Artificial Neural Networks: Current Application Areasand Future Potential

This paper provides a comprehensive analysis of <i>Artificial Neural Networks (ANNs)</i>, exploring both their <b>current real-world applications</b> and <b>future technological potential</b>. Drawing from recent academic literature and industry breakthroughs, it examines ANN-driven innovations in healthcare, finance, autonomous vehicles, natural language processing, and image/video processing, supported by <i>case studies</i> such as Google DeepMind’s AlphaFold, Tesla’s Autopilot, and OpenAI’s GPT models. The study further investigates emerging frontiers—including <b>Quantum Machine Learning</b>, <b>Neuromorphic Computing</b>, <b>Multimodal AI</b>, and <b>Sustainable AI</b>—while critically addressing <b>technical limitations</b>, <b>ethical challenges</b>, and <b>environmental concerns</b>. By combining technical insight with an ethics-focused perspective, this work offers actionable recommendations for researchers, developers, and policymakers to guide responsible, transparent, and energy-efficient AI development.