Artificial Neural Networks and Computational Power: Bridging the Gap Between Human Cognition and Machine Intelligence

This paper, titled “Artificial Neural Networks and Computational Power: Bridging the Gap Between Human Cognition and Machine Intelligence,” explores how modern AI systems inspired by the human brain are redefining computational intelligence. It examines the evolution of Artificial Neural Networks (ANNs)—from perceptrons to deep and transformer architectures—and how advancements in computational hardware such as GPUs, TPUs, and emerging neuromorphic chips have fueled this progress. The study draws parallels between biological and artificial neurons, analyzing how machine intelligence increasingly mirrors human cognition in learning, adaptation, and pattern recognition. It also investigates the growing tension between computational power and energy efficiency, emphasizing the need for sustainable and interpretable AI models. By blending insights from neuroscience, computer engineering, and ethics, the paper highlights the importance of responsible AI development—one that balances raw processing capacity with cognitive depth and environmental sustainability. Ultimately, it argues that the future of intelligence lies not in limitless computation, but in creating systems that learn and reason as efficiently as the human brain.