About Krino

“Krino” from ancient Greek: ‘to distinguish, judge, criticize.’

Schema to explain the way Krino works. The user first gives it a background knowledge input. 
Then, the user has 3 possibilities : 
1- To give Krino assumptions. Krino use a casual model to verify the assumption. Then Krino uses it to make testable implications, or to see if a query can be answered.
2-To give Krino a query. If Krino cannot answer it, it asks for a new query or new assumptions.
If Kirno can answer it, estimands are delivered to the Data part of Krino, a recipe for Answering a query.)
3- To give Krino data. Krino make a linguistic and statistical estimation of it, and use it to answer the query of the user with an Estimate.
How Krino thinks (high-level abstraction)

INSPECTABLE

INSPECTABLE

To create an inspectable, explainable truth engine that communicates in any natural language.

VERIFIABLE

VERIFIABLE

To make the verification of information accessible and affordable to a wide range of users.

ADAPTIVE

ADAPTIVE

To provide the users with the possibility to enhance, upgrade and improve their cognitive environment.

INQUISITIVE

INQUISITIVE

To support argumentation for any use case or context while maintaining unbiasedness and integrity.

CHALLENGE

Krino will combine the following fields of expertise:

Linguistics
in particular the framework of Constructive Adpositional Grammars (CAG)

Argumentation
in particular the argument classification framework of the Periodic Table of Arguments (PTA).

Causality
Causal philosophy of science to categorize types of arguments (Causality).

Mathematics
in particular sheaf theory and topos theory, to support and formalize the use of CAG.

Engineering
high-performance database plus implementations of A.N. Whitehead’s ontology and Judea Pearl’s mathematization of causality in an object-oriented programming language (Java or C++).

Knowledge
for example to the Oxford English Dictionary (OED) and Historical Thesaurus of the OED (HTOED).