In a recent trial demonstration, Fujitsu’s researchers succeeded in rediscovering the gene of interest in the colorectal cancer classification, offering a key to the development of a treatment plan individualised for each patient
Fujitsu has developed a technology to discover the characteristic causality of individual pieces of data by quickly extracting all the groups of data that have a common correlation from an entire dataset, and evaluating the causality of each group of data to find the characteristic causality. The technology specifically addresses the need to isolate and identify characteristics from data in different real-world scenarios.
While the use of AI to tackle real-world problems continues to accelerate, certain challenges remain in applying AI and machine learning technologies to resolve challenges in a variety of fields, including medicine and marketing. To identify the key drivers of the problem to be solved and develop a strategy, for instance, it’s necessary to not only look at the correlation between attributes A and B, but also at the causal relationship between A and B, such as “A is the cause of B.”
To date, data analysis research has led to the development of techniques for estimating the common cause-and-effect relationship of data. In addition, estimating the characteristic cause-and-effect relationship of each piece of data is needed to solve many real problems.
For example, in the case of cancer treatment in the medical field, many cancer patients have been identified by the expression of unique genes that affect the disease state of cancer. In order to devise an appropriate treatment plan for individual patients, therefore, doctors must identify genes that are unique to each cancer patient, not genes that are common to all cancer patients.
In the case of promotions in marketing, each customer within a larger group has a distinctive characteristic that leads to their purchase, and in order to plan appropriate outreach for individual customers, it becomes necessary to identify a characteristic, motivational cause for each customer, not a cause common to all customers.
In this way, there has been a need for new techniques for estimating the characteristic cause-and-effect relationship of each piece of data to solve many real problems.
The original article can be found here.
In the field of AI application with Cause-and-Effect, Professor Judea Pearl is a pioneer for developing a theory of causal and counterfactual inference based on structural models. In 2011, Professor Pearl won the Turing Award, computer science’s highest honor, for “fundamental contributions to artificial intelligence through the development of a calculus of probabilistic and causal reasoning.” In 2020, Michael Dukakis Institute also awarded Professor Pearl as World Leader in AI World Society (AIWS.net) for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Judea is a Mentor of AIWS.net and Head of Modern Causal Inference section, which is one of important AIWS.net topics on AI Ethics.