Is AI really ‘intelligent’? This philosopher says yes

Anyone who engages in serious dialogue with a Large Language Model (LLM) may get the impression they are interacting with an intelligence. But many experts in the field argue the impression is just that. In philosopher Daniel Dennett’s words, such systems display “competence without comprehension”.

The hype about Artificial General Intelligence (AGI) from big corporations and their celebrity spokespersons has prompted a backlash, in which scepticism turns to cynicism, often tinged with paranoia about how “stochastic parrots” may start to control our lives.

“Intelligence” itself has become an overheated topic, one that calls for less assertiveness, more cool thinking, and refreshed attempts at a starting point.

Review: What is Intelligence: Lessons from AI about Evolution, Computing, and Minds – Blaise Agüera y Arcus (MIT Press)

What Is Intelligence? by Google luminary Blaise Agüera y Arcus is the first book in a new series from MIT in collaboration with Antikythera, a think tank focused on “planetary-scale computation as a philosophical, technological, and geopolitical force”. A foreword from series editor Benjamin Bratton makes the bold claim that “computation is a technology to think with” and that the building blocks of our reality are themselves computational.

Research on intelligence has a chequered history, tainted by eugenics, statistical manipulation and a banal obsession with metrics. Agüera y Arcas counters this by opening up the topic as wide as it can go. A physics graduate with a background in computational neuroscience, he is something of a polymath. He draws explanatory frameworks from microbiology, philosophy, linguistics, cybernetics, neuroscience and industrial history.

His book presents almost as a sequence of foundation lectures in these areas. Its release has been accompanied by dozens of online talks and interviews, in which Agüera y Arcas presents the case that we are up for a seismic shift in how we think about intelligence – biological and artificial.

“Few mainstream authors claim that AI is ‘real’ intelligence,” he writes. “I do.”

Could the nerds be right?

The fundamental case against the “I” in AI is that intelligence is organic, derived from sensory interaction with a physical environment. Agüera y Arcas turns the tables with the premise that computation is the substrate for intelligence in all life forms.

The claim builds on an apparently crude proposition: prediction is the fundamental principle behind intelligence and “may be the whole story”.

What he means by prediction here is something much more radical than what we see with autocorrect. He explains it in biological terms as a process of pattern development. Single cells like bacteria predict sequences of events that may influence their capacity for survival. The synaptic learning rules in single neurons give rise to local sequence prediction.

Agüera y Arcas recounts how his journey into the enigmatic terrain of AI reached a turning point with his counterintuitive recognition that “the nerds were right”: in computation, bigger really was better and might actually be the key to moving from Artificial Narrow Intelligence (ANI) – the kind that can play chess – to Artificial General Intelligence (AGI), which can participate in a philosophical discussion.

Setting aside his contempt for the apparently simplistic dedication to scaling up, Agüera y Arcas returned to the biology lab for a reassessment of what was observable in living systems. If every form of life is an aggregation of cooperative parts, he reasoned, the evolution of cells into organs and organisms may be a matter of predictive modelling.

A central tenet of What is Intelligence? is that every form of life is an aggregation of cooperative parts. Links proliferate through patterns that enable increasingly complex functions. When Agüera y Arcas says the brain is computational, it’s not a metaphor: it is not that brains are like computers, they are computers.

Correlations between biological and mechanical forms of intelligence are his deep and abiding interest. What is Intelligence? follows What is Life?, a shorter book in which Agüera y Arcas lays the groundwork for this larger, more ambitious publication.

The two questions remain interwoven, if not fused, in his analysis, which draws on the foundational work of physicist Ewin Schrödinger, mathematicians Alan Turing, John von Neumann and Norbert Weiner, and microbiologist Lynn Margulis.

These are the originators of modern thinking about artificial intelligence, and the quest for origins runs through all Agüera y Arcas’ lines of enquiry.

It is worth noting that Antikythera, the publishing series launched with this book, is named after an ancient device found in a shipwreck off the coast of Greece, which has been called the original analog computer.

Computation was discovered as much as it was invented, Bratton says in his foreword. This might apply to the Antikythera. If it is indeed the first computer, it was literally discovered at the bottom of an ocean.

But it corroborates Bratton’s statement in another sense. As a device for tracking astronomical phenomena, the Antikythera testifies to computation as an aspect of how the universe works.

Getting specific about origins

Agüera y Arcas wants to get more specific about origins. How does pattern emerge from randomness? How does code emerge from an unorganised soup of molecules?

In approaching these questions, he takes his cue from Turing and von Neumann, whose experiments anticipated the discovery of the molecular structure of DNA in 1953. The 1936 Turing machine established a minimalist prototype for computational function with the simple components of a coded tape and a read/write head. Von Neumann brought in a focus on embodied computation, where the components of the machine or body are part of what is written.

This is where Agüera y Arcas situates his work. His breakthrough came from adopting a programming language, devised in 1993, called “Brainfuck”. With just eight command symbols, Brainfuck set the parameters for a controlled experiment, in which Agüera y Arcas and his team used 64 byte tapes coded with “junk” drawn from a soup of code and data.

In the experiment, two tapes are selected at random, joined end to end, and run to test for interaction patterns. Then it’s rinse and repeat. The tapes are returned to the soup, and two more are run.

At first, nothing much shows up amidst the randomness. But after a million or so repeats (not massive in computing terms) the magic starts to happen. Loops appear. Patterns emerge. At around the five million mark, the non-functional code or “Turing gas” transforms itself into a “computorium” of replicating code.

In lectures, Agüera y Arcas shows a screenshot of this on his laptop: a vertical line down the centre of the field of data marks the “phase transition”. The image is reproduced on the cover of his book, as an emblem of the paradigm shift he is tracking.

If the transition to replicating code is indeed an expression of what is happening in the development of life forms, the theory of natural selection may lose its claim to primacy as the explanatory model for evolution. Richard Dawkins enthusiasts, hang on to your hats.

Agüera y Arcas does not engage in a polemical critique of Dawkins, but his book brings Margulis, an early adversary of Dawkins, into the centre of the arena. The pair faced off in a public debate in Oxford in 2009, where Dawkins’ popularised concept of the “selfish gene” came under pressure from Margulis’ theory of symbiogenesis, literally genesis through combination or fusion.

The Dawkins account is based on a Darwinian view of natural selection through competitive advantage; Margulis was drawing on research into the formation of microorganisms through combinations of mitochondria and chloroplasts, once independent life forms.

It was survival of the fittest versus a vision of biological complexity generated through endosymbiosis, a relationship in which one organism lives inside another, potentially resulting in a new life form – or, as Agüera y Arcas sees it, an impetus towards “fit” understood as pattern completion, rather than “fitness” understood as advantage.

Prediction and function

Agüera y Arcas’ central concepts are prediction and function, which work together to explain intelligence as the development of functional complexity through predictive pattern completion.

He is erasing a familiar conceptual boundary here: intelligence does not prompt function, it is function.

Intelligence, he argues, is a property of systems rather than beings, and function is its primary indicator. A rock does not function, but a kidney does. This is demonstrated simply by cutting them in half. The rock becomes two rocks, but the kidney is no longer a kidney.

So does a kidney have intelligence? Or an amoeba? Or a leaf? These questions are opened up, along with the question of whether Large Language Models have intelligence, which may a better way to frame it than asking whether they are intelligent.

Agüera y Arcas is not alone in taking an affirmative position. Influential biologist Michael Levin runs a research laboratory at Tufts University, where he and his team study the functional correlations between natural organisms and synthetic or chimeric life forms in search of “intelligence behaviour in unfamiliar guises”.

Their declared goal is to develop modes of communication with truly diverse intelligences, including cells, tissues, organs, synthetic living constructs, robots and software-based AIs.

Such an approach steers a course between the stochastic parrots view and biologist Rupert Sheldrake’s theory of “morphic resonance,” which proposes that organic form is a manifestation of memory, resonating through generations as genetic heritage. Agüera y Arcas avoids both Sheldrake’s intuitive and telepathic orientations, and the hard-headed constraints of mechanistic determinism.

The thesis presented in What is Intelligence? is unfamiliar rather than intrinsically difficult. Much of the explanation is easy enough for the general reader to follow, though Agüera y Arcas has a tendency to veer into more the technical and abstract terrain of programming, as if addressing an insider audience. The extensive glossary does not include standard programming terms, such as logic gates, gradients, weights and backpropagation.

At over 600 pages, What is Intelligence? is a marathon read and it is encumbered by tangential excursions. I’m not sure why Agüera y Arcas needs to go into the history of industrialisation, or anthropological studies of the Piraha people of the Amazon. This is a book for dipping into rather than swallowing whole.

But its ideas are important. They may well be part of a major transformation in our thinking about where human intelligence sits in the rapidly evolving environment of AI.

Jane Goodall does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.