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

Topic Information

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

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	3.1	7.2	2020	18.9 Days	CHF 1600	Submit
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Designs designs	-	3.9	2017	21.7 Days	CHF 1600	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit

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Published Papers (1 paper)

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17 pages, 29778 KiB  

Open AccessArticle

Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning

by Zhe Sun, Yaowei Huang, Aohai Zhang, Chao Li, Lifan Jiang, Xiaotong Liao, Ran Li and Junping Wan

Appl. Sci. 2025, 15(10), 5229; https://doi.org/10.3390/app15105229 - 8 May 2025

Viewed by 226

Abstract

Multimodal learning enhances model performance by integrating heterogeneous data but is hindered by modality laziness and privacy vulnerabilities. Modality laziness occurs when the model overly relies on a single modality for predictions, underutilizing other modalities and leading to suboptimal performance and poor cross-modal [...] Read more.

Multimodal learning enhances model performance by integrating heterogeneous data but is hindered by modality laziness and privacy vulnerabilities. Modality laziness occurs when the model overly relies on a single modality for predictions, underutilizing other modalities and leading to suboptimal performance and poor cross-modal integration. Privacy vulnerabilities arise when sensitive data from individual modalities are exposed during training or inference, risking unauthorized access or attacks, especially in shared model components. In this paper, we propose Privacy-Preserving Alternating Multimodal Representation Learning (PAMRL). Built on Multimodal Learning with Alternating Unimodal Adaptation (MLA), PAMRL alternately optimizes unimodal encoders and a shared representation head to mitigate modality laziness and improve cross-modal consistency. It introduces a hybrid uncertainty metric combining KL divergence and entropy to enhance prediction robustness while applying differential privacy to protect sensitive data in unimodal encoders, preserving the shared head for efficient cross-modal fusion. Extensive experiments on the MVSA and CREMA-D datasets, comparing PAMRL with MLA and other baselines, demonstrate its superior performance, achieving an optimal balance of predictive accuracy, attack resilience, and privacy protection, thus supporting secure, efficient multimodal applications. Full article

(This article belongs to the Topic The Future of Artificial Intelligence: Trends, Challenges, and Developments)

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