Will We Recognize Artificial General Intelligence When It Arrives

Why the old Turing Test no longer works and why designing a new “IQ test” for AI is surprisingly complicated

The Strange Obsession With AGI Timelines

If you hang around people who work in artificial intelligence long enough, you eventually start hearing a funny question slipped casually into conversations: “So… what’s your timeline?” And they don’t mean retirement plans or family goals. They’re talking about when they expect AGI artificial general intelligence to show up.

It’s become normalized to the point where you might hear someone compare timelines the way sports fans compare tournament brackets. Some researchers swear AGI is still decades away; others especially the folks leading labs like OpenAI, Anthropic, or Google DeepMind have started saying out loud that they think it’s only a few years off. That’s a bold claim, considering how sci fi the idea once sounded.

AGI is one of those phrases that people toss around without agreeing on what it actually means. Some define it vaguely something like, “an AI that’s as capable as a human at most intellectually demanding tasks.” Others attach it to economic impact or inner architecture or even “vibes,” which isn’t exactly helpful if you’re trying to build safety protocols.

But whatever AGI is, or will be, almost everybody agrees on one thing: we need to know when we’re getting close. Because once machines can learn, plan, and adapt across domains the way humans do, the ripple effects won’t be subtle. We’re talking shifts in labor markets, science, national security and depending on who you ask maybe even the hierarchy of intelligent life on Earth.

That’s why measuring intelligence in machines isn’t some academic game. It’s preparation.

Why Testing Intelligence (Even Human Intelligence) Is Such a Mess

Before jumping into how to measure AI intelligence, it helps to remember that even measuring human intelligence has never been straightforward.

Take IQ tests. They attempt to capture something like a “general ability level” by grouping together tasks that rely on vocabulary, memory, logic, spatial reasoning, and quick thinking. It’s sort of like using a single score to summarize the performance of an entire orchestra not perfect, but informative enough for some real world predictions, like academic success or job performance.

Still, IQ doesn’t capture everything. You can’t measure social intuition, humor, the ability to negotiate a tense situation, or the weird creative spark that makes someone suddenly decide to rearrange their living room at 3 a.m. And machines have the same problem in reverse: they may handle some cognitive tasks impressively well while having zero grasp of physical reality or social nuance.

One of the earliest cautionary tales in intelligence testing comes from an old story about a horse named Clever Hans. In the early 1900s, his owner claimed Hans could solve math problems. Crowds watched amazed as Hans tapped out correct answers with his hoof. Eventually researchers figured out that the horse wasn’t doing arithmetic at all he was reacting to tiny, unconscious cues from his trainer.

This little episode became a classic warning: a system can appear intelligent for the wrong reasons. And Hans wasn’t the last to fool us.

The Problem With Defining AGI

You can define AGI in at least four incompatible ways:

Behavioral an AI that can match humans at almost any task.
Architectural an AI that reasons or learns similarly to humans.
Economic an AI capable of performing any job as well as (or better than) a person.
Intuitive the one that “feels” intelligent to people.

These definitions overlap but not perfectly. For example, a machine might be economically transformative able to outperform experts in medicine, engineering, or finance yet terrible at childhood level tasks like folding laundry or understanding sarcasm. Would that count as AGI?

AI pioneer Geoffrey Hinton once called advanced models “alien beings,” and it wasn’t hyperbole. They don’t think like humans, even when they mimic our writing or logic. That alienness makes benchmarking tricky: How do you compare two intelligence systems built on entirely different principles?

The Turing Test Used to Be the Gold Standard But Not Anymore

Alan Turing’s famous test from 1950 was simple: if a human couldn’t distinguish between a machine’s and a person’s responses in a typed conversation, the machine could be called intelligent. For decades, people treated it like the ultimate goalpost.

Now? Modern AI models casually leap over it.

A recent experiment asked people to chat for five minutes with two entities one human, one GPT 4.5 and guess which was which. Participants misidentified the AI as the human 73% of the time.

That sounds like we’ve reached AGI, or at least something Turing would’ve considered convincing. But then you ask the same model to count the number of “r”s in the word strawberry, and it cheerfully gets it wrong. Every. Single. Time.

So the Turing Test no longer tells us what we need to know. Passing it is too easy, and failing it doesn’t correlate with lack of intelligence it’s more like tripping on a loose floorboard.

A Brief History of Failed AGI Benchmarks

The field is littered with “ultimate tests” that turned out to be less ultimate than expected.

Chess Was Supposed to Be the Final Frontier

In the 1950s, researchers insisted that if a machine could beat the best human chess players, that would prove deep intelligence. They weren’t naive they just underestimated brute force computation.

IBM’s Deep Blue crushed Garry Kasparov in 1997, yet the machine couldn’t have explained the rules to a child, let alone held a conversation.

Go Was the Next Big Dream

Then came Go, which many experts considered “too complex for computers” because it requires intuition and long term strategic thinking. AlphaGo proved that wrong in 2016 and then AlphaZero surpassed it with techniques even human experts found hard to interpret.

But again, mastery of Go didn’t translate into general intelligence. These systems could obliterate world champions at board games but couldn’t solve a simple riddle or understand a joke.

In short, we’ve repeatedly mistaken “hard for humans” as “hard in general.” Computers think differently.

The ARC Challenge: One of the Few AGI Tests Still Standing

In 2019, François Chollet introduced something new: the Abstraction and Reasoning Corpus (ARC). His argument was bold: most AI benchmarks reward memorization or pattern matching, not true learning ability.

ARC is different. It’s a collection of visual puzzles involving small grids filled with colored squares. Each puzzle shows a few examples of an input grid and the corresponding correct output. Your job whether human or machine is to infer the rule that transforms input to output.

For example:

Maybe all red squares get mirrored vertically.
Or shapes shrink to their outlines.
Or isolated tiles get grouped into new formations.

The rules vary wildly. And crucially, they’re unfamiliar. You can’t solve ARC puzzles through brute force or by memorizing patterns from the internet. You have to generalize.

Chollet’s argument is that intelligence isn’t a bag of skills it’s the ability to rapidly acquire new ones. On ARC, humans do reasonably well. AI models? They flounder unless heavily hand assisted suggesting we’re far from machines that can learn the way we do.

Even today, top models like GPT 4, Claude 3.5, or Gemini Ultra struggle mightily unless someone redesigns the puzzles to suit their strengths, which undermines the point.

Why Measuring AGI Is Harder Than Ever

You would think that with smarter AI we’d have clearer metrics. Ironically, the opposite happened. As models became more capable, their failure modes became stranger.

1. AI shines in areas humans consider intellectual… but fails in basic reasoning

Ask a model to summarize a 500 page technical document? No problem.
Ask it to tell you whether a bicycle would fit inside a refrigerator? It hesitates or gives absurd explanations.

2. AI sometimes looks smart because it learned shortcuts, not principles

Just like Clever Hans, modern AI often finds signals hidden in training data correlations humans didn’t notice so it “appears intelligent.” But when faced with problems outside those patterns, it collapses.

3. AGI depends on what society cares about, not a fixed definition

People today value quick reasoning more than encyclopedic memory. A century ago, the opposite was true. Future societies might define intelligence in ways we haven’t imagined, depending on what jobs remain.

The Risks of False Positives and False Negatives

A test that claims we’ve reached AGI when we haven’t would be dangerous companies might deploy systems in medicine or law that can’t handle rare edge cases.

But a test that underrates AI could be equally risky. Imagine dismissing an AI system as “not real AGI” even as it quietly destabilizes financial markets or manipulates political discourse with precision targeting. Intelligence sneaks up on you.

Toward a Better AGI Benchmark: What Should We Look For?

A meaningful AGI test might need to evaluate:

1. Adaptability

Can the AI learn skills it wasn’t trained for, without massive new datasets?

2. Embodied reasoning

Does it understand physical causality like why a cup placed upside down won’t hold water?

3. Social intuition

Can it trace beliefs, intentions, and misunderstandings in complex situations?

4. Robustness across domains

Humans aren’t savants in everything, but we adapt reasonably across fields. An AGI should too.

5. Meta cognition

Can it recognize its own uncertainty, check its work, or change strategies?

Right now, even the strongest models only show flashes of these abilities.

The Strange Future of Intelligence Testing

Some researchers think that by the time we create AGI, we’ll know instinctively because the systems will be able to explain themselves, design new tests, and reason about their own limitations.

Others argue the opposite: AGI will be so alien that no human test will ever capture its true nature. It might be superhuman in some areas, child like in others, and inscrutable everywhere else.

A few even suspect AGI will arrive gradually, not with a cinematic “birth moment,” but through a long, subtle creep where each model seems only slightly better than the last until, suddenly, the entire definition of intelligence feels outdated.

So Will We Recognize AGI When We See It?

Honestly… maybe. But it won’t be because of one perfect test. It’ll be because patterns emerge:

We’ll see models learning new concepts on the fly.
We’ll watch them reason about situations they’ve never encountered.
We’ll notice they stop making certain categories of “dumb mistakes.”
And eventually, they’ll surprise us in ways that feel less like glitches and more like insight.

The real challenge isn’t scoring AI intelligence it’s deciding what kind of intelligence we’re expecting in the first place.

Humans spent centuries trying to understand our own minds. Machines might force us to start over.

Open Your Mind !!!

Source: IEEE

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