Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills 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 long-term memory consolidation and retrieval. Consequently, lifelong learning capabilities are crucial for computational 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 forgetting or interference. This limitation represents a major drawback 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 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 forgetting. 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 structural plasticity, memory replay, curriculum and transfer learning, intrinsic motivation, and multisensory integration.

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In this paper, we present the design, implementation and evaluation of a simple and user-friendly interface for a home automation system. Our approach is based on speech; given that this allows a more natural way of interacting with the system. The use of speech also improves accessibility since people with impaired vision can potentially use it. The interface is based on Android and thus, it runs on any Android compatible devices. Alternatively to the interface implemented, we introduce a mockup of what the ultimate design would look like. Finally, we present the results from an evaluation of the system performed on 30 individuals between the ages of 20-29 years old. These results show an overall broad acceptance of the proposed system. Categories and Subject Descriptors• Natural language interfaces • HCI design and evaluation methods. KeywordsVoice control, smart-home and interaction.

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