Artificial Intelligence Engineering (AI) makes it possible for engineered systems to learn from experience, perform complex tasks with human like abilities simply by learning from input data, interact with humans or other machines to learn collaboratively – ultimately evolving into system-of-systems, capable of working with multiple streams of inputs from disparate sources. Current applications of AI include autonomous systems, computer vision, speech recognition, natural language processing, multi-agent systems and systems management such as in wireless communications, where AI automates spectrum management, dynamic access and networking at the wireless edge among many others. Solving these problems require representation of real-world inputs such as image, voice or continuous time data into digital forms that are suitable for processing coupled with mathematical and formal reasoning, which are at the heart of electrical and computer engineering. As AI applications become ubiquitous, they execute distributed models running on networked machines ranging from battery operated handheld devices to data centers with varying power, performance and real-time requirements requiring optimizations from model size to mapping models onto custom hardware – where principles of electrical and computer engineering play key role.
Data Engineering (DE) involves data acquisition from physical world, representation to support high-level query, storage in distributed physical medium, analytical techniques for de-noising and conditioning data, analytical techniques for discovery of relationship across various data sets, feature extraction, and if appropriate, automatic labeling of data for higher-level learning systems such as AI. Electrical and computer engineering principles apply throughout this data lifecycle from acquisition, transformation to feature extraction, semantic analysis for higher-level learning.