Complexity At the Cost of Being Simple
Reductionism at @13:00 is completely annoying. Epiphenomenological aspects of the problem are completely missing when you reduce into pure binary! It’s like taking you and your emotional life (with its incipient impact on your immune system) and reducing it down to DNA!
“There are way more problems than there are solutions.” @17:00!Sure! When you peel away the contextual embedding of any problem (via reductionism), then you’ve just committed a sort of lobotomy!
The definition of NP at @23:00 while correct, reveals how misguided this theory is. Not all choices are guesses, and correct answers aren’t always ‘lucky’.
Check out the response one receives from the system (algorithm) at @25:11.Did you notice something’s wrong or what?
@26:51 Does anyone notice who is supplying the criterion for the value of ‘correct’? The algorithm is being falsely attributed with properties it can only be endowed with and not arrive at on its own!
@30:00 The rules to Tetris are known by both (algorithm and human) however, the proof of a truth value cannot be computationally arrived at in NP, yet the proof – via a human being AND the skills necessary to ‘prove’ anything can do it in P! It should be obvious that we are going about the whole thing in the wrong way by now!
@31:00 the P<>NP Problem is described. The problem is meaningless and yet you’ll get a Millenium Prize for solving it! (Even sane and not sane find themselves in the balance! Whoa!) If you continue listening to the justification, you might want to be near a bathroom.
@32:27 Check out how NP is being determined to be ‘more’ than P! “Nobody in their right mind…”, “Obviously insane…”,… so naturally NP must be more than P!
Sounds reasonable? I don’t think so…
@32:37 Watch the disappointment: “…very annoying…” and I wonder why? The question is meaningless! Other phrasings of the P<>NP Problem are nothing special and are completely obvious: “You can’t engineer luck.” (Excuse me, but isn’t that the definition of luck in the first place?) and “Solving problems is harder than checking them.”
@34:17 “What could we possibly say… this is all kind of weired…” I don’t know anymore either and I sure hope you don’t tell me! Are we at the end of the lecture already?
@35:53 Now we are getting to the ‘meat of the potato’. If we just “believe in… have faith in…” P<>NP, then Tetris is within NP-P! Wait a minute? That doesn’t sound like any proof to me… perhaps it’s an axiom? We’ll see. It sure looks like begging the question, but I want to be convinced so I’ll just have to wait.
@36:43 He then moves on to a ‘proof’ that looks more like a set of definitions! NP-hard and NP-complete are correctly defined, but they do not prove anything! Tetris and chess act like a definitions, as well!
@40:33 Now he wants to talk about reductions. Wait, weren’t we talking about them already? Let’s take a look…
Yes, we stand upon giants [Authoritarianism]@46:15(Karp’s 3-Partition) and don’t need to think about it anymore and just reconfirm that all NP-complete is reducible to each other! You find some problem that was defined by a “giant” to be a member of your classification and then show that yours is at least as hard @48:47.
If we happen to find a better solution to a member of NP-complete, then either the whole house of cards falls down or we simply reclassify (by reduction) it to P! Now believe it or believe what you want, okay?
There will be a time when we have to revisit mathematics and do a house cleaning of this ‘cuddle muddle’.
This entry was posted on November 2, 2014 by heurist. It was filed under Artificial Intelligence, Consciousness, Knowledge, Learning, Mathematics, Mathesis Universalis, Metaphysics, Psyence, Science and Technology Run Amok, Social Engineering and was tagged with ai, artifical intelligence, Artificial Intelligence, BadMathematics, BadPhilosophy, BadScience, Complexity, fraud, insight, knowledge, learning, NP-complete, Psyence, ScienceRunAmok, Tetris, understanding, wisdom.