Lifelong machine learning (LML) is an area of machine learning research concerned with human-like persistent and cumulative nature of learning. LML system's objective is consolidating new information into an existing machine learning model without catastrophically disrupting the prior information. Our research addresses this LML retention problem for creating a knowledge consolidation network through task rehearsal without retaining the prior task's training examples. We discovered that the training data reconstruction error from a trained Restricted Boltzmann Machine can be successfully used to generate accurate virtual examples from the reconstructed set of a uniform random set of examples given to the trained model. We also defined a measure for comparing the probability distributions of two datasets given to a trained network model based on their reconstruction mean square errors.

We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets were arranged in a lifelong learning and evaluation scenario and CodaLab was used as the challenge platform. The challenge attracted more than 300 participants in its two month duration. This chapter describes the design of the challenge and summarizes its main results.

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent.

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent.

What don't you know that you need to know, and how do you know you don't know? Imagine putting that into a search engine, expecting a coherent answer. The answers we seek are at the core of discovery and learning, motivated by necessity and pleasure, by job displacement, or leadership uncertainty in a complex and fast changing world. The process of learning requires a detailed knowledge of ourselves and of our world, whether a human or a machine is tasked to help. We can fill gaps in our skills and knowledge through web searches, discussion with experts and like-minded people, reading books, working through an education curriculum, learning online with video tutorials, and absorbing a vast amount of content flowing through online news channels and aggregators.

Lifelong Machine Learning (LML) considers situations in which a learner faces a series of tasks over a lifetime. An LML system requires a method of using prior knowledge to learn models for new tasks as efficiently and effectively as possible, and a method of retaining task knowledge after it has been learned. Knowledge retention is necessary for a lifelong learning system, however it is not sufficient. We propose that domain knowledge must be integrated for the purposes of efficient and effective retention and for more efficient and effective transfer during future learning. The process of integration we define as consolidation. The challenge for an LML system is consolidating the knowledge of a new task while maintaining and possibly improving knowledge of prior tasks; this requires a solution to the stability-plasticity problem. This paper provides a summary of prior work by the author on the consolidation problem within various LML systems.