| I made the claim that problem simplification through an abstraction that has error can reduce computational complexity leading to improved solution quality. You responded by talking about people who are talking about Lagrange points. I found that extremely insulting. I still find you to be extremely insulting. I don't think my claims are so hard to understand. I find your intentional misinterpretation of my points annoying. I despise that you lied about whether we were talking about learning and abstraction. I don't like talking with people who blatantly lie. I consider lying bad. I also dislike that you misquoted me. You put quotes around words I didn't say. I didn't do that to you. You say that I did. You lie when you say that. I gave you my interpretation of what I felt you were claiming. I even explained why I felt you claimed that. This wasn't under the quote symbol. Yours was inside quotes. Yours was a lie. Mine had your original quote, unaltered, with my interpretation below it. I was in error. I admit that. I was trying to get at the heart of my point - that erroneous abstractions aren't inherently bad. Outcome error is much more important than input error. I don't agree that you've taught me anything - you just try to call me incoherent because what I'm saying is true but you employ motivated reasoning to avoid having to refute it. If you actually understood what I'm saying - which obviously you don't, which is a big part of the problem here, you would agree with me. Or at least, I think you would. That simple problems are easier to solve and sometimes an actual solution is better than no solution really isn't that complicated a thing. Or controversial. I'm sure plenty of people understand it. There are so many times in life where my point holds. The use of floating point is one. Perhaps you didn't notice that ML engineers frequently choose to move from float64 to float32 to float16 to float8? Perhaps you didn't notice that services all throughout the computing industry choose to meet an SLA, minimizing latency sometimes at the cost of optimal solutions whose computation isn't realistic given their computing budget. I don't know. But you're definitely not actually teaching me anything. Your just not understanding me. So this conversation is pointless. I'm still just as convinced of the truth of the idea that it can be very wise to accept a bad abstraction, one that has error, rather than a perfect abstraction. I can't even fathom how to go about the opposite. How would a child go from knowing nothing to knowing everything perfectly without moving through areas of bad abstraction along the way? I feel you are mean. I'd rather we stop talking about this together if we're not going to actually engage with each other on the topic under discussion. |