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.

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.

We are experiencing a pivotal moment in the history of our profession. Our community’s technology finds applications in various fields, including machine learning, integrated sensing, processing, and communication; robotics and autonomous systems; medical and computational imaging; communication systems; and speech and language processing systems, among others. As signal processing technology continues to advance, these applications will likely expand. The need to narrow the gap between academia and industry has seldom been greater.