Key Points
- The term “AI” now covers a vast array of technologies, causing widespread confusion.
- Vasant Dhar (NYU Stern) says “AI” is often used synonymously with technology in general.
- Rupert Shute (Imperial College London) emphasizes that AI comprises many distinct technologies.
- Thiago Ferreira (Elevate AI Consulting) lists everyday AI examples such as spam filters and medical imaging.
- Generative AI, powered by large language models, dominates public perception of AI.
- Experts classify AI by function (recognition, prediction, autonomous systems) and by historical development.
- Shute describes three AI waves: symbolic logic, statistical learning, and neuro‑symbolic AI.
- Precise terminology helps consumers evaluate product claims and avoid hype.
- All experts agree AI systems rely on human direction and extend human intelligence.
Why “AI” Is No Longer a Precise Label
The word “AI” has turned into a broad label used to describe a wide range of technologies, from conversational chatbots like ChatGPT to tools that detect tumors or sort photos. This breadth has created confusion among the public and even among experts.
Expert Perspectives on the Terminology Issue
Vasant Dhar, a professor at NYU Stern, notes that people often use “AI” synonymously with technology in general, which dilutes its meaning. Rupert Shute, a professor of practice at Imperial College London, emphasizes that AI is actually many different technologies, and that the popularity of generative AI has crowded out awareness of older, still valuable AI classes. Thiago Ferreira, CEO and founder of Elevate AI Consulting, points out that everyday AI includes spam filters, fraud detection, medical imaging, recommendation systems, and photo‑sorting algorithms.
Generative AI’s Dominance and Its Effects
Generative AI, which creates new content in response to prompts, is the most visible form of AI today. Large language models (LLMs) power chatbots such as ChatGPT, making generative AI the public’s primary mental image of the field. While this visibility helps people relate to AI, it also overshadows quieter systems that have been in use for decades.
Different Ways to Classify AI
Experts describe AI using various frameworks. Ferreira prefers a practical lens that focuses on what AI does—recognition, prediction, and autonomous operation. Dhar outlines a historical evolution from expert systems to machine learning, deep learning, and finally large, general‑purpose models. Shute separates AI into three waves: symbolic logic, statistical learning (including deep learning and transformers), and neuro‑symbolic AI that blends learning with reasoning.
The Importance of Precise Language
When companies claim their products “use AI,” understanding which type of AI is involved and what it actually does becomes essential. Precise terminology helps consumers assess risk and benefit, and it prevents hype from obscuring real capabilities.
Power Dynamics and Human Involvement
All experts agree that AI systems depend on human direction. Ferreira stresses that AI extends, rather than replaces, human thinking, and that the intelligence starts with human input.
Moving Forward
Clarity about AI’s many subfields can improve public discourse, guide responsible adoption, and ensure that enthusiasm for generative AI does not eclipse the value of longstanding AI applications.
Source: techradar.com