Why AGI benchmarks will never be objective

The Turing Test, once seemingly an ideal check for intelligence, is now clearly outdated. Researchers are seeking new ways to assess whether a system has reached the level of strong artificial intelligence — but they face an unexpected obstacle.

Why the Turing Test No Longer Works

When Alan Turing proposed his famous test in 1950, the idea was simple: if a machine can convince a human that it is a human, then it thinks. Seventy years later, modern LLMs easily pass this test — but this doesn't mean they are intelligent in the AGI sense. Researchers acknowledge: new criteria are needed. Conferences like IEEE are looking for fresh benchmarks to evaluate strong AI. The problem is that the criteria must be objective — and this is far more difficult than it seemed.

Seventy Definitions of Intelligence

The first obstacle: scientists themselves cannot agree on what intelligence is. There are at least 70 different scientific definitions of human intelligence. Some consider intelligence the ability to adapt, others the speed of information processing, still others creativity. If we cannot objectively define intelligence even in our own species, how can we create a test to evaluate it in a machine?

• Definition through IQ (logic and arithmetic)
• Definition through adaptability (how quickly it learns in a new environment)
• Definition through context (understanding nuances and culture)
• Definition through creativity (original ideas and solutions)

The Paradox of Consciousness in Neural Networks

The second obstacle — endless scientific debates about whether LLMs can develop consciousness. Dozens of papers on arxiv examine whether there is an "inner life" in large language models. But this is a strange dispute. Neurobiology and psychology established long ago: consciousness in humans is rather an impediment than a tool of thinking. The most effective cognitive processes occur at the subconscious level. When you concentrate on a task (consciousness is activated), you often begin to slow down and make errors. This is why athletes talk about "flow" — a state where consciousness is turned off.

Interference of self-awareness only hinders the solution of complex

problems — established in scientific literature.

If a model ever develops self-awareness, engineers will simply delete this bug — so the system works faster and more accurately, as it does now.

What It Means

The conclusion is paradoxical: assessing AGI objectively is impossible not because AI is too smart, but because we cannot agree even on criteria for our own intelligence. Any benchmark will reflect a subjective choice of what to consider "intelligence" — and no one will be satisfied with this choice.