Artificial Intelligence
From the Guest Editors: Artificial Intelligence for Education: A Signal Processing Perspective: Part II: From Human–AI Cocreativity to Educational Equity
Signal processing (SP) is at the heart of our digital lives and has served as an enabling technology across multiple disciplines, from the acquisition of signals and images through to artificial intelligence (AI). With education playing a key role in the advancement of modern data-centric disciplines, it has been recognized that AI technologies, particularly the success of generative AI, offer transformative possibilities to revolutionize SP education, making it more data driven, relevant, and personalized.
Deploying AI for Signal Processing Education: Selected Challenges and Intriguing Opportunities
Abstract: Powerful artificial intelligence (AI) tools that have emerged in recent years—including large language models (LLMs), automated coding assistants, and advanced image and speech generation technologies—are the result of monumental human achievements. These breakthroughs reflect mastery across multiple technical disciplines and the resolution of significant technological challenges. However, some of the most profound challenges may still lie ahead.
Call for Nominations: Editor-in-Chief IEEE Transactions on Artificial Intelligence
The IEEE Computational Intelligence Society; IEEE Computer Society; IEEE Systems, Man, and Cybernetics Society; IEEE Signal Processing Society; and…
Read moreDeep Learning on Graphs: History, Successes, Challenges, and Next Steps
Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases, has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [2], if not two [3], it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the Machine Learning (ML) community.
Artificial Intelligence in Radio Frequencies
Artificial intelligence (AI) and machine learning (ML) as an application of AI, has today become an inevitable part of major industries such as healthcare, financial trending, and transportation. Future urgent need to intelligently utilize wireless resources to meet the need of ever-increasing diversity in services and user behavior, has actuated the wireless communication industry to deploy AI and ML techniques.
