Lessons From Two Roundtables on Artificial Intelligence and Signal Processing Education: Addressing the emergence of a new era and a new discipline [Special Issue on Artificial Intelligence for Education: A Signal Processing Perspective]
Bibliographic record
Abstract
Recently, the Signal Processing Society Education Board launched an initiative to host roundtables discussing the impact of machine learning and artificial intelligence advancements in signal processing education. The first of these events took place in October 2023. The panelists were Profs. Alan Bovik (The University of Texas at Austin), Edward J. Delp (Purdue University), Aggelos K. Katsaggelos (Northwestern University), Anna Scaglione (Cornell University and Cornell Tech.), Sharon Gannot (Bar-Ilan University), and Andreas Spanias (Arizona State University). The panel was moderated by Profs. Marios Pattichis (University of New Mexico) and Andres Kwasinski (Rochester Institute of Technology). The second panel, which was organized during ICASSP 2024, had as panelists Profs. Gene Cheung (York University), Danilo Mandic (Imperial College London), Martin Haardt (Ilmenau University of Technology), and José M. F. Moura (Carnegie Mellon University). This panel was moderated by Prof. Andres Kwasinski. This article summarizes the roundtable discussions, distills key lessons, and offers additional insights. A key consensus among the panels was that we are at a pivotal moment when we are witnessing the emergence of a new discipline that combines the model-based approach from traditional signal processing with the data-driven approach from ML and Data Science. The emergence of this new discipline calls for new pedagogical methods and brings new tools that will reshape how we do research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.021 | 0.034 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".