Published July 13, 2026 | Source: Taylor & Francis Group
A new book argues that artificial intelligence research has been chasing an impossible dream for decades, built on a flawed assumption dating back to Alan Turing’s famous 1950 paper. Leading computer scientist Peter J. Denning warns that machines may become dangerously intelligent without ever truly understanding the human world.
Turing’s Two Foundational Claims
In his book Turing’s Mistake: Escaping the Yoke of Unintelligent Machines, Denning challenges two core ideas that Turing introduced in 1950 and that continue to shape AI research in 2026:
- Intelligence can exist independently of a physical body — and therefore be recreated in computer software.
- A machine can demonstrate intelligence by successfully imitating a human in conversation — later known as the Turing test.
- Common sense
- Everyday interactions with people and the environment
- Emotions and perception
- Practical know-how and intuition
- Cultural understanding
“These two claims have shaped much of AI research and development,” Denning writes. “My premise is that our acquiescence to these claims has led to the AI mess in which we find ourselves today.”
The Pursuit of AGI May Be Misguided
Denning argues that the goal of achieving artificial general intelligence (AGI) — machines with human-level intelligence — is unlikely to succeed. Instead, the technologies society is building could introduce significant new risks, especially as large language models and autonomous systems become more prevalent in the 2020s.
The Tacit Knowledge Problem
At the heart of Denning’s argument is the concept of tacit knowledge: the vast amount of human understanding that cannot easily be put into words or represented in a form computers can process. He identifies five categories of tacit knowledge that machine learning cannot capture:
“The most important parts of human intelligence — including common sense, intuition, culture, and practical know-how — cannot be encoded into computers,” Denning explains. “This makes true human-level AI impossible, regardless of how large language models become.”
A Warning for the AI Industry
As of 2026, the AI industry has seen rapid advances in generative AI, autonomous agents, and reasoning models. Yet Denning cautions that these systems lack genuine understanding, even as they become more capable. He warns that increasingly autonomous AI systems may evolve in ways that are difficult for humans to predict or manage, posing risks that society is not fully prepared to address.
Image credit: AI/ScienceDaily.com — Peter J. Denning warns that increasingly autonomous AI systems may evolve in ways that are difficult for humans to predict or manage.