Open-World Machine Learning and Classification

(Open-world Recognition, Open Set Recognition, Open-world AI)
A form of Lifelong Learning


Second Edition: "Lifelong Machine Learning." by Z. Chen and B. Liu, Morgan & Claypool, August 2018 (1st edition, 2016)

Learning on the Job in the Open World. Invited talk given at the Continual Learning Workshop @ ICML-2020, July 17, 2020.

Motivation: Sooner or later, AI agents will need to explore and learn by themsleves in the real world. They cannot forever depend on manually labeled data. The real world is open and dynamic, and full of unknowns. AI agents must be able to detect the unknowns and learn them in a self-supervised manner. They should not make the closed-world assumption any more.

Open world learning (OWL) (a.k.a. open world recognition or classification, or open-world AI) is getting increasingly important as the learning agent is increasingly working in or facing the real-world open and dynamic environment, e.g., chatbot and self-driving car, where the agent cannot assume or expect what it will see in the real-world contains only what it has learned previously. For example, a chatbot cannot assume that it knows everything that a user may say. A self-driving car cannot assume that the real-world has only things that it has seen and learned before. The core of open-world learning or open-world AI is about recognizing unknowns and learning them so that the AI agent will become more and more knowledgeable.

Classic machine learning makes the closed world assumption, i.e., the classes that the agent sees in training are what it will see in testing (no new objects or classes can appear in testing) (Fei and Liu 2016). A more realistic scenario is to expect unseen classes during testing (open world). In this case, the goal is to design a learning algorithm that can classify data of the known/seen classes into their respective classes and also to reject/detect instances from unknown/unseen classes. This problem is called open-world learning (or open-world classification). Apart from detecting the unseen classes, open-world learning should also incrementally or continually learn the new classes.

Tasks of open-world learning (OWL)

Open-world learning in dialogue systems: We have been working on this topic for the past two years because in dialogues unknown or new things happen all the time. See our 2020 and 2021 papers below.

In the process, the system learns and accumulates more and more knowledge (Fei et al. 2016). The learner is self-motivated and it knows what it does and does not know. With intelligent systems such as chatbots and self-driving cars increasingly facing the real-world open (unknown) environments, we can no longer make the closed world assumption.

Publications

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

Created on Jan 24, 2018 by Bing Liu.