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November 14, 2007, 9:38 pm
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If one asks a black box a complex question, and the black box responds
with complex answers, is that AI or a Search Engine?
Is Computational Linguistics Artificial Intelligence? Depends (I
Sandy manages context, applies WSD based on context, and understands
defining statements that allows it to build associations. I.e. Abraham
Lincoln = sixteenth president of the United States. Abraham Lincoln =
first republican president, blast = explosion so on.
The modern definition of artificial intelligence (or AI) is "the study
and design of intelligent agents" where an intelligent agent is a
system that perceives its environment and takes actions which
maximizes its chances of success. More
Quick test of Google "what is required for life" (top 3)
WorldNetDaily: Is water <I>really</I> required for life on Mars?
Some NASA-funded scientists are ecstatic about that, since they assume
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If you ask Sandy the same, you get:
From: Drinking Wikipedia, the free encyclopedia
1) 41993 Water, for example, is required for many of life's
physiological processes, and excess or decreased water intake is
associated with health problems.
From: Potassium Wikipedia, the free encyclopedia
2) 163111 Outside of dating, potassium isotopes have been used
extensively as tracers in studies of weathering. They have also been
used for nutrient cycling studies because potassium is a macronutrient
required for life.
From: Calcium Wikipedia, the free encyclopedia
3) 163231 Calcium is essential for the normal growth and maintenance
of bones and teeth, and calcium requirements must be met throughout
Google sandypondfarm or go to www.sandypondfarm.net
More on Sandy:
Sandy is a program that understands the English language from a
mechanical perspective. It organizes the relationships between words
and their definitions, with the focus on the meaning behind the
relationship of the definition to the word. Sandy is built on top of
the English dictionary, (specifically WordNet 2.1) incorporating and
organizing the dictionary data to form a type of Ontology for each
word and definition derivation. The current prototype contains a base
of 36,554,566 unique Ontologies.
When a document (text, HTML, XML or anything exposing text) is
presented to Sandy, Sandy has the ability to identify well formed
thoughts. Sandy processes the well formed thoughts by deconstructing
the thought into its logical components, subject and predicate. If the
thought can be simplified i.e.; a compound sentence, Sandy will create
multiple simple sentences from the compound sentence. Sandy then
leverages the individual Ontologies to create relationships between
the individual words with in the thought. These relationships allow
Sandy to apply pattern matching and probability to establish the
specific definition for each word. For example given the following
At the start of the trial the judge showed the jury the photographs in
a private chamber
First the pattern is established:
Starting Adjunct: At the start of the trial,
Subject: the judge,
Sentence verb: showed,
Indirect object: the jury,
Direct object: the photographs,
Ending Adjunct: in a private chamber.
Second: use the relational Ontology, pattern matching, and probability
to establish context. The subject "the judge" will be assigned the
value of "a public official authorized to decide questions brought
before a court of justice", as apposed to: "an authority who is able
to estimate worth or quality".
Sandy applies the same logic across multiple thoughts or sentences, as
it parses text. Sandy re-evaluates past assignments as new context is
established. At the top there is the concept of topic that creates
relationships across sentences, much along the same line as a "Topic
Sandy has the ability to store the data based on the establish topic
in the components that make up the thought, i.e. indexing the Adjunct,
Subject, Verb, Subject compliment, Indirect object, Direct object,
Object complement. Indexing the data in a way that reflects the
sentence patterns, allows Sandy to search on any given component. For
example, given the following thought: "With almost 330,000 employees
worldwide and revenues of 91 billion annually IBM is the biggest
information technology company in the world", one could ask: "What is
IBM", Sandy would return "IBM is the biggest information technology
company in the world".
Sandy recognizes two types of thoughts; the informative and the
interrogative. If the thought is informative, Sandy processes the data
as stated. If the thought is interrogative Sandy performs a local
search in an attempt to answer the question. As part of the database
build out, Sandy takes each word and properly relates it to the
underlying definition to generate a well formed sentence or thought.
These statements are then digested into thought component tables.
At the end of indexing the English dictionary we start to see the
power of the product. One might inquire "what is carbon-dioxide" and
the response would be "a heavy, odorless, colorless gas formed during
respiration and by the decomposition of organic substances; absorbed
from the air by plants in photosynthesis". This is no different than
looking up the word in a dictionary. The real power comes from
indexing the word "Black", where there is a specific definition of
"Joseph Black", "A British chemist who identified carbon dioxide and
who formulated the concepts of specific heat and latent heat". Based
on indexing this thought one could not only inquire on a subject,
carbon-dioxide but more powerfully ask question related to carbon-
dioxide, i.e. "Who discovered carbon-dioxide? The information returned
would be "Joseph Black identified carbon dioxide". One could go on to
ask "Who was Joseph Black"
This is a simple example of indexing well established information. You
may have been able to find the same level of information by looking up
carbon-dioxide in your favorite encyclopedia, scan through the page
and you might find this information. Wikipedia has a reference to a
Scottish physician named Joseph Black. After indexing all of
Wikipedia, one could argue that Sandy has knowledge of the universe as
we know it.
If you move this process into data that is not so established (just
about every thing else) you have a new way to look at indexing free
formed information. For example, If Sandy indexes a list of financial
websites; one could ask questions about potential investments. i.e.
"Who thinks IBM is a good buy". Sandy would reply with a list of
people who have stated that IBM is a positive investment". In this
example Sandy accomplishes dynamics by leveraging the Ontology's for
the words and definitions of the question, "IBM", "good" and "buy".
Sandy represents a way of organizing information that can refine the
data to produce a more meaningful response to a search or question.
Inherent to having a program that can interpret the English language
opens the door to the prospects of using everyday conversation as a
user interface. Sandy is well on the road to being an application that
is conversational on human terms.