absolute or relative? If you scale down your search space... This only makes some kind of sense if your step size is fixed. While I agree with another poster that a reduction of a creative process to gradient descent is not wise, the article also misses the point what makes such a gradient descent hard -- it's not sharp peaks, it's the flat area around them -- and the presence of local minima.
I see your point. I'd meant relatively fewer progressive options compared to an absolute and unchanging number of total options.
But that's not what the author's analogy would imply.
Still, I think you're saying the author is deducing the creative process as a kind of gradient descent, whereas my reading was the author was trying to abductively explore an analogy.
> In a sharp peak, micro optimizations would give you a clearer signal where the optimum lies since the gradient is steeper.
I would refuse to even engage with the piece on this level, since it lends credibility to the idea that the creative process is even remotely related to or analogous to gradient descent.