Strong AI is being coded for autonomous humanoid robots by these steps.
1. Code the MainLoop module --
Use either an actual loop with subroutine calls, or make a ringlet of perha
ps object-oriented module stubs, each calling the next stub. Provide the ES
CAPE key or other mechanisms for the user to stop the AI.
2. Code the Sensorium module or subroutine --
Start a subroutine or module that is able to sense something coming in from
the outside world, i.e., a key-press on the keyboard.
3. Stub in the EnThink module for English thinking --
4. Initiate the AudInput module for keyboard or acoustic input.
Drop any [ESCAPE] mechanism down by one tier, into the AudInput module, but
do not eliminate or bypass the quite essential Sensorium module, because a
nother programmer may wish to specialize in implementing some elaborate sen
sory modality among your sensory input stubs. Code the AudInput module init
ially to deal with ASCII keyboard input. If you are an expert at speech rec
ognition, extrapolate backwards from the storage requirements (space and fo
rmat) of the acoustic input of real phonemes in your AudInput system, so th
at the emerging robot Mind may be ready in advance for the switch from hear
ing by keyboard to hearing by microphone or artificial ear.
5. The TabulaRasa loop.
Before you can create an auditory memory AudMem subroutine for storing inpu
t from the keyboard, you may need to code a "TabulaRasa" loop that will fil
l the mental memory of the AI with blank engrams, thus reserving the memory
space and preventing error messages about unavailable locations in the AI
6. MindBoot English +/- Russian bootstrap --
The knowledge base (MindBoot) module makes it possible for the Strong AI Mi
nd to begin thinking immediately when you launch the more advanced AI progr
am. Here we stub in the EnBoot subroutine with an English word or two befor
e the AudMem module begins to store new words coming from the AudInput modu
le. The EnBoot stub shows us that the first portion of the AI mental memory
is reserved for the innate concepts and the English words that express eac
h concept. If you use the same Unicode that Perl enjoys to create a Strong
AI Mind in Arabic, Chinese, Hungarian, Indonesian, Japanese, Korean, Swahil
i, Urdu or any other natural human language, you will need to create a boot
strap module for your chosen human language.
7. AudMem (Auditory Memory) --
Into the auditory array that was filled with blank spaces by the TabulaRasa
sequence and primed with some bootstrap content by the EnBoot or MindBoot
sequence, insert some new memories with the AudMem auditory memory module.
Modify the AudInput module to prompt for English words and modify the EnThi
nk module to display words stored in memory as if they were a thought being
generated in English (or in your chosen natural human language).
8. NewConcept Module --
The NewConcept module addresses the symbol grounding problem by creating a
new concept for any unrecognized word in the input stream, even a misspelle
d word entered by mistake. In Symbolic AI, each word of natural language is
the symbol of a concept, and as such is the key to accessing the concept.
Of course, a recognized image may also grant access to a concept.
9. EnParser English Parsing Module --
The EnParser (English parser) module does not so much determine the part of
speech of a word of input, but more importantly it assigns to an input wor
d its grammatical role in the complete phrase being processed during Natura
l Language Understanding.
10. InStantiate -- --
The InStantiate module creates a new instance or node of a concept in Symbo
lic AI when a word of input activates the concept. The created instance is
subject to change by the possibly delayed action of the English EnParser or
Latin LaParser or Russian RuParser module, because Natural Language Unders
tanding must often wait for the end of an idea before the whole idea can be
11. AudRecog auditory Recognition Module --
The AudRecog module for auditory recognition recognizes various forms of a
word, such as singular or plural nouns, or verbs with various inflected end
12. TacRecog Module --
The TacRecog module for tactile recognition in robots implements the haptic
sense for an AI Mind directly to touch and feel the external world. Even a
n AI Mind not yet embodied in a physical robot may use TacRecog directly to
sense and feel a number-key pressed by the human user on a computer keyboa
rd. With philosophic implications for the learning of mathematics, an AI Mi
nd may directly sense numeric quantities through the numeric keys on the ke
13. OldConcept Module --
If the AudRecog module recognizes a particular word, then the AudInput modu
le calls the OldConcept module to create a new instance of the previously k
nown concept. If a word is not recognized, AudInput calls the NewConcept mo
dule to create a new concept for the word as a symbol.
14. SpreadAct Spreading Activation Module --
The SpreadAct module for Spreading Activation performs both simple spreadin
g activation between concepts and also an extremely sophisticated role of r
esponding to various input queries posed by human users.
15. PsiDecay -- --
The PsiDecay module lets the activation on "Psi" concepts decay gradually o
ver time, so that mind-modules which impose or spread activation may operat
e more effectively and so that artificial Consciousness may emerge as the s
eearchlight of attention shifts from one highly activated concept or sensat
ion to other highly activated concepts or sensations.
16. Speech Module --
The Speech module fetches characters from a starting point in auditory memo
ry and displays the characters on-screen until a blank space occurs to sign
ify the end of the word stored in memory.
17. Indicative --
The Indicative Mood module, as opposed to the Imperative Mood module for ex
pressing commands, calls linguistically generative modules such as EnNounPh
rase and EnVerbPhrase to express a thought indicating an idea or a belief.
18. EnNounPhrase English Noun-Phrase Module --
The English noun-phrase module selects the most activated noun-concept to b
e the subject of a phrase or sentence.
19. ReEntry --
e reentry of an output word back into the AI Mind.
20. EnVerbPhrase English Verb-Phrase Module --
The English verb-phrase module fetches from memory a verb that has basicall
y been pre-ordained to be expressed as the verb in a Subject-Verb-Object (S
VO) phrase or sentence. EnVerbPhrase also calls a module like EnVerbGen to
generate an inflected form of an indicated verb. EnVerbPhrase is designed w
ith a view to calling the VisRecog module to supply the English word for th
e visually recognized object of the action of a verb, such as in a sentence
like "I see... (a dog)."
21. EnAuxVerb English Auxiliary Verb Module --
The English auxiliary-verb module calls auxiliary verbs such as "do" or "do
es" for use in the generation of such sentences as a negated idea, such as
"God does not play dice."
22. AskUser Module --
The AskUser module works in conjunction with the logical InFerence module t
o ask a human user to confirm or deny a logical inference being proposed in
side an AI Mind.
23. ConJoin Module --
The ConJoin module inserts a conjunction during the generation of a compoun
d thought. For instance, if an AI Mind has two or more higjly activated sub
jects of thought, the ConJoin module will insert the conjunction "and" to j
oin two active ideas together.
24. EnArticle Module --
The English article module inserts the article "a" or the article "the" bef
ore a noun in a sentence being generated.
25. EnAdjective Module --
The English adjective module recalls and inserts an adjective during the ge
neration of a thought.
26. EnPronoun Module --
The English pronoun module replaces a noun with a pronoun.
27. AudBuffer Module --
The auditory buffer module stores a word in memory for transfer to the OutB
uffer module for inflectional processing.
28. OutBuffer Module --
The OutBuffer module holds a word in a right-justified framework where the
ending of the word may be modified by a module like the EnVerbGen module fo
r generating a required English verb-form.
29. KbRetro Module --
The KbRetro module retroactively adjusts the knowledge base (KB) of the AI
in response to user input responding to a question from the AskUser module.
30. EnNounGen English-Noun Generating Module
The English noun-generating module shall modify a singular English noun int
o its proper plural form by adding "s" or "es".
31. EnVerbGen EnGlish Verb Generating Module --
The verb-generation module operates when the verb-phrase module fails to fi
nd a needed verb-form in auditory memory.
32. InFerence Module --
The InFerence module engages in automated reasoning with logical inference.
For instance, if the user inputs 'John is a student," the AI may infer the
possibility that John reads books, The AskUser module asks the user, "Does
John read books?" Depending on a "yes" or "no" answer, the KbRetro module
retroactively adjusts the knowledge base (KB), either discarding the unwarr
anted inference or by leaving intact a true inference or inserting "not" in
to a negated inference such as "John does not read books."
33. EnThink English Thinking Module --
The English thinking module calls such subordinate modules as the Indicativ
e module for a declarative sentence or the InFerence module for automated r
34. Motorium Robot Motor Memory Module --
As soon as you have sensory memory for audition, it is imperative to includ
e motor memory for action. The polarity of robot-to-world is about to becom
e a circularity of robot - motorium - world - sensorium - robot. If you hav
e been making robots longer than you have been making minds, you now need t
o engrammatize whatever motor software routines you may have written for yo
ur particular automaton. You must decouple your legacy motor output softwar
e from whatever mindless stimuli were controlling the robot and you must no
w associate each motor output routine with memory engram nodes accreting ov
er time onto a lifelong motor memory channel for your mentally awakening ro
bot. If you have not been making robots, implement some simple motor output
function like emitting sounds or moving in four directions across a real o
r virtual world.
35. Volition module for robot free will --
In your robot software, de-link any direct connection that you have hardcod
ed between a sensory stimulus and a motor initiative. Force motor execution
commands to transit through your stubbed-in Volition module, so that futur
e versions of your thought-bot will afford at least the option of incorpora
ting a sophisticated algorithm for free will in robots. If you have no robo
t and you are building a creature of pure reason, nevertheless include a Vo
lition stub for the sake of AI-Complete design patterns.
36. Imperative --
The Imperative Mood module, called by the free-will Volition module, issues
commands such as "Teach me something" to the human user.
37. The SeCurity module --
The SeCurity module is not a natural component of the mind, but rather a ma
chine equivalent of the immune system in a human body. When we have advance
d AI robots running factories to fabricate even more advanced AI robots, le
t not the complaint arise that nobody bothered to build in any security pre
cautions. Stub in a SeCurity module and let it be called from the MainLoop
by uncommenting any commented-out mention of SeCurity in the MainLoop code.
Inside the new SeCurity module, insert a call to ReJuvenate but immediatel
y comment-out the call to the not-yet-existent ReJuvenate module. Also inse
rt into SeCurity any desired code or diagnostic messages pertinent to secur
39. Spawn --
The Spawn module issues commands to the operating system to make copies of
an AI Mind that include experiential memories up to the point of the spawning of each new AI Mind.
40. MetEmPsychosis --
The module of MetEmPsychosis or soul travel is designed to spawn a remote c
opy of an AI Mind while immediately deleting the previous version of the so
ftware and memories so that the remote new version of the AI Mind is effect
ively the same AI traveling across cyberspace in a metastatic process akin
to mind uploading.
- posted 2 years ago