Artificial Intelligence
76 What is Modern AI and Why it Shouldn’t be Called AI at All
Jack Hobbs
Introduction
Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably, but they are not the same. The distinction between these two fields is critical to understanding the capabilities and limitations of current technologies. While AI aims to create systems that can perform tasks typically requiring human intelligence, ML is a subset of AI focused on enabling systems to learn from data. In today’s landscape, what is commonly referred to as “AI” is more accurately described as advanced machine learning. This chapter discusses these differences and why current technology falls short of true AI.
AI vs Machine Learning
AI, in its original conception, was envisioned as a technology that could mimic or even replicate human intelligence. Early AI research set ambitious goals, aiming to create machines that could reason, learn, and interact with the world autonomously. The Lawrence Livermore National Laboratory notes that early researchers in AI aimed to develop “machines with human-like intelligence” but quickly found this objective far more challenging than anticipated (Lawrence Livermore National Laboratory). The high hopes of achieving general AI—an intelligence that can perform a variety of tasks beyond narrow, data-driven contexts—contrast starkly with the machine learning models that dominate the field today.
Machine Learning, a subset of AI, has become the driving force behind most AI advancements today. It uses statistical techniques and algorithms to parse large datasets, identify patterns, and make predictions or decisions. According to CSU Global, much of what is considered AI today “relies on large datasets and statistical methods” rather than true cognitive capabilities (CSU Global). While these systems excel at specific, narrowly defined tasks, they lack the flexible and adaptive reasoning required for genuine AI.
The limitations of current AI technologies are clear when examined through the lens of true AI capabilities. SAS explains that today’s AI applications are essentially advanced machine learning models that “perform specific tasks by learning from data” but lack general intelligence (“Artificial Intelligence (AI): What It Is and Why It Matters”). For instance, while a machine learning algorithm can be trained to recognize images or process natural language, it does not “understand” these tasks in the human sense. It is essentially programmed to identify patterns within a dataset without any deeper comprehension or cognitive processing.
One of the primary characteristics that distinguish true AI from machine learning is the ability to understand and interact with the world. Roger Schank argues that most of what we call AI today falls short because these systems “are merely sophisticated machine learning programs” and lack true comprehension (Schank). He emphasizes that an AI capable of understanding would not