Dictionary of life - Append Item (AI)

I was using Python dictionaries and thinking about how I implement those in assembly and also in hardware and considering the time of access, energy usage, and utility. I started considering the relationship between learning anything and that dictionary. Consider this: If I am to understand a subject my understanding(dictionary) must contain that concept so that it can be combined to create a new concept. For instance I could say that the dictionary is a "hash table" and if you are familiar with that concept then you can potentially understand a sentence that contains it. If however it is unfamiliar it must be appended and instantiated and perhaps it depends on other concepts. I know this is strange in a "normal" sense, but it relates to library dependencies, module dependency, and many other things.

When a course says that it has a pre-requisite it is very similar to library dependencies and dictionary dependencies. I could view the course, but I would never grasp the concept because I have an unsatisfied dependency.

Oh well, I was doing `scrabble` as a program and created a factorial expansion of letter combinations with scoring and application methods using a dictionary and this relates to how one would apply a dictionary of C key words or using methods in a C program or actually in any framework.

My biggest issue is that I can implement a dictionary in software that runs at less than log₂(N) and in hardware at N=1 (1 clock cycle). In the case of this Python, I can see that I could have word(x) in 100 picoseconds in hardware or 100 nanoseconds in assembly and since this is 6! *6 searches of a log₂(N) then it should be 4320 * 100 ns as a minimum, but with the overhead in Python I see 40 seconds, which is usable, but about 100,000 times slower than what I might do in assembly language directly. I grasp hash methods, and they are really useful. It seems that if I used this much, I would want to code a library in __ASM__ that did the heavy lifting and call it with a string of jumbled letters and return a library of the usable words.

Another thing is tat(that)[sic] I have decided to add a dictionary popup to my Firefox that allows me to (control+a) select all and perform a check to see if my typing contains the common errors that happen because of the way I type and other structural checks that I commonly make. Yes, there is only XUL.

It seems to me that an AI interface could be constructed in the same way that I would use WebGL. In order to "grok" a certain thing it would be necessary to satisfy certain dependent understandings before considering it. I wonder if I could construct a blog entry that generated a dependency of understanding list. :) Of course it should be #pragma once, well I would prefer __ifndef__ but your mileage may vary if you drive a different car..

It is a useful thing to learn in Python or any computer language. Dictionary methods and hashes have application in many instances and the general concept can apply to some real life problems.

if(not dicton.has_key(string)):
 if(wordlist.containsWord(string)):
  print string
  realwords+=1;
  x=getWordScore(string)
  dicton[string]=x
  if x>wordbest:
   wordbest=x
   string4=string

In the example above I am using a huge dictionary and a dictionary that tracks the best words found in a factorial combination of the letters in the computer's hand. Below is the console indicator of a pass with the letters 'qeesun' and best scoring. I have my doubts about this dictionary as it says "en" is a word, and I suppose it is , but seems odd.

en : In typesetting, the width of an average letter in a font.

Using letters qeesun - after iterations of 720 the best word is queens with score 15 out of 22 words found in dictionary.

An interesting learning experience and implications of how words are arranged in a weighted branched tree also provides some insight. That is next and it is a parallel to dealing with sequences of base pairs or amino acid sequences in a folded protein.