Sunday, December 4, 2016

The "final" meaning

I puzzle over whether a filled narrative is the final form of information or whether that can be transformed, one last time, into a better, static data structure. Mostly, I conclude the efficient static structure is the filled narrative itself together with ifound[] information about what words were 'read' in the text.

Monday, November 28, 2016

Narwhal reaches "Design Complete" stage

This means I have done all the basic typing I expect to do and now have to debug. In principle, the tough ideas and design choices are behind us, and Narwhal is at "Alpha". Of course I fear the debugging still ahead but, as I wrote in my GitHub commit, the NWObject works in at least one case. That is the Narwhal object.
As far as that goes, here is an encouraging firsts impression:
Of course a moment later it falls on its face - ah well.

Friday, November 18, 2016

Slicing the "search" problem differently

Suppose that Google focused on algorithms that were entirely personal and connected with a user's profile, while at the same time it built a neutral backend for its indexed data. I suppose the problem is that you cannot index without making assumptions. Anyway, if the profile became the thing, then you could always step out of it and do neutral exact searching if desired.

Wednesday, November 16, 2016

"End of Life" for a neural net

Because they are black boxes giving no window into the multi dimensional measurement space where "clusters" are forming during machine learning, you will never see just how non-convex the classification regions are - how topologically different they are from the original regions in object space. Un-justified assumptions of convexity and a blind belief in the applicability of a random Euclidean metric have created a situation where, inevitably, a sample will get added from "category A" that is closer the known examples from "category B". From there, it is only a matter of time when the two categories start merging and the system deliver more and more incorrect classifications.
It seems to me this is almost inevitable.
At first a neural net system seems great. A small number of examples have been added to the system and they are far enough apart to work as nearest-neighbor classifiers. But then we start adding other examples for greater "accuracy". From personal experience, two things are happening now. Counter examples are starting to show up and so they are added as new training examples. Also the developer is beginning to be hypnotized (PRADHS - "pattern recognition algorithm developers hypnosis syndrome") into believing objects belong in the category, if their system tells them that is where the object belongs. This leads to the addition of more and more boundary case examples. Rather than becoming more accurate, the system has actually become useless and incapable of delivering accuracy greater than a not-very-good level like 65%. That is machine learning "end of life".

I believe that Google may have reached end of life in its search algorithm. You can always find straw in a haystack but I am afraid that you can no longer find a needle there. As far as I am concerned, when I search for "Barbara Waksman" and they return pages with both words but not all pages where the words are adjacent, then what I am seeing is a whole lot of false positives. The world seems much too accepting of these Google errors. When Netflix makes the same error it is SO bad that I can utilize their bogus search results as a backdoor search for random movie titles that are not available otherwise - a different Netflix error. 

Tuesday, November 15, 2016

current GOF formula

(min(u,r)/N) * (r/F)
Where:
N = num slots in narrative
u = num slots used
F = num all words between first and last word read
r = number of words read

Update: Skip the min(u,r) but DO use the total number T of words, so the latest version of the formula is:
(u/N)*(r/F)*(r/T)
This makes a single word match less good than a multi word match; and it favors longer narrative patterns.

Monday, November 14, 2016

Artificial Intelligence, embedded intelligence, and real intelligence in a computer

Count me as an AI skeptic. When I hear about the program that beat a Go master, I think: there is no way that program arose by machine learning or any "AI" technique. Instead, the programmers knew how to play the game well, and embedded their intelligence into a computer. Given sufficiently good Go playing programmers, getting a machine to do what they do, but with the overview of seeing many steps ahead, it is not surprising the program beats the master. All you need is near-master level programmers. This was real intelligence that they embedded into a computer game. But calling it an "AI success" is claiming a mantle of success that had nothing to do with so called "AI".

On the other hand, I propose that a computer that brings me a Coke when I say "bring me a Coke", is exactly as intelligent as needed. It satisfies my requirement, and so it is real intelligence.

As for artificial intelligence, I guess it deserves its own title. Anyone know examples that are 85% accurate?
Update: I see "embedded intelligence" is already taken, how about  "embedded cognition"? There's a buzzword for ya. OOPS! That's taken too. Can I go with "machine cognition"? Nope. All combinations of these words have been taken. So I might as well stick with "embedded intelligence".