Lifelong and Continual Learning

Learn as "Humans" do for Artificial General Intelligence (AGI)

Second Edition: "Lifelong Machine Learning." by Z. Chen and B. Liu, Morgan & Claypool, August 2018 (1st edition, 2016).
  • Added three new chapters: (4) Continual Learning and Catastrophic Forgetting, (5) Open-world Learning, (8) Continuous Knowledge Learning in Chatbots
  • Introduced the concept of learning on the job or learning while working.
  • Updated and/or reorganized the other chapters.
  • Download the first edition, Lifelong Machine Learning, Nov 2016.

  • Continual Learning Dialogue Systems - Learning on the Job after Model Deployment. Tutorial @ IJCAI-2021, August 21-26, 2021, Montreal, Canada.
    Continual Learning Dialogue Systems - Learning after Model Deployment. Invited talk @ ICLR-21 Workshop on Neural Conversational AI, May 7, 2021.
    Learning on the Job in the Open World. Invited talk@ ICML-2020 Workshop on Continual Learning, July 17, 2020.
    Lifelong Machine Learning Tutorial. Title: lifelong machine learning and computer reading the Web, KDD-2016, August 13-17, 2016, San Francisco, USA.
    Lifelong Machine Learning Tutorial, IJCAI-2015, July 25-31, 2015, Buenos Aires, Argentina.
    A Podcast: "Machines that Learn Like Humans" by my former student Zhiyuan Chen and Francesco Gadaleta (host).
    A Resource Site maintained by Eric Eaton's group
    DARPA program: Lifelong Learning Machines (L2M), 3/16/2017

    The classic machine learning paradigm learns in isolation. Given a dataset, a learning algorithm is applied to a dataset to produce a model without considering any previously learned knowledge. This paradigm needs a large number of training examples and is only suitable for well-defined and narrow tasks in closed environments. Looking ahead, to deal with these limitations and to learn more like humans, I believe that it is necessary to do lifelong machine learning or simply lifelong learning (also called continual learning or even continuous learning), which tries to mimic "human learning" to build a lifelong learning machine. The key characteristic of "human learning" is the continual learning and adaptation to new environments - we accumulate the knowledge gained in the past and use the knowledge to help future learning and problem solving with possible adaptations. Ideally, it should also be able to discover new tasks and learn on the job in open environments in a self-supervised manner. Without the lifelong learning capability, AI systems will probably never be truly intelligent. learning machine or agent to continually learn and accumulate knowledge, and to become more and more knowledgeable and better and better at learning.

    Human learning is very different: I believe that no human being has ever been given 1000 positive and 1000 negative documents (or images) and asked to learn a text classifier. As we have accumulated so much knowledge in the past and understand it, we can usually learn with little effort and few examples. If we don't have the accumulated knowledge, even if we are given 2000 training examples, it is very hard to learn manually. For example, I don't understand Arabic. If you give me 2000 Arabic documents and ask me to build a classifier, I cannot do it. But that is exactly what current machine learning is doing. That is not how humans learn.

    Our Work

    Some of my work uses sentiment analysis (SA) tasks and data because it is the problems that I encountered in a SA startup that motivated me to work on lifelong learning or continual learning. SA is very hard to scale-up without lifelong learning.

    Related Learning Paradigms: Transfer learning, multitask learning, and lifelong learning


             TextBook: Zhiyuan Chen and Bing Liu. Lifelong Machine Learning. Morgan & Claypool, 2018 (2nd edition), 2016 (1st edition).

    Created on Sep 24, 2014 by Bing Liu.