In a semantic net, a diagram of relations between objects, essential notions can be defined as follows:
Combinators: linking objects together
Reification: actions implying results
Localization: a frame where objects and actions happen
Story sequence: a series of actions happening linearly in time
In natural language, knowledge is generally organized from general categories to basic objects and finally specific objects.
Another element of language is recording change in the evolution of objects during the unfolding of stories.
Language also tracks movement in the description of actions.
An agent makes an object move from a source to a destination using a instrument. A co-agent may be involved, the action might be aimed towards a beneficiary, and the motion may be helped by a conveyance, etc. In English, preposition tend to be used to define the role of each part in the action, enabled recording of interactions.
Agents’ action determine transitions in the semantic net, which result in trajectories.
Each type of story implies a number of characteristics that correspond to the situation. Example: events can be disasters or parties, they have a time and place, involved people, casualties, money, places…
Words can be classified in tables representing the 14 features of each sound, notably if they are “voiced” or not. With such a matrix of sounds, a “speaking machine” can be built to produce words and sentences.
Example: a machine pronouncing plurals
The machine processes from recognition through different characteristics of words (verbs, nouns, plurals…) and constraints (pronunciation of plural) to produce sounds. The machine’s input comes from visual recognition and its output is sounds to mimic a voice to produce the right sound, “z“, “s” or “e-z“.
The vision apparatus recognizes objects
The information flows to the meaning register, labeling the objects, as well as through characteristics register to define if they are plural or not
The information from the meaning register also flows through the characteristics register to define if they are nouns or verbs, and to a buffer which stores the beginning of the word letters while waiting to know if a “z” or “s” or “e-z” plural sound needs to be added.
The buffer letters moves to be processed by the pronunciation apparatus, waiting to be processed by the plural constraint.
The word is send to the plural constraint processor.
If the word is plural and the last letter is voiced, the constraint adds a “z” sound, if not voiced an “s” sound is added. If the word is not plural, nothing is added, ending the word’s pronunciation.
Such machine is called a propagator as connections are reversible: by hearing a plural word, the machine can figure where are many of the objects to be seen by the visual apparatus.
Building phonological algorithm
Collect + and – examples
Pick and seed positive
Generalize by churning useless information where + and – examples are not differentiated
Quit when cover a negative example, or go back to step 3 and generalize more
The plural machine works because in a high dimensional sparse space, a hyperplane can easily separate two sets of examples, as phonemes constitutes a 14-dimension space.
Generalizing to problem solving with AI
Define the problem
Devise a representation suiting the problem
Determine an approach / method
Pick a mechanism or devise an algorithm
Troubles ensues when people focus on a specific mechanism rather the problem at hand.
The right representation does the following:
it makes the problem explicit
it exposes constraint
it generates localness to solve the problem: the answer can be pinpointed rather than gathered from various sources, areas, places, etc.