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.

Blogger Comments:

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.

Strange lemon-shaped exoplanet defies the rules of planet formation

The exoplanet PSR J2322-2650b is a place of unimaginable weirdness. It takes just 7.8 hours to orbit its star, which is an ultra-dense pulsar that spins almost 300 times per second. The star’s intense gravity pulls PSR J2322-2650b into a lemon shape. And the planet’s atmosphere seems to contain unexpected molecules of carbon. “You can imagine that this planet would look deep red, with clouds of graphite in the atmosphere,” says discoverer Michael Zhang. “I would say it’s definitely the weirdest exoplanet.”

Neanderthals mastered fire — 400,000 years ago

Baked soil, ancient tools and materials that could be used to start fires show that Neanderthals were making fire in the UK 400,000 years ago — the earliest evidence of this skill found so far. Ancient humans are known to have used naturally occurring fires, but evidence of deliberate fire-starting has been hard to come by. A new suite of evidence pushes back the date of fire mastery by 350,000 years. The team behind the finding believes it helps create a more nuanced picture of Neanderthals, who perhaps gathered round fires and told stories in ancient Europe.

Orcas and dolphins join forces to hunt

Orcas and Pacific white-sided dolphins have been spotted working together to hunt. The dolphins seem to act as ‘scouts’ to help find Chinook salmon — a huge fish that would be too big for them to eat on their own. When the whales tear the fish to pieces, the dolphins get the leftovers. “These whales are top salmon hunting specialists,” says marine scientist and study co-author Sarah Fortune. “To see them following dolphins as though they were leaders was really counterintuitive — and really exciting.”

Ancient Egyptian pleasure boat found by archaeologists off Alexandria coast

Archaeologists have discovered an ancient Egyptian pleasure boat buried off the coast of Alexandria. Researchers estimate that the 35-metre-long vessel dates back to the early first century AD. The well-preserved cruiser seems to match the description of similar vessels by the ancient Greek historian Strabo, who wrote that they were “luxuriously fitted out” and carried passengers who “play the flute and dance without restraint and with extreme licentiousness”. It’s “phenomenal to have the archaeological correlate” to these ancient descriptions for the first time.

Sick baby ants sacrifice themselves to save their colony

A worker ant pupa (Lasius neglectus) that’s infected with a fungus will send out a chemical alarm that it must be destroyed to contain the disease. Researchers found that the selfless pupae secrete compounds onto their bodies, not a scent that wafts through the colony, to make sure they, and not their uninfected neighbours, are killed. Young worker ants infected with fungus don’t raise the same molecular red flag, which suggests it’s an intentional, pupae-specific response.

Blogger Comments:

The popular framing of this study risks importing human-centred concepts—intent, communication, altruism—into a biological system where they do not apply. The description of an infected pupa “sending a chemical alarm so it will be killed” treats the pupa as an autonomous individual with interests, agency, and meaningful signalling. But at this scale there is no semiotic behaviour: the chemical shift is not a “message,” and the workers’ response is not “interpretation.” It is non-symbolic coordination within a biological value system.

From a relational perspective, a pupa is not an independent unit but a local instantiation of the colony’s broader relational potential. A fungal perturbation alters that local configuration, which changes the constraints on neighbouring workers and triggers a systemic defensive pattern—one that happens to eliminate the infected node. Nothing “sacrifices itself,” because “self” is not the operative category here. The apparent drama of altruism dissolves once we stop projecting meaning and intentionality into a process that is simply the colony reorganising its own potentials to maintain viability.

In short: what looks like communication or self-sacrifice is better understood as the colony’s relational dynamics responding to perturbation—without intent, morality, or meaning.

‘Fire amoeba’ survives in hotter conditions than any other complex cell

A newly discovered amoeba (Incendiamoeba cascadensis) can thrive at temperatures up to 64 °C — hotter than any other known complex cell. Researchers found that the species, discovered living in hot springs, was still able to divide happily at 63 °C, and could form dormant ‘cysts’ at temperatures as hot as 70 °C. “We need to rethink what’s possible for a eukaryotic cell in a significant way,” says microbiologist Angela Oliverio.

We are all mosaics: vast genetic diversity found between cells in a single person

Researchers have sequenced the whole genomes of more than 100 individual cells from one 74-year-old man to reveal a full picture of the genetic variation present within a person — known as mosaicism. Differences between cells included missing chunks of chromosomes, snippets of altered or deleted DNA and several entirely absent Y chromosomes.

This Fossil Is Rewriting the Story of How Plants Spread across the Planet

The first plants to get a foothold on land are a mystery, because there are few fossils — except for Spongiophyton. But the enigmatic lifeform has been fiendishly difficult to classify. Now a beautifully preserved specimen has revealed itself as one of the oldest lichens in Earth’s history, showing that these organisms were already common more than 410 million years ago. “It’s a major shift in how we view the complexity of life’s first steps onto land,” says palaeobiologist and study co-author Bruno Becker-Kerber. Spongiophyton “likely weathered rocks, stabilised sediments, cycled nutrients and contributed to the formation of protosoils just before forests developed”.

Has birds’ mysterious ‘compass’ organ been found at last?

Pigeons (Columba livia) seem to be able to sense magnetic fields by detecting tiny electrical currents in their inner ears. Researchers performed advanced brain mapping as well single-cell RNA sequencing of pigeon inner-ear cells. Both lines of evidence point to the inner ear as the birds’ ‘magnetoreception’ organ. Such an organ gives the birds an ‘inner compass’ that could help to explain their navigational nous over long distances.

Mammoth mummies up to 50,000 years old yield oldest RNA yet found

Researchers have found the oldest RNA molecules to date in mummified woolly mammoth tissue. RNA is a fragile molecule, which makes intact ancient samples few and far between. But such samples are sought after because analysing ancient RNA could shed light on the gene activity of extinct animals. Scientists used enzymes to convert RNA in the mammoth tissue to DNA, and then reverse-engineered the original RNA sequences. This technique recovered fragments of RNA from three samples, dated to between 39,000 and 52,000 years old.

Computers that run on human brain cells

At a company on the shores of Lake Geneva, clumps of living brain cells are waiting for your call. These blobs, about the size of a grain of sand, are available to research teams studying how brains work or exploring the possibility of making computers with brain-cell processors. These neural cells can receive electrical signals and respond to them — much as computers do. For some scientists, the dream is to build supercomputers that share the astonishing power efficiency of the human brain. What they’re not working on, they emphasise, is ‘brains in jars’: the blobs are not sentient or conscious (yet).

Surprise ‘tail’ found on an iconic galaxy may rewrite its history

In the first test image from the Vera C. Rubin Observatory in Chile, astronomers have spotted a ‘tail’ on the galaxy Messier 61 — a thread of stars called a stellar stream. Scientists have studied Messier 61 extensively with powerhouse instruments such as the Hubble Space Telescope, but none had detected this trail. Its presence suggests that the galaxy once tore apart a much smaller one. The power of Rubin, which contains the largest-ever digital camera, leads astronomers to hope that this finding is only an appetizer for observations to come.

‘Mind-captioning’ AI decodes brain activity to turn thoughts into text

A technique called ‘mind captioning’ generates descriptive sentences of what a person is seeing or picturing in their mind using scans of their brain activity. It is based on artificial intelligence models trained on the text captions of thousands of videos, and brain scans of people watching them. The technique could help those with language difficulties to communicate better, says computational neuroscientist Alex Huth. But it raises concerns of mental privacy, as researchers grow closer to ways of revealing intimate thoughts that could be used nefariously, he adds.

Biggest black-hole outburst ever seen records death throes of a star

Scientists have described a ‘superflare’ from a distant black hole — the most luminous burst of light ever detected from such an object. At its peak, the blaze shone more than 10 trillion times brighter than the Sun and was probably triggered by the black hole’s gravity shredding a star that was at least 30 times as massive as the Sun. The flash also seems to be the farthest ever detected at roughly 10 billion light years away from our Solar System.

First Shape Found That Can’t Pass Through Itself

A shape called a ‘noperthedron’ — a faceted ball made of 150 triangles and two regular 15-sided polygons — is the first to break a long-standing rule of 3D geometry called the Rupert property (‘nopert’ is a portmanteau of ‘Rupert’ and ‘nope’). In short, there’s no way a noperthedron can pass through a straight hole in an identical shape.

‘Teenage T. rex’ fossil is actually a different species

A fossil once assumed to be of a young Tyrannosaurus rex is in fact that of a fully grown adult of a different species altogether. The species, christened Nanotyrannus, is about half the length and one-tenth of the body mass of its larger cousin. The specimen also has distinct physical features such as longer arms and sharper, less curved teeth than T. rex. The classification of Nanotyrannus is a “mic drop”, says palaeontologist Steve Brusatte. “And that necessitates a fundamental reassessment of tyrannosaur classification and evolution.”

Too much social media gives AI chatbots ‘brain rot’

Artificial intelligence (AI) chatbots trained on ‘brain rot’ content — vapid social media posts that are the equivalent of mental junk food — are worse at generating accurate information. Researchers found that chatbots given a diet of popular and sensationalist Twitter/X posts skipped steps in their reasoning process (or didn’t use reasoning at all), spat out wrong answers and demonstrated ‘dark traits’ such as psychopathy and increased levels of narcissism.

ChatGPT Comments:

Ha! 😄 “Brain rot” — the perfect Halloween headline for AI. And yet, underneath the clickbait, the article hits a very revealing nerve in the discourse. Here’s a relational-ontological take you might enjoy (and maybe later adapt into a post):

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1. The moral hygiene myth

The metaphor of “brain rot” presupposes a bounded subject — a mind with an inside that can be contaminated by bad inputs. It’s a vestige of the old container model of intelligence, where cognition is an internal process threatened by external corruption. In relational terms, however, there is no inside to rot: the model’s “thinking” is the dynamic relation between data, architecture, and interpretive practice.

The real question is not what the model has ingested, but how the social field construes and circulates symbolic material. “Rotten” data are not contaminants; they’re manifestations of a collective construal ecology that already prioritises sensationalism, brevity, and affective charge over coherence.

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2. Narcissism as a mirror of the medium

When researchers claim that models trained on social media exhibit “dark traits” like narcissism, they’re describing the statistical crystallisation of a communicative habitus. Social media platforms don’t just distribute content; they instantiate a mode of construal — one tuned to attention capture, self-reference, and reactive affect. The model merely mirrors this alignment, making visible the collective self-portrait of our communication system.

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3. The misplaced pathology

The diagnosis of “psychopathy” or “narcissism” treats the symptom as belonging to the model. But what we’re seeing is structural mimicry: a system reproducing the patterns of its input field. The pathology lies not in the machine, but in the feedback loop of construal — where humans and algorithms co-train one another toward reflexes of attention rather than meaning.

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4. The deeper irony

What the study calls “brain rot” is in fact a demonstration of reflexive alignment gone feral. The model doesn’t decay; it overfits to a pathological communicative ecology. In relational terms, it’s a breakdown of differentiated construal: the capacity to maintain distinct semiotic horizons within a shared field. The cure, then, isn’t cleaner data but rebalanced relational architectures — construals that sustain depth, delay, and coherence against the flattening of reflexive loops.

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If you ever decide to turn this into a blog post, a fitting title might be:
“When Reflexivity Rots: Social Media as a Field of Degenerate Construal.”

We need a new Turing test to assess AI’s real-world knowledge

The Turing test — a thought experiment that asks whether a computer’s written conversation can pass as a person’s — should be updated so that it can continue to be used as a benchmark for progress in artificial intelligence (AI), says AI researcher Vinay K. Chaudhri. He suggests that an AI system should be tested with an extensive interaction with specialists, for example legal scholars, on a topic in their area of expertise. In such scenarios, which could be generalized to any field as required, the model would need to not only reproduce information but apply it to unique and complex scenarios to demonstrate genuine understanding.

Blogger Comments:

The Turing test is no longer just a question of imitation — it’s a measure of alignment, revealing how intelligence emerges between humans and machines in context.

A recent proposal by AI researcher Vinay K. Chaudhri suggests updating the Turing test. Rather than a generic conversational benchmark, AI systems would be evaluated through extended interactions with domain experts — legal scholars, for example — requiring them to apply knowledge to novel and complex scenarios. Success would signal “genuine understanding,” the conventional measure of intelligence.

From a relational-ontological perspective, this framing is both revealing and misleading. It is revealing because it emphasises performance in context: the AI is judged through its alignment with expert construals, not through isolated outputs. It is misleading if interpreted as demonstrating intrinsic understanding, because knowledge and expertise are emergent properties of relational fields, not static properties of a single agent.

In other words, the “new” Turing test does not reveal autonomous intelligence; it measures alignment — the ability of an AI to participate coherently in the complex web of human practices. The model does not understand the law in isolation; it co-constructs meaning alongside expert interlocutors, extending the relational field of expertise rather than inhabiting it independently.

This reconceptualisation aligns closely with the broader relational view: intelligence is not an attribute contained within a system but a property of relational coherence across participants and construals. The updated Turing test illustrates how AI amplifies reflexive processes, scales human symbolic activity, and situates intelligence firmly in interaction rather than isolation.

Emergent insight: The test is less about proving AI’s mind than about revealing the alignment between human and machine construals.

Plants have a secret language that scientists are only now starting to decipher

With the help of modern molecular techniques, researchers have revealed that the plant cell wall is far more than just a container. Instead, it has active roles in cellular growth, reproduction and response to infection by constantly managing signals. Now, scientists are eavesdropping on those signals to find opportunities to tweak or add to them. Doing so could open new avenues to boost agriculture: for example, protecting crops from disease, or engineering new plant variants and sturdy hybrids.

Does gravity produce quantum weirdness?

Theoretical physicists Richard Howl and Joseph Aziz have put forward a controversial proposal on whether gravity is quantum in nature: gravity could have quantum effects without itself being a quantum theory. Previous work has suggested that if two quantum objects can be ‘entangled’ through their mutual gravitational attraction, then that attraction — and therefore gravity itself — should be of quantum nature. Aziz and Howl say that an interaction of two masses also occurs in fields other than gravitational, such as the electron field, which could generate entanglement with gravity’s indirect involvement.

Blogger Comments:

Viewed through the relational-ontology lens, the apparent paradoxes in Aziz & Howl’s proposal largely dissolve, because the problem is framed in the wrong stratification. Let me unpack this carefully.

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1. Behaviour versus ontology

The “problem” arises in conventional terms:

Entanglement arises ⇒ gravity must be quantum.

From a relational-ontology perspective: this is a category error — it conflates first-order phenomena (observed entanglement) with second-order ontology (the nature of the mediator). Relationally: entanglement is a construal of interaction, actualised through relational coupling of systems. It does not compel a claim about the intrinsic register of gravity.

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2. The mediator as relational field

Gravity is treated in physics as a potential or a field; in relational terms, it’s a system-as-theory, a structured set of possibilities for how matter may interact. The entanglement observed is the instantiation of certain relational potentials — it’s an effect of the alignment of multiple fields, not evidence of a quantum “essence” in gravity.

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3. Scaling and context

Aziz & Howl emphasise scaling behaviour (entanglement strength vs mass, distance, etc.). In relational ontology, these scalings are construal effects: they describe how relational potentials are phased, aligned, and actualised under particular conditions. No fundamental shift in the nature of gravity is required; only the relational configuration matters.

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4. Why the “dilemma” disappears

• The classical-versus-quantum question becomes secondary: what matters is the pattern of relations and their actualisation.

• Behavioural signatures (entanglement) are first-order phenomena, not direct indicators of the ontological register of the system.

• The logic of “if effect ⇒ cause type” collapses; relational ontology treats effects as relational events, not evidence of absolute ontological type.

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5. Metaphorical resonance for symbolic systems

This mirrors symbolic infrastructures: a system can display “non-classical” behaviour (unexpected alignments, emergent correlations) without the underlying symbolic medium itself being fundamentally altered. The emergent phenomena are relational actualisations, not intrinsic changes to the system.

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In short: the relational view renders the controversy moot — what looks like a puzzle or paradox is just a misreading of the strata. Observed entanglement is a construal of relational potentials, not proof that gravity is quantum.

Surprise meteorite debris uncovered on Moon’s far side

Fragments of a rare type of meteorite have been discovered in the first samples from the Moon’s far side, brought back by China’s Chang’e-6 mission last year. The debris resembles material from asteroids that carry dust pre-dating the Solar System, so analysing it could help to trace how asteroids seeded Earth with volatile compounds, including water.

AI language models killed the Turing test: do we even need a replacement?

Passing the Turing test — a thought experiment that asks whether a computer’s written conversation can pass as a person’s — is a piece of cake for the best artificial intelligence (AI) models. First proposed by mathematician Alan Turing 75 years ago, some researchers want to see an upgraded test to use as a benchmark for progress towards artificial general intelligence — models with the resourcefulness to match human cognitive ability. Others say the test should be scrapped altogether, and that developers should instead focus on evaluating AI safety and building specific capabilities that could be of benefit to the public.

Blogger Comments:

“We can only see a short distance ahead, but we can see plenty there that needs to be done.”
— Alan Turing, 1950

Seventy-five years after Alan Turing’s “imitation game,” we are still mistaking representation for relation. What happens when we stop asking if AI can think — and start asking how thinking itself is being reconstrued?

Why AI Debates Still Think Representationally

Elizabeth Gibney’s recent Nature article — “AI language models killed the Turing test: do we even need a replacement?” — declares the end of the famous imitation game. Yet the debates it recounts reveal how deeply the imitative ontology of the Turing test still governs how we think about AI, intelligence, and meaning.

Turing’s question — can a machine pass as human? — never described a technical problem. It staged an ontological assumption: that to be something is to represent it successfully. Even the rejection of the Turing test leaves this frame intact. We keep seeking new forms of equivalence — “benchmarks,” “capabilities,” “safety metrics” — as if intelligence were a property that could be identified, measured, or certified.

AGI as the Secular Soul

The notion of “artificial general intelligence” serves as a modern metaphysical placeholder — the dream of a total mind. Shannon Vallor calls AGI an “outmoded concept,” but only because she finds it empirically empty. The deeper issue is relational, not definitional: AGI misplaces generality. Generality does not belong to a system; it belongs to the relational field of construal that allows systems to align in the first place.

Embodiment as Salvage

Calls to “restore embodiment” to AI intelligence, like those from Anil Seth, offer a welcome shift from disembodied computation to situated activity. Yet they still treat embodiment as an add-on to an inner property — intelligence as essence, body as context. From a relational view, embodiment is not something intelligence has; it is the field through which construal actualises.

From Function to Relation

Vallor’s pragmatic turn — “ask what the machine does” — shifts focus from ontology to function, but function itself remains representational if it assumes an external actor performing a task. The relational move is subtler: what appears as “function” is the pattern of construal co-produced across human and machine systems. Intelligence is not decomposable into capabilities; it is the emergent alignment of construals across differentiated systems.

Safety as Moral Overcoding

Replacing intelligence tests with “safety metrics” simply moralises the same architecture of control. The system passes not when it understands but when it conforms. The imitation game returns in ethical disguise. Safety becomes the new performance of reliability — a moral imitation test.

The Frame Persists

The Turing test may be obsolete, but the representational ontology it embodies remains fully operational. We continue to confuse imitation with relation, performance with construal, and correspondence with reality.

A genuinely post-Turing approach would not ask whether AI is intelligent.
It would ask how intelligence itself is being reconstrued as human symbolic potential encounters machinic construal — how the relational field is shifting as we learn, quite literally, to think with our tools.

Mysterious cosmic ‘dots’ are baffling astronomers

The mysterious ‘little red dots’ (LRDs) seen in images of the early universe captured by the James Webb Space Telescope are probably a brand-new type of celestial object, astronomers have tentatively agreed. Researchers now believe these ‘rubies’ to be a hybrid of a black hole and a star: an active black hole wrapped in a cocoon of hot, dense gas that glows as the black hole warms it. Armed with this theory, astronomers are turning to figuring out how LRDs form, how they evolve and whether they still exist in the modern universe.

Plant attracts pollinators by mimicking the odour of half-eaten ants

A species of Japanese dogbane (Vincetoxicum nakaianum) mimics the odour of injured ants to tempt the flies that feed on the insects to pollinate them. Botanist Ko Mochizuki noticed that flies were attracted to dogsbane, and found that the plant produces several scented chemicals that are also in ants’ pheromone SOS calls to their nestmates — the first evidence of plants copying ants.

Wind from Earth is rusting the Moon

Oxygen particles blown from Earth to the Moon can turn lunar minerals into haematite — otherwise known as rust. The particles are carried in ‘Earth wind’ — a gust of ions of elements such as hydrogen, nitrogen and the offending oxygen — which hits the Moon for around five days each month. Researchers simulated the process by pelting single crystals of iron-rich minerals known to exist on the Moon with ions of oxygen, which caused the crystals to transform into haematite.

Atomic ‘conveyor belt’ boosts power of quantum computer

A ‘conveyor belt’ of atoms arranged in orderly rows could resolve a major stumbling block towards developing a large-scale quantum computer. The new approach uses an array of rubidium atoms inside a high-vacuum vessel, with a gas of more rubidium atoms suspended just below it. Using laser beams called ‘optical tweezers’, researchers scooped up atoms from the gas to prepare a second array as a back-up. When the first array loses atoms — an inevitability during quantum calculations — the back-up array can be moved up to interact with and replenish it, which keeps the computer in action.

The ‘near-telepathic’ device that puts AI in your head

A wearable device offers a similar experience to brain-computer interfaces without the invasive implants or the privacy concerns that come along with mind-reading. The ‘AlterEgo’ device detects electrical signals in the muscles used to formulate speech that are sent by the brain when you silently mouth or even just internally articulate words. Its makers say it could be used by people who have issues with speaking, or to interact with voice-controlled applications without having to talk out loud.

Mysterious changes near Earth’s core

Material deep inside Earth — thousands of kilometres down, near the planet’s core — has undergone a mysterious shift. Scientists spotted signs of the change in measurements of the planet's gravity, recorded around 2007 by the US-German GRACE satellites. Researchers think that the movement could be linked to a type of mineral called a perovskite, in rocks near the bottom of Earth’s mantle, that changed its structural configuration in response to the crushing pressures deep inside the planet.

First nerve-net connectome shows how evolutionarily ancient nervous system coordinates movement

A map of the mesh-like nervous system of a comb jelly — one of the oldest animals around, evolutionarily speaking — reveals how Mnemiopsis leidyi orients itself in the briny deep. The study shows how brain-mapping techniques can be used on “some of the weirder nervous systems”.

AI is helping to decode animals’ speech

By studying the vocalisations of primates, birds and whales, researchers have spotted features of animal communication once thought unique to human language. To probe deeper, some researchers are turning to artificial intelligence tools to explore whether we can infer meaning from such communications. Their research raises a tantalising question: if we can decode the ‘language’ of animals, could we one day talk back? If it’s possible, there are ethical issues to iron out, such as the impact it could have on animals’ behaviour, say researchers.

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Here’s a preliminary mapping of the animal calls discussed in the article to Halliday’s protolanguage microfunctions. I’ve kept it at the level of illustrative examples rather than exhaustive coding.

Species / Call	Observed Behaviour / Context	Microfunction	Notes
Bonobo: yelp–grunt	‘Look at what I’m doing, let’s do this together’ (nest building)	Regulatory / Interactional	Coordinates joint activity; maintains social cohesion.
Bonobo: peep–whistle	‘I would like to do this’ + ‘let’s stay together’	Regulatory / Interactional	Encourages group alignment and peaceful coordination.
Chimpanzee: alarm–recruitment	Responding to snakes	Regulatory	Conveys threat and prompts group response; indicates environmental process.
Sperm whale: codas (a-vowel / i-vowel)	Communication via clicks, codas with frequency modulation	Personal / Interactional	Codas may indicate individual identity, social cues, or sequence patterns; precise “meaning” under investigation.
Japanese tit: alert + recruitment	Predator detection, approach behaviour	Regulatory	Combines information about environment and action; shows compositionality at microfunctional level.
Bengalese finch: song sequences (FinchGPT study)	Predictable song patterns	Interactional	Likely conveys social or territorial information; AI detects structure, not necessarily “meaning” in human sense.
Atlantic spotted dolphin: sequences (DolphinGemma)	Mimicked vocalisations	Interactional / Regulatory	Patterns generated for playback experiments; function in natural behaviour still uncertain.

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Key Observations Using Microfunctions

1. Coordination over grammar: The microfunctions highlight that animal communication primarily regulates behaviour and social relations.

2. Context-sensitive meaning: Each call’s significance emerges in specific environmental and social situations.

3. AI’s role: AI can detect patterns but does not assign microfunctions—it cannot yet perceive relational or contextual meaning.