The burgeoning field of artificial intelligence for social impact (AI4SI) represents a significant evolution in artificial intelligence, prioritizing measurable positive impact for vulnerable and under-resourced populations. This article examines the historical context and recent surge in AI4SI, driven by technological advancements and a growing awareness of societal challenges. It highlights the crucial role of interdisciplinary collaboration, ethical considerations, and the potential of emerging AI trends in addressing issues such as poverty, health, and environmental sustainability. Furthermore, the article delves into key research questions and challenges facing the field, including the need for contextually relevant AI design, overcoming data limitations, ensuring scalable and sustainable deployments in resource-constrained environments, and establishing robust evaluation frameworks. Realizing the full potential of AI to address pressing societal needs in the coming decade and beyond will hinge on effectively navigating these challenges and fostering a deeply impact-driven approach to research and development.

The practical deployment of federated learning in distributed software-defined networks has to face many challenges, among which designing effective methods to handle heterogeneous environments with a convergence guarantee is of great concern. This paper proposes a novel algorithm called FL4SDN, using weighted averaging stochastic gradient descent updates for the federated optimization problem. In FL4SDN, the weight given to each controller is determined based on the relationship between the local and global gradients. Theoretically, it has been proven that faster convergence can be achieved if the loss function is convex and has a bounded first derivative. The effectiveness of FL4SDN is confirmed by experiments carried out on different network topologies. We demonstrate that such an algorithm can give a reduction of at least 24.4% in terms of communication rounds as compared to the state-of-the-art Gaia, FedAvg, Deep Gradient Compression, and Bulk-Synchronous Parallel. It also provides high prediction accuracy and low communication costs.

Advances in deep learning and knowledge representation have driven the adoption of intelligent decision support systems in high-stakes domains such as health-care applications. However, traditional statistical approaches often yield opaque, correlation-driven predictions, making it difficult for experts to interpret results and take meaningful actions. To address these limitations, we propose a cognitive neurosymbolic artificial intelligence (AI) framework by combining multimodal perception, neurosymbolic reasoning (with symbolic controls), and knowledge, this paradigm enhances accuracy and robustness in complex decision-making environments. This integrated framework ensures that every recommendation follows human-like decision-making, while being grounded in verifiable, expert knowledge, a crucial attribute for critical application scenarios such as health care. We demonstrate this approach through a mental health support system, showcasing how cognitive neurosymbolic AI can improve decision-making outcomes, personalized interventions, longitudinal tracking, and actionable insights for critical applications.

The recent development of Internet of Things (IoT) and unmanned aerial vehicles (UAVs) has revolutionized traditional agriculture with intelligence and automation. In a typical intelligent agriculture (IA) ecosystem, massive and real-time data are generated, analyzed, and sent to the cloud server (CS) for the purpose of addressing complex agricultural issues, such as yield prediction, water feed calculation, and so on. This helps farmer and associated stakeholders to take correct decision that improves the yield and quality of agricultural product. However, the distributed nature of IA entities and the usage of insecure wireless communication open various challenges related to data sharing, monitoring, storage, and further makes the entire IA ecosystem vulnerable to various potential attacks. In this article, we exploit deep learning and smart contract to propose a new IoT-enabled IA framework for enabling secure data sharing among its various entities. Specifically, first we develop new authentication and key management scheme to ensure secure data transmission in IoT-enabled IA. The encrypted transactions are then used by the CS to analyze and further detect intrusions by a novel deep learning architecture. In CS, the smart contract (SC)-based consensus mechanism is executed on legitimate transactions that verifies and adds the formed blocks into blockchain by a peer-to-peer CSs network. In comparison to existing competing security solutions, a rigorous comparative research demonstrates that the proposed approach provides greater security and more utility characteristics.