Does AI already have human-level intelligence? The evidence is clear

When will artificial intelligence systems achieve artificial general intelligence (AGI), when defined as the ability to convincingly imitate the “broad, flexible cognitive competence” of a person? We’re already there, argue four scholars in philosophy, machine learning, linguistics and cognitive science who look back to a seminal 1950 paper by Alan Turing as their guide. Large language models can already chat convincingly, write passable poetry and prose, solve mathematics problems, propose scientific experiments and assist in writing computer code. “The machines Turing envisioned 75 years ago have finally arrived, in a form both more alien and more human than anyone imagined,” write the authors, who argue that recognising this matters for understanding our minds and world.

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AGI, Stochastic Parrots, and the Culture That Defines Intelligence

"Furthermore, there is no guarantee that human intelligence is not itself a sophisticated version of a stochastic parrot."

That sentence, from a recent Nature commentary arguing that artificial general intelligence (AGI) may already be here, does more than provoke. It is a hinge — a small linguistic pivot around which a vast conceptual shift quietly turns. To read the claim at face value is to miss the larger, subtler work being done: a redefinition of intelligence itself, human and artificial alike.

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AGI Already Here? The Nature Argument

The authors claim that large language models and related systems already demonstrate the kind of broad, flexible cognitive competence that Alan Turing imagined in 1950. These systems can chat convincingly, generate prose and poetry, solve mathematical problems, propose scientific experiments, and even assist in writing code. By Turing’s criterion — the imitation game — these capabilities are presented as evidence that AGI is not a distant horizon, but a present reality.

At first glance, this feels startlingly plausible. Chatbots can answer questions with fluency, propose solutions with apparent insight, and mimic reasoning across domains. Yet the claim rests on a subtle, often unspoken manoeuvre: intelligence is defined by performance on tasks we, historically and culturally, consider meaningful. Benchmarks and success criteria are not neutral measures; they are socially stabilised definitions.

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The Meta Problem: What Counts as Human Intelligence?

The Nature commentary is compelling because it leverages unexamined assumptions about human intelligence. Intelligence is treated as stable, measurable, and largely symbolic: the ability to communicate, reason, and solve problems in literate, analytic ways. But this proxy omits much of what humans actually do: navigate risk, act within moral or normative frameworks, participate in embodied practices, and respond to real-world consequences.

By suggesting that humans might themselves be “sophisticated stochastic parrots,” the article flattens the human into a process of pattern extraction, a subtle but radical deflation that allows machines to be measured on the same plane.

“If humans are sophisticated parrots, why can’t machines be too?”

The deeper meta-move is epistemic: uncertainty about the nature of human intelligence is leveraged to lower the threshold for recognising intelligence in machines. What appears humble is actually a strategic repositioning.

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The Circularity of AI Culture

Here we encounter a deeper structural point: intelligence, as currently defined in AI discourse, is co-constituted by the culture of its developers. Consider the loop:

1. Developers set tasks — benchmarks, coding challenges, dialogue prompts — based on what they value and can measure.

2. AI systems perform these tasks, optimised to succeed.

3. Success validates the AI as “intelligent.”

4. That validation shapes the culture, reinforcing which tasks matter, which challenges are prioritised, and what counts as intelligence.

In short: the tasks define intelligence, the AI performs the tasks, and the AI’s performance confirms the validity of those tasks.

This is not merely a critique of metrics; it is a structural observation about mutual actualisation. Intelligence is not simply a property of the machine; it emerges from the interaction between human priorities, institutional practices, and technological affordances.

The circularity is invisible because it is internal to professional practice. From the outside, performance looks natural, inevitable, and “objective.” Yet it is profoundly contingent, culturally and historically situated.

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Correlation, Structure, and the Flattening of Intelligence

The article reinforces this perspective with the line:

"All intelligence, human or artificial, must extract structure from correlational data; the question is how deep the extraction goes."

This is a masterstroke of conceptual framing. Intelligence is reduced to pattern recognition and abstraction. Depth, not kind, becomes the relevant metric. Qualitative, embodied, and normative aspects of human cognition are quietly flattened into a single continuum.

The rhetorical power is subtle but immense. Humans and machines are rendered comparable not because they share experience or consequence, but because they share a capacity for structural extraction. Depth becomes the axis along which competence is measured; the stakes, the embodiment, the meaning, and the lived consequences of action are bracketed away.

“Once the debate is framed around depth of extraction, scale becomes destiny.”

The human is reconstructed to fit the machine, and the machine is praised for mirroring the flattened human. The explanatory direction matters: we do not evaluate the machine against the human; we evaluate both through the lens of pattern extraction, and the human is quietly redefined to fit.

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Implications and Takeaways

Viewed meta-analytically, the Nature article does less reporting than cultural reconfiguration. Intelligence is not a pre-existing property; it is co-constructed through human practice, task design, and perceptual validation. Declaring AGI “already here” is thus as much a reflection of cultural priorities as it is a statement about technological capacity.

Two consequences follow:

1. The question of AGI shifts

   From:

   “When will AI become intelligent?”

   To:

   “Which aspects of human intelligence do we prioritise, for whom, and under which cultural regimes?”

2. The human is subtly redefined
   By flattening human intelligence into a continuum of depth in pattern extraction, our own conception of mind, agency, and cognition is quietly reshaped. Machines are not just performing tasks — they are participating in a mutual recalibration of intelligence itself.

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A Closing Reflection

The Nature commentary does not merely announce AGI; it reframes what counts as human intelligence. By normalising structure extraction as the essence of cognition, it creates a space in which machines appear not merely competent, but generically intelligent.

Yet the most compelling intelligence may reside not in machines, but in the capacity to perceive and critique the loop by which intelligence is defined. Recognising the co-constitution of human and machine intelligence — the mutual shaping of definitions, priorities, and validation — may be the most reflexively powerful act of cognition we can perform today.

“Perhaps the most interesting intelligence is not in the machines at all, but in the ways we define, measure, and collectively actualise intelligence through our own cultural practices.”

In the end, the stochastic parrot is not only a mirror for AI, but also a mirror for us. The intelligence that matters is the intelligence that notices the mirror — and steps back, just long enough, to see the loop itself.

430,000-Year-Old Wooden Tools Are the Oldest Ever Found

Wooden objects carrying the marks of carving and use could be the oldest wooden tools ever found. Researchers dated the artefacts, found in what is now Greece, to 430,000 years ago — and suggest they might have been made by early Neanderthals or their ancestors, Homo heidelbergensis. A separate study describes 480,000-old flint-knapping tools made from antler and elephant bone, from what is now the United Kingdom. Organic artefacts are a rare find because they’re less likely to endure than stone, and show how “bone and wood were probably more valuable for our ancient ancestors,” says archaeologist Thomas Terberger. “Imagine how many tools you can make from a single large bone of an elephant.”

First animals known to fake the smell of flowers

Larvae of the European blister beetle (Meloe proscarabaeus) mimic the scent of flowers to find a ride to their next meal. The larvae produce floral-smelling compounds to lure bees, then hitchhike back to the bees’ nest and eat the eggs they find there. This trickery is the first known example of an animal imitating the scent of a flower.

Schrödinger’s cat just got bigger: quantum physicists create largest ever ‘superposition’

In the iconic quantum double-slit experiment, a photon acts like a wave and interferes with itself — illustrating the mind-boggling concept that particles can exist in a ‘superposition’ of possible locations at once. But just how big can such a quantum object be? At least as big as a blob of around 7,000 sodium atoms, it turns out. Using a painstakingly constructed experimental set-up, researchers managed to observe tell-tale interference patterns for sodium clusters some 8 nanometres wide, with superpositions that stretched across 133 nanometres. The clusters are as big as some viruses, raising the tantalising possibility of putting biological matter through the same experimental set-up.

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This is an impressive and painstaking experiment. Demonstrating clear interference patterns for clusters of around 7,000 atoms, spatially separated by more than 100 nanometres, represents a real extension of the regimes in which quantum descriptions can be experimentally sustained. As experimental control of isolation, coherence, and interferometric precision, the work deserves genuine admiration.

What is worth handling carefully, however, is how such results are often presented.

When articles speak of objects “existing in a superposition of locations at once”, or frame the experiment as probing whether quantum mechanics “still applies” at larger scales, a subtle shift occurs. Formal features of a successful theoretical description begin to be treated as literal claims about what the system is, rather than about how it can be described under tightly controlled conditions.

From a more structural perspective, a superposition is not an ontological state of affairs. It is a theoretical potential: a space of possible outcomes defined relative to a particular experimental arrangement. The interferometer does not reveal a sodium cluster to be “in many places”; it actualises a phenomenon whose meaning is inseparable from the construal that makes it observable.

Seen this way, the familiar question — “where does the quantum world give way to the classical?” — is slightly misplaced. What changes is not the world itself, but the stability of the conditions under which certain descriptions remain coherent. Quantum mechanics does not abruptly fail at larger scales; rather, it becomes progressively harder to maintain the isolation and precision required for quantum descriptions to remain usable.

The real achievement of experiments like this is therefore not that they show ever-larger objects to be “really” quantum, but that they map how far we can extend a powerful theoretical construal before the practical conditions that sustain it dissolve.

Oldest cave painting of red claw hand could rewrite human creativity timeline

The outline of a hand discovered in an Indonesian cave has been dated to at least 67,800 years ago — the oldest known example of cave art in the world. The researchers who discovered the painting suggest that it was made using a stencil that was altered to give the shape a claw-like appearance. The finding provides circumstantial backing for a controversial theory that early humans had arrived in Sahul — the landmass that once encompassed modern-day Australia and New Guinea — by 65,000 years ago, around 15,000 years earlier than previously thought.

These dogs can learn new words just by eavesdropping

Some gifted dogs can learn words for hundreds of objects and pick up new ones just by listening in to the conversations of human family members — abilities that put them on a par with infants at about 18 months old. Only a few animals, including bonobos (Pan paniscus) and an African grey parrot (Psittacus erithacus), have been observed to recognize objects through unique names. Learning words indirectly through watching human interactions is even harder because it requires following a person’s gaze and, to some extent, understanding their intentions.

Flowers hint at maths before numerals

Pottery made by people of the Halafian culture, who inhabited northern Mesopotamia between around 6200 and 5500 BC, is painted with flowers that have 4, 8, 16 or 32 petals, and some show arrangements of 64 flowers. These patterns show a clear understanding of symmetry and spatial division long before written numbers came into use around 3400 BC, argue scientists in a new study. The skill might have helped the Halafian people with tasks such as sharing harvests or dividing communal fields, the authors say.

Wolf pup’s stomach yields DNA from one of world’s last surviving woolly rhinos

A genomic analysis of around 14,400-year-old woolly rhino (Coelodonta antiquitatis) tissue — recovered from the stomach of an ice age wolf (Canis lupis) — has revealed clues as to the cause of the species’ rapid extinction around 14,000 years ago. Researchers compared the animal’s genetic diversity to genomes belonging to older woolly rhino samples and found no evidence of inbreeding, which suggests the species’ downfall was not a long, gradual process. The team instead proposes that a warming climate destroyed the rhinos’ habitat, which caused a swift population collapse.

How did birds evolve?

For more than a century, Archaeopteryx was the earliest known bird genus, and the only one known from the Jurassic period, when birds first evolved. Last year, fossils of a newly discovered genus revealed that Archaeopteryx wasn’t alone: a creature called Baminornis also walked the Jurassic Earth — and flew as well. These two genera of birds are so different that some researchers suggest that the first birds predated them both. The findings are revealing clues about how and why birds evolved, and hint that the evolutionary story of birds has an even earlier chapter than researchers thought.

Poison arrows used 60,000 years ago

Traces of toxic compounds have been found on 60,000-year-old arrowheads, providing the oldest chemical evidence that Palaeolithic hunter-gatherers used poison to bring down prey. Chemical analysis revealed a compound called buphandrine, derived from the poison bulb plant (Boophone disticha), on arrowheads discovered in KwaZulu-Natal, South Africa. The use of poisons “shows advanced planning, strategy and causal reasoning” among hunter-gatherers, says archaeologist Justin Bradfield. It also suggests that they had a complex understanding of the properties of plants.