IJCAI-2015 Tutorial Slides --- (My Lifelong Learning Research Page)
This tutorial covers the important problem and existing techniques of lifelong machine learning (or lifelong learning) and discusses opportunities and challenges of big data for lifelong machine learning. The tutorial has been given at the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), July 25-31, 2015, Buenos Aires, Argentina.
Lifelong machine learning (LML) aims to design and develop computational systems and algorithms that learn as humans do, i.e., retaining the results learned in the past, abstracting knowledge from them, and using the knowledge to help future learning and problem solving. The rationale is that when faced with a new situation, we humans use our previous experience and knowledge to help deal with and learn from the new situation. We learn and accumulate knowledge continuously. It is essential to incorporate this capability in an AI system to make it versatile, holistic, and intelligent. Current research in lifelong learning is still in its infancy. Through this tutorial we would like to introduce the existing techniques and to encourage researchers to work on this problem. We also want to highlight the tremendous opportunities offered by big data to make major progresses in lifelong learning and in AI.
Lifelong Learning vs. Transfer Learning vs. Multitask Learning
In the tutorial, several people asked about the differences of transfer learning, multitask learning, and lifelong learning. The descriptions of these concepts in the literature are quite confusing as they are closely related and overlap. In a paper about lifelong learning, the authors may propose a technique that is actually for multitask learning or transfer learning. Let me give my view. But first, let me state the general definitions given in the existing literature based on my understanding.
My View on Lifelong Learning: I like the more general definition of lifelong learning above. The relevant prior knowledge may be generated from the past n-1 tasks with or without considering the nth task data. But I would like to stress the importance of big data and the growth of the number of tasks for lifelong learning. That is, for lifelong learning to be meaningful, the number of past tasks must be large (i.e., n – 1 should be large) and growing so that a large amount of knowledge can be accumulated. The future task can just find and use any relevant pieces of knowledge learned in the past to help learning the new task t(n). In this way, the learning becomes truly lifelong and autonomous. When n = 2, it is the mainstream transfer learning or domain adaptation, which cannot be called lifelong learning because it does not have a sequence of past tasks and thus not lifelong. Also the human user has to manually identify two tasks that are very similar to each other in order to perform meaningful transfer. Based on this view, multitask learning is not lifelong learning either because lifelong leanring does not jointly optimize all new and past tasks.
Date and Place
Date: July 25 (Afternoon), 2015.
Place: IJCAI'15, Room 438 in the New Building of Facultad de Ciencias Económicas.
Researchers, graduate students, and practitioners who are interested in machine learning, big data and Artificial Intelligence. The tutorial will particularly benefit people who intend to develop machine learning techniques and applications that can keep improving themselves after seeing more and diverse data in order to achieve “real” intelligence.
Basic knowledge of machine learning or data mining.