2. The fundamental approach of Thinknowlogy

Since the origin of life is subject to discussion, the starting point of the all research regarding the origin of life is dependent on the world view of the researcher: atheism or creationism. It includes the way natural intelligence, natural language and natural laws are researched. So, it also includes the field of AI and NLP.

  • Atheism: Despite exhaustive research, atheism still hasn't provided a satisfactory explanation for the origin of natural intelligence, natural language and natural laws. Let alone, how they are related;

  • Creationism: Creationists will try to find the intelligent design – natural laws – that governs the natural data in a generic way. In delivering a generic solution, the natural data itself isn't important. The problem is where to find – and how to research – the intelligent design – natural laws – that governs the data.

According to the biblical world view, all natural systems are created by God. It includes laws of nature, to make his creation run like clockwork, in a unified, structured and deterministic (*) way. It means that all natural phenomena must obey the laws of nature, and that laws of nature work in a unifying, structured and deterministic (=implementable) way.

 

Assuming that God’s intelligent design includes laws of natural intelligence, these laws of intelligence will operate in a unifying, structured and deterministic (=implementable) way. Being deterministic (=implementable), these Laws of Intelligence can be implemented in artificial systems, by a process of reverse-engineering.

I have identified the human language and spacial information as sources of natural intelligence. And because all natural phenomena are designed in a unified way, natural intelligence and the human language may be related. If so, it must possible to identify the natural laws that are obeyed by language (Laws of Intelligence that are naturally found in the Human Language), by which it is possible to reconstruct the language center of our brain, through a process of reverse-engineering.

Furthermore, according to the biblical world view, life and the universe were all designed once. And no improvements were made afterwards. So – if intelligence and language are related – current languages must still obey the same laws of intelligence as was designed in the beginning, regardless of all their differences (**). Then, current languages still must share a common logic.

(*) deterministic: “the doctrine that all facts and events exemplify natural laws”.

(**) The existence of entirely different languages today, is explained in the bible: “At one time all the people of the world spoke the same language and used the same words” (Genesis 11:1). During the building of the tower of Babel, God confused the tongues: “Come, let’s go down and confuse the people with different languages. Then they won’t be able to understand each other” (Genesis 11:7).

2.1. Natural intelligence, giftedness and talent, knowledge and skills

Scientists are struggling with terms like intelligence, giftedness, talent, knowledge and skills, because they don’t understand their origin:

  • natural intelligence is innate, equal for every kind. Intelligently designed by God;

  • giftedness and talent are inherited, different for each individual;

  • knowledge and skills are learned by each individual.

 

2.2. Natural intelligence

In order to contribute to science, intelligence need to be defined in a unifying, fundamental (=natural) and deterministic (*) (=implementable) way:

 

Natural intelligence is the natural ability to organize independently.

It is the extent to which one is able to independently:

  • group what belongs together;

  • separate what doesn't belong together;

  • archive what is no longer relevant;

  • plan future actions;

  • foresee the consequences that the planned actions will have;

  • learn from mistakes.

Assuming that natural language is an intelligent system, predictions can be made on the intelligence that will be found in language:

  1. Natural language will have self-organizing abilities;

  2. In expressing knowledge, the language center of the sender’s brain will add clues to the knowledge that is expressed, how the knowledge is organized in the brain of the sender;

  3. In receiving knowledge, the language center of the receiver’s brain will use the clues that are added to the received knowledge, in order to organize the knowledge in the brain of the receiver.

In all languages, there will be specific words – or word constructions – for:

  • grouping knowledge that belongs together;

  • separating knowledge that doesn't belong together;

  • archiving knowledge that is no longer relevant;

  • planning future actions;

  • foreseeing the consequences that the planned actions will have;

  • learning from mistakes.

(*) deterministic: “the doctrine that all facts and events exemplify natural laws”.

2.3. Laws of Intelligence that are naturally found in the Human Language

Logical clues that are naturally found in language, provide information to our brain how to structure / organize the gained knowledge. These clues include specific words for grouping, separating and archiving (see definition of natural intelligence). By using these clues provided by natural language – which I call: Laws of Intelligence that are naturally found in Language – we are able to implement a self-organizing (=intelligent) knowledge technology, similar to the way nature works in the language center of our brain:

  • Conjunction “and” has the intelligent function in language to group knowledge;

  • Conjunction “or” has the intelligent (Exclusive OR) function in language to separate knowledge;

  • An definite article (in English: “the”) has the intelligent function in language to archive knowledge;

  • An indefinite article (in English: “a”) defines a structure, which is already known for a few centuries;

  • Basic verb “am/is/are” defines present tense basic logic, which is already known for a few centuries;

  • Basic verb “was/were” defines past tense basic logic;

  • Possessive verb “has/have” defines present tense direct and indirect possessive logic;

  • Possessive verb “had” defines past tense direct and indirect possessive logic.

 

Besides that, grammar also provides logical reasoning constructions, as described from paragraph 2.3.1 Specification Substitution Conclusions of the Theory document.

 

These Laws of Intelligence that are naturally found in Language drive a set of structuring algorithms (*) in my system, in order to independently group, separate and archive knowledge in its knowledge base.

 

So, the basics of natural language: Grammar provides language a general structure of separate words, by which the words form a sentence. And Laws of Intelligence that are naturally found in Language provide language a logical structure of separate words and separate sentences, by which the words and sentences make sense.

 

Scientists are unable, unwilling or forbidden to define intelligence as a set of natural laws. Therefore, scientists are unable, unwilling or forbidden to add natural intelligence to chatbots, virtual assistants and robots ('bots' for short). As a consequence, bots are lacking natural intelligence: Either they are limited to programmed dialogues, or the sentences they produce don't make sense.

(*) algorithm: “any set of detailed instructions which results in a predictable end-state from a known beginning

 

2.3.1. Example: Autonomous generation of questions

Not a single scientific paper describes automatically generated questions in a generic way (=through an algorithm), like:

 

Given:

  • “Every person is a man or a woman.”

  • “Addison is a person.”

Generated question:

  • “Is Addison a man or a woman?”

 

The implementation of this kind of automatically generated questions is extremely simple when Laws of Intelligence that are naturally found in the Human Language are used:

  • A Law of Intelligence that is naturally found in Language: Conjunction “or” has the intelligent (Exclusive OR) function in language to separate knowledge;

  • Given “Every person is a man or a woman” and “Addison is a person”;

  • Substitution of both sentences: “Addison is a man or a woman”;

  • Conversion to a question: “Is Addison a man or a woman?”.

Note: In most cases, a conjunction separates a series of words of the same word type. In this case, a series of singular nouns.

 

2.3.2. Improve your ontology system towards a grammar-based approach

Why wait for scientists to accept a grammar-based approach? You can improve your own ontology system gradually towards a grammar-based approach:

  • Start to implement the scientific challenge I launched to beat the simplest results of my Controlled Natural Language reasoner;

  • Then expand your system by implementing the reasoning constructions – listed in the Theory document – that are not listed in the challenge document;

  • Contact me for more improvements.

2.4. Intelligence – more into depth

Intelligence is a natural phenomenon, which can be described as the extent to which one is able to organize independently. More specific, to independently:

  • avoid chaos;

  • create order;

  • restore order.

The basic capabilities of intelligence are:

  • Grouping (combining) of individual or separate objects, with the aim of achieving a goal that can not be achieved by either of those objects separately;

  • Separating (differentiating) compound or intertwined objects, with the aim to clarify the situation, by putting them in their own context;

  • Archiving of obsolete information, separating current from obsolete information;

  • Planning future actions, setting goals and anticipation to changes;

  • Foreseeing possible consequences: Using knowledge and experience to predict possible consequences of planned actions (own plans and planned actions of others);

  • Learning from mistakes: Using knowledge and experience to determine the course of a mistake, and to avoid making this kind of mistake in the future.

These capabilities of intelligence can be applied to basic concepts like: numbers, language and spatial objects. Grouping of for example numbers, we call: adding. And separating of numbers, we call: subtracting.

Deepening:

  • Creation starts with grouping;

  • Understanding starts with separating;

  • Omitting starts with archiving;

  • Governing starts with planning;

  • Anticipation starts with foreseeing;

  • Improvement starts with learning from mistakes.

 

I am implementing grouping, separating and archiving as much as possible, while leaving the implementation of the remaining capabilities to future generations.

2.4.1. Autonomy / independently

In the definition of natural intelligence, the word “independently” is used. So, we need to define that word – or actually the word “autonomy” – as well:


An autonomous system relies on the consistency of a natural source, or a consistent artificial source like GPS (Global Positioning System). So, an autonomously intelligent system relies on the consistency of a natural source of intelligence.


In contrast, current information systems rely on artificial sources of intelligence, like semantic vocabularies, ontology databases and statistics. Only Thinknowlogy uses a natural source of intelligence: language, or more accurate: Laws of Intelligence that are naturally found in the Human Language.


Scientists have no clue how nature works in regard to intelligence and language. So, they implement "something" that looks like nature. But they have no proof that nature works that way. “Inspired by nature”, scientists in this field are engineering specific solutions to specific problems, while a fundamental science delivers generic solutions. So, I know that their approach in fundamentally wrong.


The "scientific" approach is comparable to an old-fashioned car, in which the driver needs to operate most functions of the car manually, and in which the driver needs to navigate him/herself to an unknown address. My fundamental approach is comparable to a self-driving car, in which more and more functions are automated. It is based on the logic of language, which is a natural – and thus a consistent – source of intelligence.

 

2.4.2. IQ test

When comparing IQ tests to the above definition of natural intelligence, it becomes clear that IQ tests are focused on the capabilities grouping and separating. But they are lacking tests for archiving, planning, foreseeing and learning from mistakes.


But more important than a high IQ score: Is one's worldview in accordance with the way nature works?


One can have an extremely high IQ score, and develop many new theories. But what is the contribution of those theories, when those theories can’t be applied to daily life? Only theories that are in accordance with the way nature works, can be applied to daily life.

2.5. Universal Grammar theory

In his Universal Grammar theory, Noam Chomsky proposes that the ability to learn a language is hard-wired in the brain. This theory is heavily debated among evolutionists. But deniers of this theory have no alternative explanation – let alone an artificial implementation – that is supported by experimental evidence.

 

In my Controlled Natural Language (CNL) reasoner, one set of logical rules – as defined in my scientific challenge – is configured for multiple languages. So, it implements the Universal Grammar theory with a difference: There is no Universal Grammar, but there are Universal Rules of Logic naturally found in Grammar. Or as I would say: There are Laws of Intelligence that are naturally found in the Human Language.

 

Logic / algebra itself is language independent. And universal rules of logic seem hard-wired in the language center of our brain. When children learn a language, the universal logic – that is naturally found in the language center of their brain – is ‘configured’ for a language, which will be their native language / mother tongue.

 

My CNL reasoner works in a similar way: By embedding one set of logic / algebra / universal reasoning rules, my reasoner is (almost) language independent. During start-up, the software reads five grammar configuration files, which configure this universal logic for five languages. After start-up, my reasoner is able to read, to reason and to autonomously write – word-by-word constructed sentences – in English, Spanish, French, Dutch and Chinese.

 

Semantic techniques require each word to be defined in a words list. But we don’t feed a words list to babies and toddlers either, in order to learn their mother tongue. My CNL reasoner has no extensive words list either. The difference between semantic techniques and the universal logic techniques of CNL reasoners is illustrated by a well-known Chinese saying: “Give a man a fish and you feed him for a day. Teach a man to fish and you feed him for a lifetime”. My CNL reasoner only has a few basic words defined upfront. Instead, it has grammar definitions (*), and an algorithm (**) that determines the word type of each unknown word, like adjective, singular noun and plural noun.

 

(*) See download, sub-directory: data/grammar/
(**) See source code: class AdminReadCreateWords, function createReadWords

 

2.6. Other sources of intelligence

Language is not the only source of intelligence. Animals like dolphins, crows and chimpanzees show intelligent behavior regarding to spacial information. So, spacial information is another source of logical information (intelligence). An example:

 

If a room has only one entrance, and there are no temporary entrances, and there is an object inside that room, then we can conclude: Either that room is built around that object, or that object must have entered the room through that one entrance. So, if we see a classical miniature ship in a bottle, and this bottle has no temporary entrances like a separate bottom, either the bottle is built around that ship, but more likely, the ship has entered the bottle through the bottleneck.

 

More derived spacial information: The miniature ship consists of multiple components, leaving the audience in awe which of those components were already attached, and which were attached later on. (But its party trick is of course the unfolding of the masts and sails.)

 

Creating a miniature ship in a bottle requires capabilities of natural intelligence, like grouping, separating, planning and probably also learning from mistakes. Not only the creator, but also the audience watching the end result, will need capabilities of natural intelligence in order to analyze the problems involved with this peculiar object. A curious person who sees a miniature ship in a bottle for the first time, will not just say “nice” and walk away. Apparently, the laws of nature involved with spacial information are already present in the brain. They will trigger the brain of a curious person when the spacial information doesn’t add up.

 

Illusionists are masters in hiding aspects of spacial information that are crucial to their trick, by which the spacial information – visible to the audience – doesn’t add up: Objects seem to appear and disappear as if by magic.

 

I like the artwork of M.C. Escher. He understood the logical structures of spatial information very well. In his artwork, Escher plays with the outer lines of objects like birds and fish. In other artwork, Escher deliberately applied the logical structures of spatial information in a wrong way, by which this artwork seems 'wrong'. Brilliant!

 

Objects like birds and fish structured in artwork, are like keywords structured in a sentence.

 

A lot of daily activities – like anticipation in traffic and sports – require capabilities of natural intelligence in order to process a lot of spacial information in a fraction of a second. It includes capabilities like grouping, separating, planning, foreseeing and learning from mistakes. Experience (training) helps to use as much spacial information as possible within a short time frame. In self-driving cars and trucks, the processing of spacial information is more and more automated to our benefit. In fact, these are also artificial implementations of natural intelligence (within a limited domain). Prefix “self” in “self-steering” refers to the natural origin of the spacial information.