Explainable AI from Kyndi is Funded

Quite a claim, like the components I see mentioned.  More explanation, logic, graphs, semantics ....  we explored and used them all,  is it enough for required transparency?  And how will such AI and explanation be maintained in changing contexts?   And resulting risks?

Kyndi Platform funding:

....  EXPLAINABLE ARTIFICIAL INTELLIGENCE ...

Kyndi is building the first Explainable AI platform for government, financial services, and life sciences. Our solutions are the antithesis of the old AI “black box.”
KYNDI ADDS $20M TO EXPAND TEAM AND ACCELERATE GROWTH
The Series B round was led by Intel Capital, with participation from UL Ventures, PivotNorth Capital, and existing investors.
THE KYNDI AI PLATFORM
Kyndi is an artificial intelligence company that’s building the first Explainable AI product and Intelligent Process Automation software for government, pharmaceutical, and financial services organizations. .... 

The Kyndi AI Platform uses machine learning to streamline regulated business processes and offer auditable AI systems for enterprises and government. Kyndi’s product exists because Deep Learning is a “black box” and cannot be used in regulated industries where organizations are required to explain the reasons for any decision.

Our platform uses a novel approach to AI, unifying probabilistic and logical methods. This enables organizations to analyze massive amounts of data to create actionable knowledge significantly faster and without having to sacrifice explainability. Kyndi’s Explainable AI™ Platform supports the following solutions: Intelligence, Defense, Compliance (i.e., for financial services and healthcare), and Research. Crucially, the Kyndi AI Platform also helps to mitigate the human bias that can arise in the process of extracting knowledge and answers from data.

EXPLAINABILITY
Explainable artificial intelligence achieves the level of trust that is so important for accelerated growth and acceptance of this revolutionary technology.
AI cannot be a “black box,” as it so often is today. Explainable AI™ means that our software’s reasoning is apparent to the user, and that the system can explain its rationale. This visibility allows you to have confidence in the system’s outputs, be aware of any uncertainties, anticipate how the software will work in the future, and know how to improve the system. Such knowledge is essential to confident analysis and decision making. Explainability is at the core of Kyndi’s products and solutions.

NATURAL LANGUAGE PROCESSING (NLP)
With Kyndi’s AI products, knowledge is accumulated and transferred through written forms of natural language.

Kyndi has developed a unique and effective approach to NLP to automate and scale knowledge consumption. First, our NLP solution tokenizes text and identifies parts of speech and sentence structure. Next, we identify named entities with real-world references and compute semantic distances between words using our proprietary Semantic Distance Field Model to show us how strongly any two entities are related. Semantic parsing and Relation Extraction allows us to formally name this relationship where both the entities and relationships create a proto-ontology that encodes and condenses the meaning of a document, a collection of documents, or a whole domain.

With this clear view of ideas, concepts, and relationships, we build a knowledge graph that scales with your business and that lets you query your collection and retrieve the information you want, not just the words you used to ask the question.

KNOWLEDGE GRAPHS
Once Kyndi’s NLP pipeline identifies the structure and contents of your data we create a graph representation, no matter the size.

With each node in the graph identifying an entity, the connections between the nodes are semantic vectors that signify the relationships and significance between entities. The feature-rich graph enables you to conduct a quick and accurate analysis by matching fragments and returning details relevant to your search queries. To achieve this functionality Kyndi has developed industry-leading technology that allows for sub-graph matching based on cognitive signatures.  ..... "