DESIGNING INTERPRETABLE, ENERGY-EFFICIENT, AND ROBUST NEURAL NETWORKS WITH LIFELONG LEARNING CAPABILITIES AND REDUCED DATA DEPENDENCY

Abstract

Artificial Neural Networks (ANNs) are the core of modern machine learning technology that attempts to imitate the computational capabilities of the human brain. In this paper, the history of ANNs is described from their simple forms as the neurons of the 1940s through to their complex forms. It examines the simple mechanism of neural computation such as propagation, backpropagation, and activation functions. Furthermore, this paper demonstrates the different types of neural networks such as Feedforward, Convolutional, and Recurrent Neural Networks. This paper also critically examines the major challenges that include a lack of biological realism, interpretability that is the inability to interpret the trade-off between predictive accuracy and computational complexity.In addition to their underlying principles, artificial neural networks have experienced a dramatic evolution from their early theoretical models of the neuron to the current deep learning models that power modern intelligent systems. One of the most important aspects of this evolution is the application of activation functions, which enable neural networks to perform non-linear computations and make complex decisions rather than simple linear transformations.

The design of different network architectures, including feedforward networks, convolutional neural networks, and recurrent neural networks, differs significantly, enabling them to be applied to a wide range of problems, from image and speech recognition to processing sequential data and natural language understanding. This has resulted in the extensive use of ANNs in practical applications, including healthcare diagnostics, financial prediction, autonomous vehicles, and natural language processing. Nevertheless, the performance of neural networks is highly dependent on the availability of large amounts of quality data, and if trained inappropriately, they can lead to overfitting, where the model performs well on the training data but poorly on new data. Furthermore, contemporary neural networks are known to consume large amounts of computational power, specialized hardware, and energy, leading to concerns about their efficiency and environmental sustainability. Ethical issues, including bias and fairness, have also arisen, as the models are capable of learning and perpetuating societal biases present in the training data unintentionally. The lack of interpretability of neural networks also makes them difficult to apply, especially in safety-critical applications, where interpretability of decision-making processes is critical. To overcome these issues, current research is being conducted in the areas of explainable artificial intelligence, biologically inspired learning strategies, and efficient learning strategies. Future research is also expected to combine neural networks with systems of symbolic reasoning to develop models that are not only powerful and accurate but also transparent and trustworthy.