To do this, software programs in these systems calculate predictive relationships from massive amounts of data. The systems identify these predictive relationships using advanced algorithms -- a set of rules for solving math problems -- and "training data." This data is then used to construct the models and features that enable a system to determine the latest best-seller you wish to read or to predict the likelihood of rain next week.
This intricate process means that a piece of raw data often goes through a series of computations in a system. The computations and information derived by the system from that data together form a complex propagation network called the data's "lineage." The term was coined by Yinzhi Cao, an assistant professor of computer science and engineering, and his colleague, Junfeng Yang of Columbia University, who are pioneering a novel approach to make learning systems forget.
Considering how important this concept is to increasing security and protecting privacy, Cao and Yang believe that easy adoption of forgetting systems will be increasingly in demand. The two researchers have developed a way to do it faster and more effectively than can be done using current methods.
Their concept, called "machine unlearning," is so promising that Cao and Yang have been awarded a four-year, $1.2 million National Science Foundation grant to develop the approach.
"Effective forgetting systems must be able to let users specify the data to forget with different levels of granularity," said Cao, a principal investigator on the project. "These systems must remove the data and undo its effects so that all future operations run as if the data never existed."
- More Here
This intricate process means that a piece of raw data often goes through a series of computations in a system. The computations and information derived by the system from that data together form a complex propagation network called the data's "lineage." The term was coined by Yinzhi Cao, an assistant professor of computer science and engineering, and his colleague, Junfeng Yang of Columbia University, who are pioneering a novel approach to make learning systems forget.
Considering how important this concept is to increasing security and protecting privacy, Cao and Yang believe that easy adoption of forgetting systems will be increasingly in demand. The two researchers have developed a way to do it faster and more effectively than can be done using current methods.
Their concept, called "machine unlearning," is so promising that Cao and Yang have been awarded a four-year, $1.2 million National Science Foundation grant to develop the approach.
"Effective forgetting systems must be able to let users specify the data to forget with different levels of granularity," said Cao, a principal investigator on the project. "These systems must remove the data and undo its effects so that all future operations run as if the data never existed."
- More Here
No comments:
Post a Comment