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mikelitoris

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I love(!) how these dev!kits are for devs in silicon valley making 300k+ a year and not any other dev in any other part of the world.

Satire if you can’t tell…

“Human driven cars are going to be around for about 5 years” is one of the most out of touch quotes I’ve seen this month.

Also, all of this is about NIMBYism, which is about house prices being inflated to make up most of households’ wealth, which is not fixable without triggering a massive wealth redistribution. Most western societies are stuck in a very non-ideal Nash equilibrium and it is not going to be solved any time soon.

It’s a smokescreen people use to claim it’s not racist. It reminds me of that south park episode with the cable company representatives with velcro pockets. “Oh you want to migrate here legally? Oh it will take 3 years and it requires an active employment offer at application time and on arrival? Oh no… tell me more”

I guess Taiwanese lawmakers deemed this law necessary for national security purposes, but this would not fly elsewhere. We already have something to protect trade secrets: it’s called a patent. If you don’t patent it, and a competitor lures an employee with enough knowledge, everything is fair game, as it should be. Aka if you like it you should put a patent on it.

Edit: why would you downvote this? Jeez…

I’m in the skeptical camp. Whatever theory that will eventually emerge will not be as solid as: 1. Theory of pattern recognition (as developed in 80s and 90s) 2. Theory of thermodynamics 3. Theory of gravity 4. Theory of electromagnetism 5. Theory of relativity Etc. because of two reasons: 1. While half of deep learning is how humans construct the architecture of networks, the more important half relies on data. This data is a hodgepodge of scraped internet data (text and videos), books, user interactions etc., which really has no coherent structure 2. To extract meaningful insights from this much data, it takes models of enormous size like 10B+. The thing about random systems (in the mathematical sense) is that it takes “something” of order of magnitude bigger size to “understand” it, unless there is some concentration of measure type mathematical niceties (as in thermodynamics), which I don’t think is there in these models and data. This is the same reason I don’t think humans will ever be able to “understand” human consciousness. It will take something of an order of magnitude bigger than our own brains to do that. Here is Terence Tao explaining this concentration stuff in another context: https://mathstodon.xyz/@tao/113873092369347147 I would love to be proven wrong though.