Application of Brain-Inspired Computing for Daily Assistance

Abstract

The domains of artificial intelligence and machine learning continue to advance at a rapid speed in terms of algorithms, models, applications, and hardware thanks to an exponential increase in the amount of data collected on a daily basis. Deep neural networks have transformed these domains by achieving extraordinary human-like performance in various real-world challenges, such as picture or speech recognition. There is also a lot of effort going on to figure out the principles of computation in extensive biological neural networks, especially biologically plausible spiking neural networks. Neural-inspired algorithms (e.g., deep ANNs and deep RL) and brain intelligent systems have revolutionized the fields of machine learning and cognitive computing in the last decade, assisting in a variety of real-world learning tasks ranging from robot monitoring and interaction at home to complex decision-making about emotions and behaviors in humans and animals. While these brain-inspired algorithms and systems have made significant progress, they still require large data sets to train, and their outcomes lack the flexibility to adapt to a variety of learning tasks and provide long-term performance. To solve these issues, an analytical understanding of the concepts that allow brain-inspired intelligent systems to develop information, as well as how they might be translated to hardware for everyday help and practical applications, is required. This chapter focuses upon the applications, challenges, and solutions of brain-inspired computing for daily assistance.