The IEEE Signal Processing Society (SPS) had a momentous year in 2025. The successes we achieved together helped move the Society forward in new and innovative ways. We also learned valuable lessons, which we will use to help transform this new year.

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.

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.

Abstract: This article argues that the quest for brain-mimicking artificial intelligence is no longer limited by algorithms or transistor counts, but by the philosophical assumptions embedded in modern electronics. Today’s AI systems, despite their impressive performance, remain rooted in a worldview shaped by the historically inherited and largely unquestioned digital reductionism and Cartesian dualism: one that fragments signals, separates mind from matter, and treats intelligence as an abstract computation detached from physical embodiment.

With the advent of large language models (LLMs), the concept of artificial intelligence (AI) agents capable of decision making and action execution has gained prominence, particularly as LLMs demonstrate increasing proficiency in tool use and task planning. Following these advancements, the established methods used for task-oriented dialogue (TOD) systems have undergone a paradigm shift by integrating LLMs’ revolutionary language understanding and reasoning skills with enhanced instruction following and response generation abilities.