For decades, Turing’s test shaped public understanding of AI. Yet as technology has advanced, many researchers have asked whether imitating human conversation really proves intelligence — or whether it only shows that machines can mimic certain human behaviors. Large language models like ChatGPT can already hold convincing conversations. But does that mean they understand what they are saying?

In a Mind Matters podcast interview, Dr. Georgios Mappouras tells host Robert J. Marks that the answer is no. In a recent paper, The General Intelligence Threshold, Mappouras introduces what he calls Turing Test 2.0. This updated approach sets a higher bar for intelligence than simply chatting like a human. It asks whether machines can go beyond imitation to produce new knowledge.

From information to knowledge

At the heart of Mappouras’s proposal is a distinction between two kinds of information, non-functional vs. functional:

• Non-functional information is raw data or observations that don’t lead to new insights by themselves. One example would be noticing that an apple falls from a tree.
• Functional information is knowledge that can be applied to achieve something new. When Isaac Newton connected the falling apple to the force of gravity, he transformed ordinary observation into scientific law.

True intelligence, Mappouras argues, is the ability to transform non-functional information into functional knowledge. This creative leap is what allows humans to build skyscrapers, develop medicine, and travel to the moon. A machine that merely rearranges words or retrieves facts cannot be said to have reached the same level.

The General Intelligence Threshold

Mappouras calls this standard the General Intelligence Threshold. His threshold sets a simple challenge: given existing knowledge and raw information, can the system generate new insights that were not directly programmed into it?

This threshold does not require constant displays of brilliance. Even one undeniable breakthrough — a “flash of genius” — would be enough to demonstrate that a machine possesses general intelligence. Just as a person may excel in math but not physics, a machine would only need to show creativity once to prove its potential.

Creativity and open problems

One way to apply the new test is through unsolved problems in mathematics. Throughout history, breakthroughs such as Andrew Wiles’s proof of Fermat’s Last Theorem or Grigori Perelman’s solution to the Poincaré Conjecture marked milestones of human creativity. If AI could solve open problems like the Riemann Hypothesis or the Collatz Conjecture — problems that no one has ever solved before — it would be strong evidence that the system had crossed the threshold into true intelligence.

Large language models already solve equations and perform advanced calculations, but solving a centuries-old unsolved problem would show something far deeper: the ability to create knowledge that has never existed before.

Beyond symbol manipulation

Mappouras also draws on philosopher John Searle’s famous “Chinese Room” thought experiment. In the scenario, a person who does not understand Chinese sits in a room with a rulebook for manipulating Chinese characters. By following instructions, the person produces outputs that convince outsiders he understands the language, even though he does not.

This scenario, Searle argued, shows that a computer might appear intelligent without real understanding. Mappouras agrees but goes further. For him, real intelligence is proven not just by producing outputs, but by acting on new knowledge. If the instructions in the Chinese Room included a way to escape, the person could only succeed if he truly understood what the words meant. In the same way, AI must demonstrate it can act meaningfully on information, not just shuffle symbols.

Can AI pass the new test?

So far, Mappouras does not think modern AI has passed the General Intelligence Threshold. Systems like ChatGPT may look impressive, but their apparent creativity usually comes from patterns in the massive data sets on which they were trained. They have not shown the ability to produce new, independent knowledge disconnected from prior inputs.

That said, Mappouras emphasizes that success would not require constant novelty. One true act of creativity — an undeniable demonstration of new knowledge — would be enough. Until that happens, he remains cautious about claims that today’s AI is truly intelligent.

A shift in the debate

The debate over artificial intelligence is shifting. The original Turing Test asked whether machines could fool us into thinking they were human. Turing Test 2.0 asks a harder question: can they discover something new?

Mappouras believes this is the real measure of intelligence. Intelligence is not imitation — it is innovation. Whether machines will ever cross that line remains uncertain. But if they do, the world will not just be talking with computers. We will be learning from them.

Final thoughts: Today’s systems, tomorrow’s threshold

Models like ChatGPT and Grok are remarkable at conversation, summarization, and problem-solving within known domains, but their strengths still reflect pattern learning from vast training data. By Mappouras’s standard, they will cross the General Intelligence Threshold only when they produce a verifiable breakthrough — an insight not traceable to prior text or human scaffolding, such as an original solution to a major open problem. Until then, they remain powerful imitators and accelerators of human work — impressive, useful, and transformative, but not yet creators of genuinely new knowledge.

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