Abstract

Our fascination with AI stems from the perceived uniqueness of human intelligence. We believe it’s what differentiates us. Fears of AI not only concern how it invades our digital lives, but also the implied threat of an intelligence that displaces us from our position at the centre of the world.

Henry Ford’s Faster Horse

Figure: A 1925 Ford Model T built at Henry Ford’s Highland Park Plant in Dearborn, Michigan. This example now resides in Australia, owned by the founder of FordModelT.net. From https://commons.wikimedia.org/wiki/File:1925_Ford_Model_T_touring.jpg

It’s said that Henry Ford’s customers wanted a “a faster horse”. If Henry Ford was selling us artificial intelligence today, what would the customer call for, “a smarter human”? That’s certainly the picture of machine intelligence we find in science fiction narratives, but the reality of what we’ve developed is much more mundane.

Car engines produce prodigious power from petrol. Machine intelligences deliver decisions derived from data. In both cases the scale of consumption enables a speed of operation that is far beyond the capabilities of their natural counterparts. Unfettered energy consumption has consequences in the form of climate change. Does unbridled data consumption also have consequences for us?

If we devolve decision making to machines, we depend on those machines to accommodate our needs. If we don’t understand how those machines operate, we lose control over our destiny. Our mistake has been to see machine intelligence as a reflection of our intelligence. We cannot understand the smarter human without understanding the human. To understand the machine, we need to better understand ourselves.

The Atomic Human

Figure: The Atomic Eye, by slicing away aspects of the human that we used to believe to be unique to us, but are now the preserve of the machine, we learn something about what it means to be human.

The development of what some are calling intelligence in machines, raises questions around what machine intelligence means for our intelligence. The idea of the atomic human is derived from Democritus’s atomism.

In the fifth century bce the Greek philosopher Democritus posed a similar question about our physical universe. He imagined cutting physical matter into pieces in a repeated process: cutting a piece, then taking one of the cut pieces and cutting it again so that each time it becomes smaller and smaller. Democritus believed this process had to stop somewhere, that we would be left with an indivisible piece. The Greek word for indivisible is atom, and so this theory was called atomism. This book considers this question, but in a different domain, asking: As the machine slices away portions of human capabilities, are we left with a kernel of humanity, an indivisible piece that can no longer be divided into parts? Or does the human disappear altogether? If we are left with something, then that uncuttable piece, a form of atomic human, would tell us something about our human spirit.

See Lawrence (2024) atomic human, the p. 13.

Max Planck

Figure: Max Planck 23 April 1858 – 4 October 1947

See Lawrence (2024) Planck, Max p. 130, 274-277, 295-296, 300, 354.

Letter from Max Planck

Figure: Letter from Max Planck 25th October 1944

Figure: Snippet of letter from Max Planck 25th October 1944

My Leader,

I am deeply shocked by the news that my son Erwin has been sentenced to death by the People’s Court. The recognition of my achievements in the service of our fatherland, which you repeatedly expressed in an honourable way, my Leader, entitles me to trust that you will listen to the request of an 87-year-old man.

As a thank you from the German people for my life’s work, which has become an imperishable intellectual property of Germany, I ask for the life of my son.

Max Planck

See Lawrence (2024) Letter to Adolf Hitler from Max Planck p. 130, 274-277, 295-296, 300, 354.

Erwin Planck

Figure: Erwin Planck. 12 March 1893 – 23 January 1945 © Archives of the Max Planck Society

See Lawrence (2024) Planck, Erwin p. 274-275, 276, 301.

Manfred Rommel

Figure: Manfred Rommel, Mayor of Stuttgart

See Lawrence (2024) Rommel, Manfred p. 158, 273–275, 301.

The Diving Bell and the Butterfly

Figure: The Diving Bell and the Buttefly is the autobiography of Jean Dominique Bauby.

The Diving Bell and the Butterfly is the autobiography of Jean Dominique Bauby. Jean Dominique, the editor of French Elle magazine, suffered a major stroke at the age of 43 in 1995. The stroke paralyzed him and rendered him speechless. He was only able to blink his left eyelid, he became a sufferer of locked in syndrome.

See Lawrence (2024) Le Scaphandre et le papillon (The Diving Bell and the Butterfly) p. 10–12.

E S A R I N T U L
O M D P C F B V
H G J Q Z Y X K W

Figure: The ordering of the letters that Bauby used for writing his autobiography.

How could he do that? Well, first, they set up a mechanism where he could scan across letters and blink at the letter he wanted to use. In this way, he was able to write each letter.

It took him 10 months of four hours a day to write the book. Each word took two minutes to write.

Imagine doing all that thinking, but so little speaking, having all those thoughts and so little ability to communicate.

One challenge for the atomic human is that we are all in that situation. While not as extreme as for Bauby, when we compare ourselves to the machine, we all have a locked-in intelligence.

Figure: Jean Dominique Bauby was the Editor in Chief of the French Elle Magazine, he suffered a stroke that destroyed his brainstem, leaving him only capable of moving one eye. Jean Dominique became a victim of locked in syndrome.

Incredibly, Jean Dominique wrote his book after he became locked in. It took him 10 months of four hours a day to write the book. Each word took two minutes to write.

The idea behind embodiment factors is that we are all in that situation. While not as extreme as for Bauby, we all have somewhat of a locked in intelligence.

See Lawrence (2024) Bauby, Jean Dominique p. 9–11, 18, 90, 99-101, 133, 186, 212–218, 234, 240, 251–257, 318, 368–369.

Bauby and Shannon

Figure: Claude Shannon developed information theory which allows us to quantify how much Bauby can communicate. This allows us to compare how locked in he is to us.

See Lawrence (2024) Shannon, Claude p. 10, 30, 61, 74, 98, 126, 134, 140, 143, 149, 260, 264, 269, 277, 315, 358, 363.

Embodiment Factors

bits/min	billions	2000	6
billion calculations/s	~100	a billion	a billion
embodiment	20 minutes	5 billion years	15 trillion years

Figure: Embodiment factors are the ratio between our ability to compute and our ability to communicate. Jean Dominique Bauby suffered from locked-in syndrome. The embodiment factors show that relative to the machine we are also locked in. In the table we represent embodiment as the length of time it would take to communicate one second’s worth of computation. For computers it is a matter of minutes, but for a human, whether locked in or not, it is a matter of many millions of years.

Let me explain what I mean. Claude Shannon introduced a mathematical concept of information for the purposes of understanding telephone exchanges.

Information has many meanings, but mathematically, Shannon defined a bit of information to be the amount of information you get from tossing a coin.

If I toss a coin, and look at it, I know the answer. You don’t. But if I now tell you the answer I communicate to you 1 bit of information. Shannon defined this as the fundamental unit of information.

If I toss the coin twice, and tell you the result of both tosses, I give you two bits of information. Information is additive.

Shannon also estimated the average information associated with the English language. He estimated that the average information in any word is 12 bits, equivalent to twelve coin tosses.

So every two minutes Bauby was able to communicate 12 bits, or six bits per minute.

This is the information transfer rate he was limited to, the rate at which he could communicate.

Compare this to me, talking now. The average speaker for TEDX speaks around 160 words per minute. That’s 320 times faster than Bauby or around a 2000 bits per minute. 2000 coin tosses per minute.

But, just think how much thought Bauby was putting into every sentence. Imagine how carefully chosen each of his words was. Because he was communication constrained he could put more thought into each of his words. Into thinking about his audience.

So, his intelligence became locked in. He thinks as fast as any of us, but can communicate slower. Like the tree falling in the woods with no one there to hear it, his intelligence is embedded inside him.

Two thousand coin tosses per minute sounds pretty impressive, but this talk is not just about us, it’s about our computers, and the type of intelligence we are creating within them.

So how does two thousand compare to our digital companions? When computers talk to each other, they do so with billions of coin tosses per minute.

Let’s imagine for a moment, that instead of talking about communication of information, we are actually talking about money. Bauby would have 6 dollars. I would have 2000 dollars, and my computer has billions of dollars.

The internet has interconnected computers and equipped them with extremely high transfer rates.

However, by our very best estimates, computers actually think slower than us.

How can that be? You might ask, computers calculate much faster than me. That’s true, but underlying your conscious thoughts there are a lot of calculations going on.

Each thought involves many thousands, millions or billions of calculations. How many exactly, we don’t know yet, because we don’t know how the brain turns calculations into thoughts.

Our best estimates suggest that to simulate your brain a computer would have to be as large as the UK Met Office machine here in Exeter. That’s a 250 million pound machine, the fastest in the UK. It can do 16 billion billon calculations per second.

It simulates the weather across the word every day, that’s how much power we think we need to simulate our brains.

So, in terms of our computational power we are extraordinary, but in terms of our ability to explain ourselves, just like Bauby, we are locked in.

For a typical computer, to communicate everything it computes in one second, it would only take it a couple of minutes. For us to do the same would take 15 billion years.

If intelligence is fundamentally about processing and sharing of information. This gives us a fundamental constraint on human intelligence that dictates its nature.

I call this ratio between the time it takes to compute something, and the time it takes to say it, the embodiment factor (Lawrence, 2017). Because it reflects how embodied our cognition is.

If it takes you two minutes to say the thing you have thought in a second, then you are a computer. If it takes you 15 billion years, then you are a human.

Bandwidth Constrained Conversations

Figure: Conversation relies on internal models of other individuals.

Figure: Misunderstanding of context and who we are talking to leads to arguments.

Embodiment factors imply that, in our communication between humans, what is not said is, perhaps, more important than what is said. To communicate with each other we need to have a model of who each of us are.

To aid this, in society, we are required to perform roles. Whether as a parent, a teacher, an employee or a boss. Each of these roles requires that we conform to certain standards of behaviour to facilitate communication between ourselves.

Control of self is vitally important to these communications.

The high availability of data available to humans undermines human-to-human communication channels by providing new routes to undermining our control of self.

The consequences between this mismatch of power and delivery are to be seen all around us. Because, just as driving an F1 car with bicycle wheels would be a fine art, so is the process of communication between humans.

If I have a thought and I wish to communicate it, I first need to have a model of what you think. I should think before I speak. When I speak, you may react. You have a model of who I am and what I was trying to say, and why I chose to say what I said. Now we begin this dance, where we are each trying to better understand each other and what we are saying. When it works, it is beautiful, but when mis-deployed, just like a badly driven F1 car, there is a horrible crash, an argument.

Computer Conversations

Figure: Conversation relies on internal models of other individuals.

Figure: Misunderstanding of context and who we are talking to leads to arguments.

Similarly, we find it difficult to comprehend how computers are making decisions. Because they do so with more data than we can possibly imagine.

In many respects, this is not a problem, it’s a good thing. Computers and us are good at different things. But when we interact with a computer, when it acts in a different way to us, we need to remember why.

Just as the first step to getting along with other humans is understanding other humans, so it needs to be with getting along with our computers.

Embodiment factors explain why, at the same time, computers are so impressive in simulating our weather, but so poor at predicting our moods. Our complexity is greater than that of our weather, and each of us is tuned to read and respond to one another.

Their intelligence is different. It is based on very large quantities of data that we cannot absorb. Our computers don’t have a complex internal model of who we are. They don’t understand the human condition. They are not tuned to respond to us as we are to each other.

Embodiment factors encapsulate a profound thing about the nature of humans. Our locked in intelligence means that we are striving to communicate, so we put a lot of thought into what we’re communicating with. And if we’re communicating with something complex, we naturally anthropomorphize them.

We give our dogs, our cats, and our cars human motivations. We do the same with our computers. We anthropomorphize them. We assume that they have the same objectives as us and the same constraints. They don’t.

This means, that when we worry about artificial intelligence, we worry about the wrong things. We fear computers that behave like more powerful versions of ourselves that will struggle to outcompete us.

In reality, the challenge is that our computers cannot be human enough. They cannot understand us with the depth we understand one another. They drop below our cognitive radar and operate outside our mental models.

The real danger is that computers don’t anthropomorphize. They’ll make decisions in isolation from us without our supervision because they can’t communicate truly and deeply with us.

New Flow of Information

Classically the field of statistics focused on mediating the relationship between the machine and the human. Our limited bandwidth of communication means we tend to over-interpret the limited information that we are given, in the extreme we assign motives and desires to inanimate objects (a process known as anthropomorphizing). Much of mathematical statistics was developed to help temper this tendency and understand when we are valid in drawing conclusions from data.

Figure: The trinity of human, data, and computer, and highlights the modern phenomenon. The communication channel between computer and data now has an extremely high bandwidth. The channel between human and computer and the channel between data and human is narrow. New direction of information flow, information is reaching us mediated by the computer. The focus on classical statistics reflected the importance of the direct communication between human and data. The modern challenges of data science emerge when that relationship is being mediated by the machine.

Data science brings new challenges. In particular, there is a very large bandwidth connection between the machine and data. This means that our relationship with data is now commonly being mediated by the machine. Whether this is in the acquisition of new data, which now happens by happenstance rather than with purpose, or the interpretation of that data where we are increasingly relying on machines to summarize what the data contains. This is leading to the emerging field of data science, which must not only deal with the same challenges that mathematical statistics faced in tempering our tendency to over interpret data but must also deal with the possibility that the machine has either inadvertently or maliciously misrepresented the underlying data.

Revolution

Arguably the information revolution we are experiencing is unprecedented in history. But changes in the way we share information have a long history. Over 5,000 years ago in the city of Uruk, on the banks of the Euphrates, communities which relied on the water to irrigate their corps developed an approach to recording transactions in clay. Eventually the system of recording system became sophisticated enough that their oral histories could be recorded in the form of the first epic: Gilgamesh.

See Lawrence (2024) cuneiform p. 337, 360, 390.

Figure: Chicago Stone, side 2, recording sale of a number of fields, probably from Isin, Early Dynastic Period, c. 2600 BC, black basalt

It was initially developed for people as a record of who owed what to whom, expanding individuals’ capacity to remember. But over a five hundred year period writing evolved to become a tool for literature as well. More pithily put, writing was invented by accountants not poets (see e.g. this piece by Tim Harford).

In some respects today’s revolution is different, because it involves also the creation of stories as well as their curation. But in some fundamental ways we can see what we have produced as another tool for us in the information revolution.

The Structure of Scientific Revolutions

Figure: The Structure of Scientific Revolutions by Thomas S. Kuhn suggests scientific paradigms are recorded in books.

Kuhn was a historian of science and a philosopher who suggested that the sociology of science has two principal components to it. His idea is that “normal science” operates within a paradigm That paradigm is defined by books which encode our best understanding. An example of a paradigm is Newtonian mechanics, or another example would be the geocentric view of the Universe. Within a paradigm normal science proceeds by scientists solving the “puzzles” that paradigm sets. A paradigm shift is when the paradigm changes, for example the Corpernican revolution or the introduction of relativity.

The notion of a paradigm shift has also entered common parlance, this reflects the idea that wider human knowledge is also shared and stored, less ormally than scientific knowledge, but still with a dependence on our information infrastructure.

The digital computer has brought a fundamental change in the nature of that information infrastructure. By moving information faster the modern information infrastructure is dominated not by the book, but by the machine. This brings challenges for managing and controlling this information infrastructure.

See Lawrence (2024) Kuhn, Thomas: The Structure of Scientific Revolutions p. 295–299.

Blake’s Newton

William Blake’s rendering of Newton captures humans in a particular state. He is trance-like absorbed in simple geometric shapes. The feel of dreams is enhanced by the underwater location, and the nature of the focus is enhanced because he ignores the complexity of the sea life around him.

Figure: William Blake’s Newton. 1795c-1805

See Lawrence (2024) Blake, William Newton p. 121–123.

The caption in the Tate Britain reads:

Here, Blake satirises the 17th-century mathematician Isaac Newton. Portrayed as a muscular youth, Newton seems to be underwater, sitting on a rock covered with colourful coral and lichen. He crouches over a diagram, measuring it with a compass. Blake believed that Newton’s scientific approach to the world was too reductive. Here he implies Newton is so fixated on his calculations that he is blind to the world around him. This is one of only 12 large colour prints Blake made. He seems to have used an experimental hybrid of printing, drawing, and painting.

From the Tate Britain

See Lawrence (2024) Blake, William Newton p. 121–123, 258, 260, 283, 284, 301, 306.

Lunette Rehoboam Abijah

Many of Blake’s works are inspired by engravings he saw of the Sistine chapel ceiling. The pose of Newton is taken from the Lunette depiction of Abijah, one of the Michelangeo’s ancestors of Christ.

Figure: Lunette containing Rehoboam and Abijah.

People, Communication and Culture

Human intelligence is a social intelligence: it is isolated, but it doesn’t exist in isolation. It sits instead within a broader culture. The term ‘culture’ originates from the Roman orator Cicero, who wrote of cultura animi, the cultivation of our minds. He drew an analogy with agriculture: human knowledge is cultivated similarly to the way crops are raised from the land: our minds grow and respond to their intellectual environment.

It is impossible to understand humans without understanding our context, and human culture is a key part of that context, but the cul- ture that sustains our minds is also evolving. Over time, how we see ourselves within the universe has changed. The world looks different to us today than it did to Michelangelo. Many of these changes have improved our ability to understand the world around us.

Figure: People communicate through artifacts and culture.

See Lawrence (2024) Cicero and culture p. 20-21.

The Sorcerer’s Apprentice

See this blog post on The Open Society and its AI.

Figure: A young sorcerer learns his masters spells, and deploys them to perform his chores, but can’t control the result.

In Goethe’s poem The Sorcerer’s Apprentice, a young sorcerer learns one of their master’s spells and deploys it to assist in his chores. Unfortunately, he cannot control it. The poem was popularised by Paul Dukas’s musical composition, in 1940 Disney used the composition in the film Fantasia. Mickey Mouse plays the role of the hapless apprentice who deploys the spell but cannot control the results.

When it comes to our software systems, the same thing is happening. The Harvard Law professor, Jonathan Zittrain calls the phenomenon intellectual debt. In intellectual debt, like the sorcerer’s apprentice, a software system is created but it cannot be explained or controlled by its creator. The phenomenon comes from the difficulty of building and maintaining large software systems: the complexity of the whole is too much for any individual to understand, so it is decomposed into parts. Each part is constructed by a smaller team. The approach is known as separation of concerns, but it has the unfortunate side effect that no individual understands how the whole system works. When this goes wrong, the effects can be devastating. We saw this in the recent Horizon scandal, where neither the Post Office or Fujitsu were able to control the accounting system they had deployed, and we saw it when Facebook’s systems were manipulated to spread misinformation in the 2016 US election.

In 2019 Mark Zuckerberg wrote an op-ed in the Washington Post calling for regulation of social media. He was repeating the realisation of Goethe’s apprentice, he had released a technology he couldn’t control. In Goethe’s poem, the master returns, “Besen, besen! Seid’s gewesen” he calls, and order is restored, but back in the real world the role of the master is played by Popper’s open society. Unfortunately, those institutions have been undermined by the very spell that these modern apprentices have cast. The book, the letter, the ledger, each of these has been supplanted in our modern information infrastructure by the computer. The modern scribes are software engineers, and their guilds are the big tech companies. Facebook’s motto was to “move fast and break things”. Their software engineers have done precisely that and the apprentice has robbed the master of his powers. This is a desperate situation, and it’s getting worse. The latest to reprise the apprentice’s role are Sam Altman and OpenAI who dream of “general intelligence” solutions to societal problems which OpenAI will develop, deploy, and control. Popper worried about the threat of totalitarianism to our open societies, today’s threat is a form of information totalitarianism which emerges from the way these companies undermine our institutions.

So, what to do? If we value the open society, we must expose these modern apprentices to scrutiny. Open development processes are critical here, Fujitsu would never have got away with their claims of system robustness for Horizon if the software they were using was open source. We also need to re-empower the professions, equipping them with the resources they need to have a critical understanding of these technologies. That involves redesigning the interface between these systems and the humans that empowers civil administrators to query how they are functioning. This is a mammoth task. But recent technological developments, such as code generation from large language models, offer a route to delivery.

The open society is characterised by institutions that collaborate with each otherin the pragmatic pursuit of solutions to social problems. The large tech companies that have thrived because of the open society are now putting that ecosystem in peril. For the open society to survive it needs to embrace open development practices that enable Popper’s piecemeal social engineers to come back together and chant “Besen, besen! Seid’s gewesen.” Before it is too late for the master to step in and deal with the mess the apprentice has made.

See Lawrence (2024) sorcerer’s apprentice p. 371-374.

The Open Society and its Enemies

Figure: The Open Society and Its Enemies by Karl Popper views liberal democracies as a collection of “piecemeal social engineers” who strive towards better outcomes.

Popper opened the preface to his book (Popper, 1945) with the following words:

If in this book harsh words are spoken about some of the greatest among the intellectual leaders of mankind, my motive is not, I hope, to belittle them. It springs rather from my conviction that, if our civilization is to survive, we must break with the habit of deference to great men. Great men may make great mistakes; and as the book tries to show, some of the greatest leaders of the past supported the perennial attack on freedom and reason.

He had written the book against the background of the second world war, his decision to write it taken on the day the Nazis invaded Austria in March 1938. His book is a reaction to totalitarianism.

For Popper, the ideas of “great men” become totalitarian when imposed on society. He advocates for direct liberal democracy as the only form of government that can allow for institutional change without bloodshed. The open society is one characterized by institutions and individuals that can engage in the practical pursuit of solutions to social and political problems. The institutions are also underpinned by individuals: lawyers, accountants, civil administrators and many more. To Popper it is these “piecemeal social engineers” who offer pragmatic solutions to our society’s political and social challenges.

See Lawrence (2024) Popper, Karl The Open Society and its Enemies p. 371–374.

The MONIAC

The MONIAC was an analogue computer designed to simulate the UK economy. Analogue comptuers work through analogy, the analogy in the MONIAC is that both money and water flow. The MONIAC exploits this through a system of tanks, pipes, valves and floats that represent the flow of money through the UK economy. Water flowed from the treasury tank at the top of the model to other tanks representing government spending, such as health and education. The machine was initially designed for teaching support but was also found to be a useful economic simulator. Several were built and today you can see the original at Leeds Business School, there is also one in the London Science Museum and one in the Unisversity of Cambridge’s economics faculty.

Figure: Bill Phillips and his MONIAC (completed in 1949). The machine is an analogue computer designed to simulate the workings of the UK economy.

See Lawrence (2024) MONIAC p. 232-233, 266, 343.

Human Analogue Machine

Recent breakthroughs in generative models, particularly large language models, have enabled machines that, for the first time, can converse plausibly with other humans.

The Apollo guidance computer provided Armstrong with an analogy when he landed it on the Moon. He controlled it through a stick which provided him with an analogy. The analogy is based in the experience that Amelia Earhart had when she flew her plane. Armstrong’s control exploited his experience as a test pilot flying planes that had control columns which were directly connected to their control surfaces.

Figure: The human analogue machine is the new interface that large language models have enabled the human to present. It has the capabilities of the computer in terms of communication, but it appears to present a “human face” to the user in terms of its ability to communicate on our terms. (Image quite obviously not drawn by generative AI!)

The generative systems we have produced do not provide us with the “AI” of science fiction. Because their intelligence is based on emulating human knowledge. Through being forced to reproduce our literature and our art they have developed aspects which are analogous to the cultural proxy truths we use to describe our world.

These machines are to humans what the MONIAC was the British economy. Not a replacement, but an analogue computer that captures some aspects of humanity while providing advantages of high bandwidth of the machine.

HAM

The Human-Analogue Machine or HAM therefore provides a route through which we could better understand our world through improving the way we interact with machines.

Figure: The trinity of human, data, and computer, and highlights the modern phenomenon. The communication channel between computer and data now has an extremely high bandwidth. The channel between human and computer and the channel between data and human is narrow. New direction of information flow, information is reaching us mediated by the computer. The focus on classical statistics reflected the importance of the direct communication between human and data. The modern challenges of data science emerge when that relationship is being mediated by the machine.

The HAM can provide an interface between the digital computer and the human allowing humans to work closely with computers regardless of their understandin gf the more technical parts of software engineering.

Figure: The HAM now sits between us and the traditional digital computer.

Of course this route provides new routes for manipulation, new ways in which the machine can undermine our autonomy or exploit our cognitive foibles. The major challenge we face is steering between these worlds where we gain the advantage of the computer’s bandwidth without undermining our culture and individual autonomy.

See Lawrence (2024) human-analogue machine (HAMs) p. 343-347, 359-359, 365-368.

Thanks!

For more information on these subjects and more you might want to check the following resources.

• book: The Atomic Human
• twitter: @lawrennd
• podcast: The Talking Machines
• newspaper: Guardian Profile Page
• blog: http://inverseprobability.com

References