TOKYO, Sept 20, 2017 – Fujitsu Limited and Fujitsu Laboratories Ltd. today announced that they have developed technology that shows the reason and academic basis for findings from AI that have been trained on large volumes of data. This is done by connecting the proprietary AI technology Deep Tensor(1), which performs machine learning on graph-structured data, with graph-structured knowledge bases called a knowledge graph(2), which brings together expert knowledge such as academic literature.
The increased prevalence of machine learning technologies such as deep learning, in which a machine finds characteristics of data on its own after being trained on large volumes of data, has led to issues with the application of this technology to mission-critical fields such as medicine and finance. This is because there are questions of accountability regarding experts’ AI-based findings, as it is difficult for humans to evaluate the reason behind the findings gained using these technologies.
Fujitsu and Fujitsu Laboratories have now successfully developed technology that shows the reason and basis for an AI inference by connecting the results of Deep Tensor findings with knowledge stored in a knowledge graph.
With this technology, experts can confirm whether the AI’s findings merit trust, based on expert knowledge, such as academic literature, obtained as the basis and reason for the findings of AI. The results can also be used as clues to gain new insights, creating a world where experts cooperate with AI to resolve problems.
This technology will be commercialized as part of Fujitsu Human Centric AI Zinrai, the company’s AI framework, in fiscal 2018.
Development Background
The development of machine learning technology in recent years has been remarkable, producing results that surpass humans in certain areas. For example, deep learning modeled on the human biological neural network offers high performance in recognition and classification, but because even experts and the developers themselves cannot explain why the AI produced a certain result, it has been called a black-box AI. In light of this property, there are concerns that it will prevent application of this technology to mission-critical areas which require accountability with regard to conclusions from experts using AI, so there has been anticipation of the development of technology that could add an explanatory capability to black-box AI.
Fujitsu Laboratories developed Deep Tensor, which learns from graph-structured data capable of describing complicated phenomena, based on deep learning technology which is a kind of machine learning. It has achieved highly accurate inferences in fields such as security. In addition, Fujitsu Laboratories have developed natural language processing technology that extracts knowledge from text through text data analysis, as well as Linked Open Data (LOD)(3) technology, which creates a knowledgebase from data on the web, offering a free service called LOD4ALL.
Through systematization of these technologies, Fujitsu Laboratories have built a knowledge graph, which is a graph-structured knowledgebase with which a computer can handle the meaning of data and surrounding knowledge.
Issues
A major feature of black-box AI is its ability to automatically infer classification for unknown input data just by being trained on a large volume of data. At the same time, however, the inability to explain the reason behind inferences from the learning algorithm is a significant issue. In recent years, research has been conducted around the world to identify parts of the input data that have a significant impact on inferences, but it has only reached the level of being able to explain whether a particular part of the image influenced the recognition results in image recognition.
In addition, in order for experts to work with AI to resolve problems, it has been necessary for them to check sources such as academic literature to see if the AI’s findings were correct. This is particularly true with regard to phenomena where relationships are only partially understood, necessitating experts to find the basis supporting these findings and link that information together to understand it.
About the Newly Developed Technology
Now, Fujitsu and Fujitsu Laboratories have developed technology to show the reason and basis for Deep Tensor findings by fusing Deep Tensor with a knowledge graph built from a variety of outside data (Figure 1). This technology 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.
