Introduction
Science relies on the assumption that we live in an ordered universe that is subject to exact, deterministic, and consistent laws of nature. So, everything in nature is bound by natural laws and proceeds according to natural laws.
Natural laws, logic, and natural phenomena are investigated using fundamental science (Basic Research):
• Natural reasoning requires both natural intelligence and natural language;
• Intelligence and language are natural phenomena;
• Natural phenomena obey the laws of nature;
• Laws of nature and logic are investigated using fundamental science (Basic Research).
However, the field of Artificial Intelligence (AI) and Natural Language Processing (NLP) — in a broad sense — is investigated using behavioral or cognitive science. As such, the field of AI and NLP is limited to mimic behavior, while mimicking a hen’s — chicken’s — behavior will not produce a single egg. As a consequence, the field of AI / NLP is not naturally intelligent.
The examples described in this document do not exceed primary school level. However, scientists are unable to describe — let alone implement — these childishly simple deterministic linguistic logic. I call this linguistic logic: natural reasoning constructs.
Problem description 1: Reasoning in the past tense
Aristotle described syllogisms — natural reasoning constructs — almost 2,400 years ago. The most well-known example:
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Given:
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“All philosophers are mortal.”
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“Socrates is a philosopher.”
Logical conclusion:
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“Socrates is mortal.”
However, at the time Aristotle described the natural reasoning example mentioned above, Socrates was already dead, as the ultimate proof of his morality. So actually, Aristotle should have used the past tense form in his example regarding Socrates:
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Given:
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“All philosophers are mortal.”
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“Socrates was a philosopher.”
Logical conclusion:
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“Socrates was mortal.”
The tense of a verb tells us about the state of the involved statement:
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“Socrates is a philosopher” tells us that Socrates is still alive;
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“Socrates was a philosopher” tells us that Socrates is no longer among the living.
Regarding the conclusion:
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“Socrates is mortal” tells us that the death of Socrates is inevitable, but that his mortality isn't proven yet by hard evidence;
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“Socrates was mortal” tells us that his mortality is proven by hard evidence.
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In Block 5: Past tense reasoning, a natural reasoning construct is proposed.
Problem description 2: Possessive reasoning
The field of electromagnetism is a fundamental science because it closes the circle:
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We can convert light to electricity, and we can convert electricity back to light;
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.We can convert motion—via magnetism—to electricity, and convert electricity—via magnetism—back to motion
In the same way, natural reasoning closes the loop for natural language and natural intelligence, without any human interaction or engineered techniques, by means of generic techniques:
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Readable sentences can be automatically converted into natural logic (the natural intelligence of language) using generic techniques,
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the results of the reasoning process can be automatically converted to readable — word-by-word constructed — sentences, using generic techniques.
In primary school we all learned a similar sum, given:
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“John has 3 apples.”
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“Peter has 4 apples.”
The school teacher then wrote:
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3 apples + 4 apples = 7 apples
However, the result of the sum — “7 apples” — lacks the reference to “John and Peter”. So, the result of this sum is insufficient to construct the following readable sentence:
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“John and Peter have 7 apples together.”
Hopefully, mathematicians will come to the rescue, by closing the circle scientifically:
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J = 3
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P = 4
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J + P = 7
Unfortunately, the mathematical result “J + P = 7” lacks the reference to “apples”. So, also the result of the algebra is insufficient to automatically construct readable sentence:
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“John and Peter have 7 apples together.”
Lacking a generic solution, it would require either human interaction, or an engineered solution—a specific solution to a specific problem. Therefore, AI / NLP is not a science, but a field of engineering.
In Block 3: Grouping of knowledge (without relation), a natural reasoning construct is proposed to solve the problem mentioned above.
Problem description 3: Possessive reasoning
Possessive reasoning—reasoning using the possessive imperative “have”—is not naturally supported by logic/algebra:
Given:
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“Paul is a son of John.”
Logical conclusion:
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“John has a son called Paul.”
Nor the other way around:
Given:
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“John has a son called Paul.”
Logical conclusion:
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“Paul is a son of John.”
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​In Block 1: Direct conversions, a natural reasoning construct is proposed.
Problem description 4: Generation of questions
Algebra describes the Exclusive OR (XOR) function, while CNL reasoners don't implement its linguistic equivalent: conjunction “or”. CNL reasoners are therefore unable to generate the following question:
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Given:
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“Every person is a man or a woman.”
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“Addison is a person.”
Logical question:
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“Is Addison a man or a woman?”
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In Block 6: Detection of a conflict and generation of a question, a natural reasoning construct is proposed.
Challenge
It may seem like Large Language Models (LLM) can solve the aforementioned reasoning problems. However, LLMs only have a limited, engineered reasoning capability. When reasoning problems are combined, LLMs will start to lose context.
Therefore, I defy anyone to beat the simplest results of my reasoner in a generic (=scientific) way, under the same strict preconditions as my system::
- Readable sentences (with restricted grammar, Controlled Natural Language),
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automatically converted to natural logic (the natural intelligence of language) using generic techniques,
- with the results of the reasoning process expressed in readable, autonomously — word-by-word — constructed sentences, using generic techniques,
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in multiple languages (*),
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without programmed or trained knowledge,
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without human-written output sentences,
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without extensive word lists,
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published — free of charge — as open-source software, just like my software is published as open-source.​
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(*) Logic is (almost) language-independent. The logic of my natural reasoner is configured for five languages: English, Spanish, French, Dutch, and Chinese.
The rules of this challenge
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Below are 9 blocks. In the first 7 blocks, I describe the simplest natural reasoning constructs of my system. Your implementation should deliver the results of at least one of the mentioned blocks. In the last 2 blocks I only show the results of my reasoning system;
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Your implementation should not contain any knowledge after startup. Instead, the system should derive the knowledge from the input sentences of the mentioned examples, from readable sentences, via a generic algorithm, back to readable sentences;
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Preferably, the nouns and proper names used should not be known in advance. I use grammar definitions and an algorithm instead of words lists;
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Your implementation should be set up as generically as possible so that all examples of this challenge can be integrated into one single system;
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The screenshots of my reasoning system show that various natural reasoning constructs reinforce each other. At the end of each of the first 7 blocks a screenshot has been added, to show how my system processes the mentioned examples;
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Your implementation should be published as open source software, so that the functionality is clear, just like my software is published as open-source software;
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In case your results are slightly different, you should explain why your system reacts differently;
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It is an ongoing challenge until all mentioned blocks have been implemented by others;
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Only the most recent document version is valid, because I am still developing my system, including this challenge document;
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I will be judging your implementation
A small reward:
I am offering a small reward per block to the first person who implements that particular block under the stated conditions. For the first 7 blocks, I am offering €1,000 per block. For the last two blocks, €1,500 per block. So €10,000 in total.
You can contact me via LinkedIn and this website.
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Scientific challenge