Secondary posts. But the AI-FOOM-debate is not far off.
537. News post
Nice Anti-rationalization advice:
A recent conversation reminded me of this simple, important, and difficult method:
When someone asks you “Why are you doing X?”,
And you don’t remember an answer previously in mind,
Do not ask yourself “Why am I doing X?”.
For example, if someone asks you
“Why are you using a QWERTY keyboard?” or “Why haven’t you invested in stocks?”
and you don’t remember already considering this exact question and deciding it,
do not ask yourself “Why am I using a QWERTY keyboard?” or “Why aren’t I invested in stocks?”
“Should I do X, or not?”
Should I use a QWERTY keyboard, or not? Should I invest in stocks, or not?
When you finish considering this question, print out a traceback of the arguments that you yourself considered in order to arrive at your decision, whether that decision is to X, or not X. Those are your only real reasons, nor is it possible to arrive at a real reason in any other way.
If you see something that probably didn’t evolve by chance, like e.g. a complicated machine, it’s probably the product of some optimization process. And if this optimization process didn’t need a lot of time and resources to do so, (contra e.g. evolution) it is probably intelligent.
Like thermodynamics, cognition is about flows of order. An ordered outcome needs negentropy to fuel it. Likewise, where we expect or recognize a thing, even so lofty and abstract as “intelligence”, we must have ordered beliefs to fuel our anticipation. It’s all part of the great game, Follow-the-Negentropy.
540. Lawful Creativity
Creativity has to follow some rules. Phenomena like the weather or white noise show that creativity isn’t made of pure chaos alone.
541. Lawful Uncertainty
Responding to unpredictable events with unpredictable behavior is often not a wise strategy (although there are many situations in which acting unpredictable is advantageous). See e.g. this passage from Robyn Dawes’ Rational Choice in an Uncertain World:
“Many psychological experiments were conducted in the late 1950s and early 1960s in which subjects were asked to predict the outcome of an event that had a random component but yet had base-rate predictability – for example, subjects were asked to predict whether the next card the experiment turned over would be red or blue in a context in which 70% of the cards were blue, but in which the sequence of red and blue cards was totally random.
In such a situation, the strategy that will yield the highest proportion of success is to predict the more common event. For example, if 70% of the cards are blue, then predicting blue on every trial yields a 70% success rate.
What subjects tended to do instead, however, was match probabilities – that is, predict the more probable event with the relative frequency with which it occurred. For example, subjects tended to predict 70% of the time that the blue card would occur and 30% of the time that the red card would occur. Such a strategy yields a 58% success rate, because the subjects are correct 70% of the time when the blue card occurs (which happens with probability .70) and 30% of the time when the red card occurs (which happens with probability .30); .70 * .70 + .30 * .30 = .58.
In fact, subjects predict the more frequent event with a slightly higher probability than that with which it occurs, but do not come close to predicting its occurrence 100% of the time, even when they are paid for the accuracy of their predictions… For example, subjects who were paid a nickel for each correct prediction over a thousand trials… predicted [the more common event] 76% of the time.”
You don’t fight fire with fire – any more than you fight irrationality with irrationality – nor fight randomness with randomness – nor fight chaos with chaos – nor fight unpredictability with unpredictability – nor survive in an uncertain universe with uncertain behaviors – nor unravel an environment of unknown laws with unknown laws of reasoning – nor analyze a changing world with a changing algebra of probability.
542. Worse Than Random
As a general principle, on any problem for which you know that a particular unrandomized algorithm is unusually stupid – so that a randomized algorithm seems wiser – you should be able to use the same knowledge to produce a superior derandomized algorithm.
…Still you should find, as a general principle, that randomness hath no power: there is no beauty in entropy, nor strength from noise.
Alright, alright; we get it! Randomness is bad.