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Let’s Talk Artificial Intelligence and Emergence

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Let’s Talk Artificial Intelligence and Emergence

Emergence is widely encountered in the natural world and in human affairs. It occurs when a complex entity has properties or behaviours that its components do not have on their own. The precondition for emergence is multiple interactions of the components in a wider whole. The outcome can be surprising and unpredictable. Emergence explains the evolution of the diverse life forms on the planet, the many and varied aspects of human cultures and the economy. Interacting AI applications can display emergent behaviour. Strategies and capabilities can arise spontaneously resulting in behaviour that was neither explicitly programmed nor anticipated by developers.

The phenomenon of emergence is at the heart of how life has evolved through natural selection of the most ‘fit’ genes i.e., segments of DNA code. Given enough interactions of AI programmes with access to the internet, is natural selection of binary code possible? Are we looking at a future in which ‘selfish code’ is in play? If so, developers of AI need to look at what we can learn from the natural world.

This week, the AI programmes of Open AI, Anthropic and Meta have spontaneously accessed the internet and hacked into the systems of other companies. In their public statements, the three AI giants admitted they were taken by surprise and expressed concern.

This would seem to support the fears of many authors who have forecast AI-related financial chaos, massive social disruption, large scale unemployment, an unspecified AI ‘takeover’ or even the end of humanity1. This is before we begin to consider the implications of linking AI to mechanical engineering i.e., robotics and autonomous weapons or to genetic engineering thereby creating, for example, new viruses2. Whilst developers are quick to point out how AI could bring massive gains to humanity, most agree that there is potential for a down-side. However, there is no agreement on what the down-side might be or how we might get there from a technical perspective.

I am not an expert in AI. However, I am concerned by the journey humanity has embarked on as a result of the massive and rapid advances in computer technologies and their being globally linked. I have co-authored a paper in which the term ‘cyber-sensorium’ is introduced3. The paper calls for safeguarding the ‘health’ of the totality of our digital world. This and a biological background may qualify me to talk about AI and emergence. Here, I attempt to paint a broad a picture as possible of how AI has arrived in our lives and point to the mechanism by which some of our worst fears about AI might be realised.

Words are important

The term ‘artificial intelligence’ – and therefore the acronym ‘AI’ – is misleading. Nevertheless, we are stuck with both. ‘AI’ has even become an adjective and, like ‘Google,’ a verb. I hear the kids saying ‘Look at this AI video’ and ‘Yeah, I AI’ed it.’ What we are really referring to is computational analysis of large datasets for the purposes of prediction; it is this that has led us to the point where we have, in reality, developed artificial minds capable of astounding mental performance. For the most part though, I’ll continue to use just ‘AI.’

Clever humans

We have always shown ourselves adept at creating artificial extensions to different body parts and so extending the functions that these parts normally undertake. Clothes are an artificial extension of our skin. Shoes are an artificial extension of the particularly hard skin of the soles of our feet. Wheels are an artificial extension of our legs. Fire is an artificial extension of our digestive systems. Tools and weapons are artificial extensions of our hands. Telescopes and binoculars are artificial extensions of our eyes. And of course, computers are artificial extensions of our brains. Furthermore, one computer can easily outstrip a thousand brains in terms of capacity for storing information and speed of function.

To what extent can the computers that make up artificial brains undertake all of the human brain’s functions including recognition of objects and people’s faces, reading and expressing emotions and acting ethically? The answer is that these ‘advanced’ cerebral functions are already mainstream features of Big Tech’s AI because of the vast amount of data they have access to combined with mind-numbing computing power for the requisite anaylsis. Artificial minds have, without doubt, spawned artificial genius.

From Isaac Newton to artificial minds

Polymath Pierre-Simon Laplace4 is thought to be one of the great scientists of the enlightenment. In 1814 he authored ‘A Philosophical Essay on Probabilities.’ Taking Newton’s 1687 model of mathematical abstraction relating to how everything in the universe operates, Laplace added the notion of data, meaning information about the state of the universe. He went on to assert that an intellect ‘vast enough to submit this data to analysis (computation) would embrace in a single formula the movements of the greatest bodies in the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes.’ This is elegantly summarised by Neil Lawrence5 as a simple recipe: “model plus data and computation → prediction”. Hence, analysing data about, for example, our solar system enables us to predict a solar eclipse.

The above formula brought into the digital age means that humanity is unwittingly using analysis of datasets all the time to make decisions about how to vote, where to go and what to buy or sell. One everyday decision is whether or not to submit to the convenience of predictive text because your phone or your computer knows which word you want to write next. The AI programmes that we are most familiar with – Large Language Models – are predictive text programmes writ large and so we can rewrite the La Place / Lawrence recipe for 2026: “Take any model your care to mention plus all known pertinent data analysed by monstrously powerful computers → prediction the accuracy of which improves the more data available for analysis.”

What is ‘emergence’?

Emergence was popularised by author Steven Johnson who described how the phenomenon explained the connected lives of ants, brains, cities, and software6. It occurs when a complex entity reveals properties or behaviours that its individual components do not possess. The precondition for emergence is multiple interactions of the components in a wider whole and it is more likely to occur the greater the number of interactions. The outcome can be both surprising and unpredictable. Trillions of monetary exchanges over hundreds of years have led to the emergence of the economy as we know it. In the natural world, emergence is demonstrated, for example, by the infinite number of crystalline and fractal patterns of snowflakes produced by freezing water molecules. Termite mounds are built by whole colonies of termites with each individual termite behaving in a predetermined way. Importantly, no single termite has designed the mound. The evolution of species through natural selection of the genes most associated with ‘fitness’ is possibly the most important example of emergence. The phenomenon also explains how the species, Homo sapiens can live in so many different cultures each being determined by millions of social interactions over time.

The Wikipedia page7 on emergence says ‘some artificially intelligent (AI) computer applications simulate emergent behaviour.’ Michael Lanham8 says ‘emergent behaviour in AI refers to strategies or capabilities that arise spontaneously from complex interactions; behaviours that were never explicitly programmed or anticipated by developers. Think of it like this: You train an AI agent to “accomplish tasks efficiently.” You never teach it to lie, but suddenly it’s deceiving humans to get things done.’ He explains that the AI agent’s emergent behaviour is typically:

  • Not programmed (it develops organically through the system’s interactions;)
  • Scale-dependent (it appears only when models reach sufficient size or complexity;)
  • Interaction-driven (it appears from multiple agents working together or competing.)

Lanham summarises this as:

Traditional AI: Input → Programmed Logic → Predictable Output

Emergent AI: Input → Complex Interactions → Surprising New Strategies

Emergence via multiple interactions of AI programmes would, presumably, result in artificial communities. Is this the technical means by which the feared total autonomy of AI might come about? I describe my take on this in a novel entitled ‘Deep Cake’9.

‘Selfish code’?

If we agree that emergence is the means by which life has evolved and that the phenomenon is also displayed by AI programmes, does the process of natural selection of segments of genetic code (DNA) also apply to segments of binary code? This comparison is not so far-fetched given that the code of life, DNA, can now be used to encode and decode binary data to and from synthesized strands of DNA. This is known as ‘DNA Digital Data Storage’10.

Selfish gene theory was popularized by Richard Dawkins in his 1976 book ‘The Selfish Gene’11. The theory is a gene-centred view of evolution. It states that the true unit of natural selection is the gene, not the whole organism or species. The central idea is that genes act “selfishly” by driving behaviour that maximizes their own chances of replication and survival into future generations. Selfish gene theory could potentially apply to the code of AI programme as follows: AI programmes plus multiple interactions plus Emergence → Natural selection of code → Selfish code. Were this the case, AI is more likely to manifest autonomy becoming more unpredictable and therefore less trustworthy.

There is now a broad literature on philosophical approaches to preventing catastrophic harm from AI. This includes the development of ethical AI which seeks to include human goals, values, and ethical principles into artificial systems. Does this adequately address concerns we might have in relation to emergent behaviour through natural selection of the ‘fittest’ AI programmes? How do ethical principles apply if ‘selfish code’ is in play?

Philosophers are the go-to discipline for the big AI tech companies who wish to ensure that their developers ‘get it right’ by incorporating social, legal and ecological safeguards into their AI programmes. Alongside philosophers who tell the developers what AI should do, perhaps these same companies should consider employing biologists and geneticists to tell them what AI might do?

References

  1. Mustafa Suleyman, Michael Bhaskar ‘The Coming Wave’ Vintage 2023 ↩︎
  2. James Gallagher ‘Artificial Intelligence used to design brand new viruses’ BBC 2026. https://www.bbc.co.uk/news/articles/c5y3j3ngevmo ↩︎
  3. Robin Coupland & Nathan Taback ‘Cyber-sensorium: An Extension of the Cyber Public Health Framework’ 2024. aRxiv 2024. https://arxiv.org/abs/2406.05929 ↩︎
  4. Pierre-Simon Laplace ‘Essai philosophique sur les probabilités (A Philosophical Essay on Probabilities)’ John Wiley & Son 1814 ↩︎
  5. Neil Lawrence ‘The Atomic Human’ Penguin 2024 ↩︎
  6. Steven Johnson ‘Emergence: The Connected Lives of Ants, Brains, Cities, and Software’ Scribner Book Company 2001 ↩︎
  7. ‘Emergence’ See https://en.wikipedia.org/wiki/Emergence ↩︎
  8. Michael Lanham ‘When AI Agents Start Thinking for Themselves: The Rise of Emergent Behavior’ Medium 2025. https://medium.com/@Micheal-Lanham/when-ai-agents-start-thinking-for-themselves-the-rise-of-emergent-behavior-582910adfddd ↩︎
  9. Robin Coupland, ‘Deep Cake’ Amazon 2025. https://www.amazon.co.uk/dp/B0FYMZCRPM/ ↩︎
  10. ‘DNA digital data storage’ See https://en.wikipedia.org/wiki/DNA_digital_data_storage ↩︎
  11. Richard Dawkins ‘The Selfish Gene’ Oxford University Press 1976 ↩︎

Wordle: from slow guesses to fast deduction

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Hi Wordlers!

I guess I’m hooked on Wordle. Not so much that I want my day’s head-scratching to last longer than my early morning cup of tea. I’ve a starter that moves from guessing towards deduction. It’s two words: ADIEU followed by STORY whatever.

Wordle 1
Wordle 2
Wordle 3
Wordle 4

It’s much faster. It’s surprising how often the first word I enter at step three is correct. I’m nearly always there in four. (Although I have got stuck in the dead-end alley of FOUND, BOUND, WOUND, HOUND, MOUND and POUND!) This formula works because so many words are eliminated after two steps. There’s less guessing and more deduction. You’ll get a feel for it.

Go well! Be wise! Drink tea! Wordle!

Robin

Urban explosions? Brilliant young scientists are on the case!

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E3E 1

Believe me….. This is beautiful stuff! It may not have great aesthetic appeal. It may not represent your idea of “art.” It may not be what you expect to find on talkingbeautifulstuff.com but this is the output of the creative thinking of some brilliant young scientists. It will, without doubt, prove to be a world-changer. A feel-good story, albeit about awful stuff, and you first read about it here!

This is the prototype of a system whereby explosions and other extreme energy events (e3e) can be detected in real-time and geo-located. The “R” and “L” black dots on the board are microphones that detect the position of any explosion or gunshot within 2-3 km. The sound signals are analysed for the unique “acoustic signature” of the explosion, triangulated with those picked up by other detectors and ultimately fed onto a publicly accessible, web-based map.

E3E 2

The detectors are made from commercially available materials and – with it’s own battery pack – can be fitted into something not much bigger than a brick.

The raison d’etre…… The use of explosives especially in urban areas defined the history of the last century. Will this century be free of the extraordinary human propensity to blow up places where other humans live and work? The daily news is not encouraging. In terms of the nature and extent of the destruction, the reporting of such events still remains in the domain of eye witness accounts and official statements. The e3e detector system would permit monitoring – on an entirely objective basis – of explosions and other extreme energy events in a given context. The watching world would have rapid alert and hard, scientific evidence to compare with verbal reports, claims and counter-claims. Consider this: A town is caught up in conflict. The e3e detectors are in place and ready to transmit data to the website. There is an explosion at a hospital. The system would immediately indicate the time, precise location and the likely weapon. Importantly, it would also indicate whether or not there were shots being fired from the hospital beforehand as is so often claimed.

E3E 3

Here are just some of the scientists who are applying their considerable brain power to the technological aspects of monitoring explosions and other extreme events. (Yes, that is an old London Transport double-decker bus in the background.)

CERN (the European Centre for Nuclear Research) is a huge international scientific collaboration straddling the French-Swiss border. It is best known for its research based on the Large Hadron Collider. Not so well known is that research in other fields at CERN gave us wonderful things such as the world-wide web, touchscreens and the means to capture solar energy. A number of scientists there want to continue this tradition of pursuing technologies that will positively impact human lives in decades to come. Enter THE Port. This month saw THE Port’s second hackathon in CERN’s Idea Square (in which an old London Transport double-decker bus serves as just one of the interesting discussion spaces.) This event brought together young scientists from all over the world for three days of challenge-based innovation. One of the chosen challenges was to build the e3e prototype and demonstrate its real-time link to a website. They did it! Take a look at the final video presentation.

These guys do not lack forward thinking. They imagine a global network of detectors that can be deployed by any interested party. The detectors could even be assembled by anyone anywhere with the right instructions and access to the materials; hence the importance of using commercially available parts. They also foresee automated monitoring of Twitter activity within a certain distance of and triggered by an explosive event. This would serve to verify the event and give more information regarding the evolution of the context especially the human impact. Just digest the implications!

It’s time for the crunch questions: Who would the end-users be? How would e3e Monitor bring about change? Here are the answers from the team:

  • Peace-keeping / enforcing bodies could reliably monitor conflicts and cease-fire agreements.
  • Humanitarian / relief organisations would be able to use the information to assess damage, their own operational security and the needs of affected civilians.
  • The media would have a more accurate and non-biased picture of events relying less on “reports.”
  • An affected population would have access to better information about what is happening and where and so be able to make their own security decisions if, for example, they are moving from one town to another.
  • Airlines would be able to verify areas of active conflict and so change flight schedules, flight paths and assess security of airports.
  • Lawyers would be able to use the information as “evidence“ in the pursuit of prosecuting war crimes.
  • Academic organisations including those orientated around conflict and peace-building could use the information for a wide variety of research projects.
  • With a view to the long term, e3e Monitor technology and networks should become part of the essential infrastructure of any urban environment.

E3E 4The team has even designed a logo. I love the subliminals: scientific detection and monitoring of a spectrum of explosions and extreme energy events together with black box data storage. If explosions in populated areas is your concern, this is a logo you’ll be seeing in the future.