Deep Tensor®

“Machines are beneficial to the extent that their actions can be expected to achieve our objectives.” – Russel, S. Human compatible: Artificial intelligence and the problem of control.

What should be the basis for AI trust?

Explanation in AI systems is considered to be critical across all areas where machine learning is used.
In the case of a recommended medical treatment or a rejected application for a mortgage loan,
we may be entitled to clear explanations of how we arrived at a particular result.

FUJITSU Deep Tensor ® FOR EXPLAINABLE AI

How Fujitsu Technology elicits New Insights from Graph Data

Knowledge Graph is used to present the basis for results obtained by AI.

DT identifies the factors (partial graphs) that had a significant influence on an inference and coordinates these with partial graphs from a knowledge graph, building a series of pieces of information in the form of connections in the knowledge graph as the basis for the findings.

Connecting inferences derived by Deep Tensor to Knowledge Graph, DT enables to understand the reasons behind AI-generated findings and to make them explainable.

So emerges the urgent need to make results and machine decisions transparent, understandable and explainable.

Our Partner, Fujitsu Laboratories developed Deep Tensor (DT), that provides users with information on the system’s prediction with an effective explanation for the AI system’s behavior.

DT combines the proprietary AI technology Deep Tensor, a deep neural network that is especially suited to datasets with meaningful graph-like properties with Knowledge Graph (Neo4j).

Deep Tensor converts graph-structured data to a form of mathematical expression called a tensor and performs deep learning to achieve the highly accurate findings.

The technology is also able to run a reverse search of the deep-learning output to identify factors that had a significant impact on the results. The knowledge graph consists of a huge amount of graph data that includes all sorts of knowledge

Fujitsu Partnership

is part of our path to new growth.
We expand and enhance our offer for our Neo4j customers, thanks to a graph-based approach to artificial intelligence.

Neo4j graph database provides human-understandable representation of multi- dimensional data.

Accessible Deep Tensor integrates Deep Tensor with Neo4j, providing an end-to-end platform for Machine Learning

The common idea of the potential of Graph AI to drive business decision-making processes was the start of a strong alliance between the two companies and the birth of Galileo XAI.

The best preparation for good work tomorrow is to do good work today.

Elbert Hubbard

We’ve gotten used to having machine intelligence that we can carry around with us

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