How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis

TL;DR

This paper explores the evolving relationship between electrical engineering and artificial intelligence, highlighting how deep learning has unified these fields and opening new interdisciplinary research opportunities.

Contribution

It provides a historical analysis of AI's development and discusses the recent convergence of electrical engineering and AI through deep learning, proposing future research directions.

Findings

Deep learning has bridged electrical engineering and AI.

AI's evolution has shifted from symbolic to data-driven methods.

Interpretable machine learning offers new research opportunities.

Abstract

This essay examines how what is considered to be artificial intelligence (AI) has changed over time and come to intersect with the expertise of the author. Initially, AI developed on a separate trajectory, both topically and institutionally, from pattern recognition, neural information processing, decision and control systems, and allied topics by focusing on symbolic systems within computer science departments rather than on continuous systems in electrical engineering departments. The separate evolutions continued throughout the author's lifetime, with some crossover in reinforcement learning and graphical models, but were shocked into converging by the virality of deep learning, thus making an electrical engineer into an AI researcher. Now that this convergence has happened, opportunity exists to pursue an agenda that combines learning and reasoning bridged by interpretable machine learning models.