The Future of Artificial Intelligence: Trends, Challenges, and Developments

Dear Colleagues,

Artificial Intelligence (AI) has become a transformative force across various industries, with widespread applications in healthcare, education, transportation, and scientific research. One of the most significant advancements in AI is the progress made in deep learning and reinforcement learning. In recent years, breakthroughs in these areas have driven developments in natural language processing, computer vision, and autonomous systems. At present, AI is experiencing widespread adoption in edge computing and the Internet of Things (IoT), playing a crucial role in traffic management for smart cities and predictive maintenance in intelligent manufacturing. With ongoing advancements in algorithms and computing hardware, AI is being widely deployed across logistics, healthcare, and agriculture, significantly enhancing efficiency, automation, and safety. Additionally, AI is transforming remote sensing, which is actively used for satellite image analysis, environmental monitoring, land use classification, disaster prediction, and climate change studies. However, the fields integrated with AI extend far beyond the aforementioned areas.

Despite its promising future, AI still faces many challenges. Among them, the interpretability of AI models has become a critical factor limiting their widespread application, especially in high-risk fields such as healthcare, finance, justice, and autonomous driving. Current deep learning models are often considered "black boxes" due to their lack of transparency, making it difficult for users to understand the decision-making process. Therefore, enhancing the interpretability of AI models and building secure and trustworthy AI systems has become a key research direction.

Integrating AI with quantum computing could potentially lead to breakthroughs in computational power, solving complex problems that are currently beyond reach. Moreover, the interpretability of AI will continue to be closely integrated with various application scenarios, such as enhancing the transparency of AI decisions in financial risk control, providing verifiable safety guarantees in autonomous driving, and enabling more precise environmental monitoring in remote sensing. The widespread adoption of collaborative AI will enhance intelligent human–machine interaction, fostering innovation and productivity. Overall, the future of AI is filled with both opportunities and challenges. This Topic aims to solicit the latest research findings in artificial intelligence.

Topics of interest include but are not limited to the following:

• Differential Privacy in AI models;
• Healthcare and medical applications in AI;
• Autonomous Vehicles and Intelligent Transportation Systems in AI;
• Federated Learning for Decentralized AI Systems;
• AI-powered Robotics and Human–Robot Collaboration;
• Edge AI and Real-time Inferencing for IoT;
• Quantum Computing Integration in AI;
• Large Language Model (LLM) and Generative AI;
• Backdoor attacks and defenses in deep learning;
• Robustness verification of AI models;
• Secure AI for autonomous systems and critical infrastructure;
• AI-driven satellite image processing and enhancement;
• Multi-spectral and hyperspectral remote sensing in AI;
• Smart contract vulnerabilities in AI-driven applications;
• AI-driven intrusion detection and threat intelligence;
• AI-based Predictive Maintenance in Industrial Applications;
• Interpretable AI-enabled remote sensing;
• Interpretable AI-enabled financial risk;
• Human–AI Interaction for interpretability control.

Prof. Dr. Yinbin Miao
Dr. Jun Feng
Dr. Wenqian Dong
Topic Editors