Graph Enhanced Intelligent Search with Homoiconic Actions
— (finally a very promising attempt) towards Technological Singularity
- Calling this algorithm GEISHA carries a certain manga cyberpunk nuance, as if the AI system that we wish to create will serve human like a real world Japanese geisha.
Conventional artificial intelligence algorithms may be broadly classified into robotic and non-robotic categories, although of course adherents of particular disciplines may express differing opinions.
A fundamental question based on the categorisation above postulates:
- Since robots mimick human beings, are robotics algorithms more promising in achieving human level artificial intelligence (HLAI) first, before those of the non-robotic category?
We believe we have conceived an algorithm that is applicable to both robotic and non-robotic AI, tentatively named "Graph Search and Homoiconic Action" (GEISHA):
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We model the human mind with a graph database (GDB).
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We postulate that external triggers (such as conversations, sound, light and other sensory events) and internal thoughts initiate searches in the graph database (modelling the human mind), hence the term "graph search".
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Let us illustrate a graph search with the following example:
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a) A CSV file F1 contains a record of a person who is a cook. F1 is linked to GDB by its full Linux path name (directory and filename), which is stored as a JSON string.
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b) A search is triggered when the AI system AI1 receives a chat message from user U1 "where can I find a cook?"
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c) One of the steps in the graph search involves simply the following:
grep cook F1
Or any equivalent function in any programming language.
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d) When the rows in F1 containing "cook" are returned, we may also infer its "field name" e.g. "profession", which is usually at the top of the column concerned.
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e) From above, we may conclude that "The profession of Mr X is a cook."
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f) F1 may be linked to other records in GDB, which may lead us to infer "Mr X is Ms Y's husband."
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g) As such, we have demonstrated that graph search may be used to construct relationships and the target objects of such relationship of a search subject.
We may call this technique "bottom up tree traversal relationship construction" (BUTTERCON), as the search subject can be viewed as the leaf node of a tree containing many other related objects (nodes).
- h) Next, the search results described above is used to construct a conditional variable C1 which is used to map to an action A1.
To illustrate, a human person may think, "if he is a cook, then I will call him when I need to employ one."
Then A1 would be:
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search C1 for his phone number.
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i) Action A1 has to be a homoiconic graph function (HGF), which is defined as:
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- A function in C/C++ or equivalent programming language is homoiconified using Phoscript Inverse Shunting Yard algorithm (see item (j) for explanations).
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- The homoiconified function word is stored in GDB.
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j) Here is an example of using Forth / Phoscript to interface to C++ SymEngine computer algebra library:
The main novelties of GEISHA are
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Bottom up tree traversal relationship construction Buttercon
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Homoiconified Graph Functions (HGF)
Are these algorithms unprecedentedly novel?
We shall find out after the almighty Internet users help us with literature review after we have posted this article.
More importantly are the next steps towards realizing HLAI with GEISHA.
The exciting news are that the inputs to both BUTTERCON and HGF are literally the entirety of Internet itself!!
As such, we just need to patiently construct the set of rules R matching conditions C to actions A, at first manually and slowly, until we hit the point of avalanche when GEISHA can automatically construct the rule sets R.
- How long would that be?
It may be a week, a month or ten years.
But one thing for sure is that, with the collective intelligence of Internet users, we may soon see the dawn of the technological singularity.
- What do we do after that?
That would be a story for another day ...