Humans and animals have the ability to continually acquire and fine-tune knowledge throughout their lifespan. This ability  is mediated by a rich set of neurocognitive functions that together contribute to the early development and experience-driven specialization of our sensorimotor skills  . Consequently, the ability to learn from continuous streams of information is crucial for computational learning systems and autonomous agents (inter)acting in the real world  . However, continual lifelong learning remains a long-standing challenge for machine learning and neural network models since the incremental acquisition of new skills from non-stationary data distributions generally leads to catastrophic URLetting or interference. This limitation represents a major drawback also for state-of-the-art deep  neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which the number of tasks is not known a priori and the information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to continual lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic interference . Although significant advances have been made in domain-specific continual lifelong learning with neural networks, extensive research efforts are required for the development of general-purpose artificial intelligence and autonomous agents . We discuss well-established research and recent methodological trends motivated by experimentally observed lifelong learning factors in biological systems . Such factors include principles of neurosynaptic stability-plasticity, critical developmental stages , intrinsically motivated exploration, transfer learning, and crossmodal integration .
Humans and animals have the ability to continually acquire and fine-tune knowledge throughout their lifespan. This ability , referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to the long-term memory consolidation and retrieval without catastrophic URLetting . Consequently, lifelong learning capabilities are crucial for computational learning systems and autonomous agents interacting in the real world and processing continuous streams of information . However, lifelong learning remains a long-standing challenge for machine learning and neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic URLetting or interference. This limitation represents a major drawback also for state-of-the-art deep and shallow neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which the number of tasks is not known a priori and the information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic URLetting . Although significant advances have been made in domain-specific learning with neural networks, extensive research efforts are required for the development of robust lifelong learning on autonomous agents and robots . We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as neurosynaptic plasticity, multi-task transfer learning , intrinsically motivated exploration, and crossmodal learning .
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, referred to as lifelong learning,
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functions
mechanisms
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early development and experience-driven
development and
A
as well as to the long-term memory consolidation and retrieval without catastrophic URLetting
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the ability to learn from continuous streams of information is
lifelong learning capabilities are
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(inter)acting
interacting
A
and processing continuous streams of information
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continual
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incremental acquisition of new skills
continual acquisition of incrementally available information
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and shallow
D
continual
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interference
URLetting
D
continual lifelong
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general-purpose artificial intelligence and autonomous agents
robust lifelong learning on autonomous agents and robots
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research and recent methodological trends motivated by experimentally observed
and emerging research motivated by
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. Such factors include principles of neurosynaptic stability-plasticity, critical developmental stages
such as neurosynaptic plasticity, multi-task transfer learning
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transfer learning, and crossmodal integration
and crossmodal learning