thinknowlogy a fundamental approach to ai knowledge technology

Thinknowlogy

A fundamental approach to Artificial Intelligence / knowledge technology

A webinar initiated by Compegence

December 15, 2012

© 2012 Menno Mafait http://mafait.org

Thinknowlogy - A fundamental approach to Artificial Intelligence / knowledge technology

A fundamental approach 3

Demo 4

Questions 5

Introduction 1

The current approach to AI 2


Short CV

• Name: Menno Mafait

• Education: Computer science (undergraduate level)

specialization: telecommunications;

• Experience: Software Tester with several companies,

currently developing Thinknowlogy.

Introduction 1


Some rules during this session

• Questions can be posed during this session in the

chatbox of TeamViewer. I will have a look now and then,

and try to answer your question before switching topics;

• Only if your question is important to your understanding,

enable your microphone in the TeamViewer window and

interrupt me with your question;

• Please try to describe your question briefly.

Introduction 1


The relation between Artificial Intelligence (AI) and

knowledge technology

Semantics / meaning is a subset of intelligence.

• Artificial Intelligence is (or should be) based on

intelligence;

• Knowledge technology is (or should be) based on

Semantics / meaning.

So, knowledge technology is a subset of AI.

Introduction 1



Demo 4

Questions 5

Introduction 1



A few questions:

• Why is Siri considered stupid by some users?

• Why do search engines still show lists with links -

rather than just answer the question of the user?

• Why does Watson need 2800 CPU cores “to find

a needle in the haystack of unstructured texts”?

Why doesn't it just structure those texts while the

knowledge is acquired? Wouldn't that use significantly

less CPU power during the search?



What is Siri?

Siri is an iPhone app that allows the user to speak to

their iPhone. Siri will then respond with text on the screen

as well as a voice. Siri is able to read, write and send text

messages (emails, tweets, etc.) on the user's command,

to read and organize the user's agenda, to answer some

questions like about the weather forecast, and to find

shops and restaurants in the neighborhood.



But why is Siri considered stupid by some users?

Siri is a clever combination of speech technology, GPS,

search engines and pre-programmed responses. It gives

the user a feeling that the system is quite human.

However, the combination of technologies is intelligent,

not the app itself. For example, Siri will probably respond

to the sentence “The Chinese is near the restaurant” by

providing the location of the nearest Chinese restaurant

rather than processing this knowledge.



Why do search engines still show lists with links -

rather than just answer the question of the user?

Because current technologies filter down the sentences

to mainly keywords and discard the “useless” words,

by which most of the meaning is lost irrecoverably.

So, despite all effort done to develop the Semantic Web,

current knowledge technologies are still unable to

understand the human language.

The next best thing: Let the user be the intelligent factor,

guided by current semantic technologies.



What is Watson?

Watson is a supercomputer system developed by IBM

to support professionals by finding a keyword from a

description, e.g. by providing a list of possible deceases

from a description of symptoms. In fact, Watson is a

reversed Wikipedia.

To prove the power of Watson, IBM initiated a Jeopardy

competition between human competitors and Watson.



What is Watson?

Jeopardy is a quiz show in which a cryptic description

must be answered by a keyword, in the form of:

“What is {noun}?” or “Who is {proper noun}?”.

So, Watson isn't able to answer in sentences, only to

find keywords.



• But why did Watson need 2800 CPU cores to find

“a needle in the haystack of unstructured texts”

(quote from IBM) in order to beat human competitors?

• Why didn't IBM just structure the knowledge base

of Watson before playing Jeopardy? Wouldn't that

speed-up the search itself, using significantly less

CPU power during the game?

Because science (and IBM) fails to develop techniques

to structure or organize knowledge autonomously.



More into depth:

Search engines, chatbots (including Siri) and other

natural language systems degrade the sentences to

interlinked keywords and throw away the rest of the

words, by which a lot of the meaning is discarded

irrecoverably. Therefore, such systems are only

able to find texts containing given keywords.

Besides that, current AI systems are only able to act

intelligently on pre-programmed situations – and

therefore – chatbots fail to keep focus.



But what about Artificial Neural Networks (ANN)?

Using a metaphor to describe an ANN: a building.

Neurons are not more then bricks, and an ANN isn't

more than a pile of interconnected bricks,

which is no building.

An architect is required to design a building. However,

nobody knows how to design a high-level architecture

for an ANN.



But what about Artificial Neural Networks (ANN)?

And therefore, an ANN:

• is limited to execute one task – and one task only;

• will always be a slave, and will never become a master;

• will never show intelligent behavior, because intelligence

should be gained autonomously, and nobody knows how

to design an ANN architecture that gains its intelligence

autonomously.



And what about the Semantic Web?

The Semantic Web is assumed to add meaning to

the Web, by which knowledge technology is assumed

to understand the human language.

The Semantic Web is a commercial success,

but it still fails to understand the user,

because scientists don't know what semantics is.



What is implemented then by AI and knowledge

technology experts?

• Nice – but baseless – techniques.

AI is only:

– “inspired by nature”;

– fooling the customer;

– marketing;

– earning big bucks.



So, what went wrong?

AI has a fundamental problem:

• AI scientists started to develop techniques

without defining and developing a foundation first.

So, in fact, the field of AI / knowledge technology

is baseless (has no foundation) and isn't scientific.



Can you prove that AI is not fundamental?

Of course.

Characteristics of a mature science:

• A mature science is defined unambiguously;

• A mature science is based on a natural foundation;

• A mature science integrates all its disciplines.



A mature science is defined unambiguously

Using the metaphor of a building again:

– Before an architect starts to draw,

the client's requirements must be specified clearly.

• However, science started to build Artificial Intelligence

without defining of intelligence first;

• And science started to build knowledge technology

without defining Semantics / meaning first.

So, on what is AI / knowledge technology based then?




Why does science fail to define intelligence and

semantics?

• According to science, intelligence should have been

emerged “by itself”, like by complexity and chaos;

• And natural language should have been evolved

from the primal sounds of cave men.

However, bright people like Leonardo da Vinci and

Albert Einstein taught us the exact opposite:

Intelligence is in simplicity and structure.




Why does science fail to define intelligence and

semantics?

• So, what if the evolution theory is nonsense?

• What if intelligence and natural language aren't the

chaotic result of evolution, but the organized result

of an intelligent design by God?



A mature science is based on a natural foundation

For example, the science of electricity is based on

the natural phenomenon of a flow of electrically

charged particles.

However,

• the current approach to AI doesn't define a natural

source of intelligence. So, AI has no natural foundation;

• and knowledge technology fails to understand the

meaning of the user, because it isn't based on a natural

source of Semantics.



A mature science integrates all its disciplines

For example: The basic rules of electricity apply from

micro-electronics to high-voltage systems.

However, the disciplines of AI / knowledge technology

are separate “islands”. For example:

Ontology (automated reasoning) is based on formal

(=cryptic) language and Natural Language Processing

(NLP) is based on natural language. So, Ontology and

NLP have no common foundation, and therefore they

are incompatible.



Summary:

• AI is not defined unambiguously;

• AI has no natural foundation;

• AI doesn't integrates all its disciplines.

Conclusion:

The current approach to AI and knowledge technology

is not fundamental, and therefore not scientific.



From a positive point of view:

The field of AI and knowledge technology is still

completely open, and provides an unprecedented

opportunity for those who will succeed to develop a

scientific foundation for AI and knowledge technology.



• But what is the definition of intelligence and semantics?

• How can a fundamental base then be developed for

AI and knowledge technology?

• And how can intelligence and semantics be

implemented on a natural foundation?



Any questions so far?




Demo 4

Questions 5

Introduction 1



Using a metaphor: the dream to fly like a bird.

The dream of flight had:

• an experimental phase;

• and a fundamental phase.



The experimental approach to the dream of flight

For a many centuries, people tried to fly like a bird.

“Inspired by nature”, a few covered themselves with

feathers or strapped a wing construction to their arms

in the hope they could fly. But they failed, because

feathers and flapping wings aren't essential for flight.

Proof: Airplanes can fly, but they no feathers, nor

flapping wings.

Also the current approach to AI is “inspired by nature”.



The fundamental approach to the dream of flight

The Wright brothers however, experimented for years

to understand the essence of flight:

• Lift (air lifting);

• Thrust (propulsion);

• Drag;

• Weight (including center of gravity).

(No feathers, nor flapping wings.)



The fundamental approach to the dream of intelligent

machines / information systems

How can we achieve a fundamental approach for the

dream of intelligent machines / information systems,

like the Wright brothers did for the dream of flight?



Characteristics of a mature science:

• A mature science is defined unambiguously;

• A mature science is based on a natural foundation;

• A mature science integrates all its disciplines.





So, let's define:

• Intelligence (for Artificial Intelligence);

• Semantics (for knowledge technology).




The definition of intelligence (for Artificial Intelligence):

“Intelligence is a naturally occurring phenomenon,

which can be described as the capability of autonomously

associating (combining),

discriminating (differentiating, distinguishing),

learning (from mistakes),

planning and

predicting,

with the aim to reach a predefined goal.”




The definition of semantics (for knowledge technology):

“Semantics is a naturally occurring phenomenon,

which can be described as the capability of autonomously

associating (combining) and

discriminating (differentiating, distinguishing),

with the aim to reach a predefined goal.”

So, Semantics is actually a subset of Intelligence,

and knowledge technology is a subset of AI.




“Intelligence is a naturally occurring phenomenon …”

So, an intelligent system should derive its intelligence

from a natural source – rather than artificially created

sources, like semantic vocabularies and statistics.




“Intelligence […] can be described as the capability

of autonomously […]”

But what is autonomy?




To illustrate autonomy by a 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.”

So, an intelligent system should be able “to fish” –

e.g. building its own semantics autonomously from a

natural source – rather than being fed constantly from an

artificial source, like semantic vocabularies and statistics.



Thinknowlogy is able “to fish”:

by deriving its intelligence autonomously from a

natural source (grammar):

There are rules of intelligence contained within grammar.

(will be explained later in this presentation),

by which it is able to preserve the meaning at

natural language level throughout the system.




A mature science integrates all its disciplines

Proof of the pudding!

Thinknowlogy integrates several disciplines with natural

language:

• Programming in natural language;

• Reasoning in natural language;

• Intelligent answering of “is” questions in full sentences;

• Detecting some cases of semantic ambiguity.



What is Thinknowlogy?

Thinknowlogy is grammar-based software, designed

to utilize the intelligence contained within grammar,

in order to create intelligence

through natural language in software,

which is demonstrated by:




• Reasoning in natural language

(=reasoning expressed in natural language):

• drawing conclusions,

• making assumptions

(with self-adjusting level of uncertainty),

• asking questions (about gaps in the knowledge),

• detecting conflicts in the knowledge;

• Building semantics autonomously (no vocabularies):

• detecting of some cases of semantic ambiguity;

• Intelligent answering of “is” questions

(by providing alternative answers as well).



Demo – Programming in natural language:

• Playing the game Connect-Four only by reading

the playing rules, written as readable language;

• Greeting program written as readable text;

• Solving the Tower of Hanoi problem with the rules

written as readable text.

Remark: With some additional development,

the system will be able to execute business rules,

written as readable text.



Demo – Reasoning in natural language:

Most reasoners work at the level of linked keywords.

And therefore, they are unable to produce in sentences

in natural language.

The reasoning capability at natural language level of

Thinknowlogy is worldwide unique:

• Drawing conclusions;

• Making assumptions (self-adjusting level of uncertainty);

• Asking questions (about gaps in the knowledge);

• Detecting conflicts in the knowledge.



Demo – Intelligent answering of “is” questions

• Most scientific Question Answering (QA) systems are

only able to provide a keyword as answer;

• Other QA systems – like chatbots – are able to answer

in full sentences. However all its sentences are written

by humans.

Only Thinknowlogy is able to answer with knowledge

(either from humans or from the reasoner) in a full

sentences. And in case no answer could be found,

it tries to find an alternative, that could answer the

question of the user.



Demo – Detecting cases of Semantic Ambiguity

Disambiguation (solving ambiguity) is the biggest problem

in Natural Language Processing.

There are two types of ambiguity:

• Grammatical ambiguity, like:

“The man hit the dog with a stick”.

(Did the man hit with a stick, or had the dog a stick?);

• Semantic ambiguity, like “former president Bush”.

George H. W. Bush, or his son George W. Bush?



Demo – Detecting cases of Semantic Ambiguity

Several techniques are developed to solve semantic

ambiguity at keyword level.

However, only Thinknowlogy is able to detect

– not yet to solve – some cases of semantic ambiguity

at natural language level.

From 2013 I will start to implement solving some cases of

semantic ambiguity to prove my approach in fundamental.



From 2013 – Solving cases of Semantic Ambiguity

When a sentence is entered and its context (semantics)

is not clear, the system can either:

• use deduction to determine which context is meant by

the user;

• make an assumption, when the meant context cannot

be determined, but when it is quite obvious;

• or ask a question, when the system has no clue about

the context.


How to build semantics autonomously?

• By autonomously associating (combining) the

new information with existing information,

• and by autonomously discriminating (differentiating,

distinguishing) the new information from existing

information,

using the rules of intelligence contained within grammar.



Some rules of intelligence contained with grammar

to associate and discriminate knowledge:

• Indefinite article (“a”) represents a definition, a static

structure;

• Definite article (“the”) represents a dynamic structure,

similar to a variable in a programming language;

• A series of words are all from the same word type:

noun, proper noun, adjective, etc;

• Conjunction “or” marks a choice;

• Conjunctions like “and” and “or” mark a set (an

association), and distinguishes from other sets.





• Indefinite article (“a”) represents a definition, a static

structure: “A son is a male.” (no assignment);

• Definite article (“the”) represents a dynamic structure,

similar to the assignment of a variable in programming

languages:

“John is the father of Paul, Joe and Laura.”.

A static structure can be derived from a dynamic

structure: “John is a father.”.





• A series of words are all from the same word type:

noun, proper noun, adjective, etc., like in:

“0, 1, 2, 3, 4, 5, 6, 7, 8 and 9 are numbers.”.

People will get confused by a list – a series of words –

having different grammatical word types, like in:

“0, 1, 2, 3, 4, 5, 6, 7, 8 and blue are numbers.”.





• Conjunction “or” marks a choice, like in:

“A person is a man or a woman.”.

Using such a choice is an act of intelligence:

When given the specification: “John is a person.”, a

substitution of both sentences will result in the question:

“Is John a man or a woman?”.





• Conjunctions (like “and” and “or”) associate a set:

“Humans have two arms and two legs.”

and

“Humans have constructed roads and bridges.”.

However, the set of “arms” and “legs” doesn't belong to

the set of “roads” and “bridges”. So, by associating

one set, at the same time we make a distinction with

other sets, which is an act of intelligence.



When will Thinknowlogy deliver revenue?

The current approach to knowledge technology is far

from fundamental. So, we need to redo basically most

AI research done in the last 60 years:

I estimate an additional 100 man years (10 developers

during 10 years) is required to develop Thinknowlogy

as a foundation for knowledge technology. And it will be

difficult to find developers able to think outside the box.



When will Thinknowlogy deliver revenue?

However, after those 10 years of development,

Thinknowlogy will deliver a monopoly position

in knowledge technology,

with a situation comparable to men covered with

feathers and having complex wing constructions

strapped to their arms, against a fundamentally

designed airplane of the Wright brothers.




Demo 4

Questions 5

Introduction 1



Demo 4

Thinknowlogy integrates several disciplines with

natural language:


• Reasoning in natural language;

• Intelligent answering of “is” questions in full sentences;

• Detecting some cases of semantic ambiguity.


Thinknowlogy at startup


Programming in natural language:

Reading the playing rules of Connect-Four as text



The playing rules of Connect-Four are executed



The system has gains knowledge and keeps track of history



Greeting program written as readable text (1)



Greeting program written as readable text (2)



Reading the rules to solve the Tower of Hanoi problem



The rules of the Tower of Hanoi problem are executed


Reasoning in natural language:

Reading definitions about a family as readable text



Autonomous conclusions, assumptions and questions



When questions are answered by the user



Another reasoning example (1)



Another reasoning example (2)



The assumptions have a self-adjusting level of uncertainty



Correcting an invalidated assumption



Justification report for the self-generated knowledge



When conflicting knowledge is entered


Question Answering:

Intelligent answering of “is” questions by providing alternatives


Detecting some cases of semantic ambiguity:

City Boston – Multiple instances



President Bush – Re-occurrence or multiple instances? (1)



President Bush – Re-occurrence or multiple instances? (2)



Demo 4

Questions 5

Introduction 1


thinknowlogy a fundamental approach to ai knowledge technology

Technology