Abstract

Professor Lawrence will explore what artificial intelligence means for human society, drawing from over 25 years of research and real-world deployment experience at Amazon. He will explain current AI technologies—from machine learning to large language models—and their transformative potential across science, healthcare, and industry. Central to his discussion will be the question from his book The Atomic Human: what makes human intelligence unique in an age of sophisticated machines? He will address both the enormous benefits AI can deliver and the critical risks that must be managed, including data governance, algorithmic accountability, and the concentration of digital power. Professor Lawrence will discuss how we can ensure AI serves humanity rather than displacing human agency, and what steps policymakers and citizens can take to navigate our AI-driven future while preserving democratic values and human autonomy.

What is Artificial Intelligence?

What is Intelligence?

One challenge with the word intelligence is it means many different things to different people. My own definition of intelligence is the use of information to achieve goals more efficiently. Where information is measured in Shannon terms, and efficiency is measured in terms of use of resource, typically available energy or maybe time. The definition appeals to me because it brings about a connection between the major historic revolutions, which have been about energy, and the current changes which focus more on information. However, the definition does not specify the goal. Some prefer to try and incorporate the goal in their definition of intelligence, and that goals should be somehow emergent from intelligence. For complex tasks sub-goals are certainly emergent, but I don’t seek to incorporate a global goal in my own definition.

So where are we in terms of intelligence? There is certainly an information revolution going on, and it is causing disruption across many industries. But what we are saying about machine learning and artificial inteligence has been said before in relation to the invention of sillicon chips. I gave a version of this talk once at the BBC, and Bill Thompson was kind enough to share with me the archive of “Silicon Factor”, a series of three programs broadcast in 1980 exploring the “microelectronics revolution”. There is a remarkable similarity to everything the experts say in these programs and what experts say about AI today. And of course, these experts were right, jobs have changed, industry is different and we are still deep in the micro-electronics revolution. However, these changes do not happen overnight, those broadcasts were made nearly forty years ago. The “Fourth Industrial Revolution” is merely a continuation of an ongoing revolution in information, one which was triggered by the Silicon Factor, continued by the internet and has been given further momentum by mobile communications.

With a word like intelligence, we can’t just think about our own definition of intelligence, but we should also take into account public understanding of the word. For many people intelligence is something specific to humans, and as a result what the term refers to evolves. Artificial intelligence is a very emotive term, because it feels close to us, it makes us think that computers are doing things like us. As a result it is also a shifting definition, it comes to me “intelligence is the stuff I can do that computers can’t”. This lends a narcissistic element to our fascination with artificial intelligence, because it is also a fascination with ourselves.

A hundred years ago computers were human beings, often female, who conducted repetitive mathematical tasks for the creation of mathematical tables such as logarithms. Our modern digital computers were originally called automatic computers to reflect the fact that the intelligence of these human operators had been automated. But despite the efficiency with which they perform these tasks, very few think of their mobile phones or computers as intelligent.

Cybernetics and the Ratio Club

Norbert Wiener launched last century’s first wave of interest in emulation of intelligence with his book “Cybernetics”. The great modern success that stemmed from that work is the modern engineering discipline of Automatic Control. The technology that allows fighter jets to fly. These ideas came out of the Second World War, when researchers explored the use of radar (automated sensing) and automatic computation for decryption of military codes (automated decision making). Post war a body of researchers, including Alan Turing, were seeing the potential for electronic emulation of what had up until then been the preserve of an animallian nervous system.

Figure: Centrifugal governor as held by “Science” on Holborn Viaduct

Artificial intelligence is a badly defined term. Successful deployments of intelligent systems are common, but normally they are redefined to be non-intelligent. My favourite example is the Centrifugal governor. Applied to the Steam Engine by Boulton and Watt and immortalised in the arms of the statue of “Science” on the Holborn viaduct in London, the centrifugal governor automatically regulated the speed of a steam engine, closing the inlet valve progressively as the engine ran faster. It did the job that an intelligent operator used to have to do, but few today would describe it as “artificial intelligence”.

The current revolution in AI is being driven by machine learning. Machine learning is an approach to prediction which is data driven. It is not the first approach to focus on data, the statistical sciences have combined models with data for a number of years. But machine learning has taken a particular focus on improving the quality of prediction, whereas statistical sciences have traditionally focussed more on explaination. Machine learning is giving us information processing engines that are equivalent to the steam engines of the industrial revolution.

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 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.

The Atomic Human considers the same 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.

The Transformative Potential of AI

AI Nobel Prizes

In 2024, artificial intelligence researchers were recognized with Nobel Prizes in both Physics and Chemistry.

The Physics prize was awarded jointly to John J. Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks”.

The Chemistry prize was shared between David Baker, Demis Hassabis, and John Jumper “for computational protein design and protein structure prediction”.

These awards mark a pivotal moment in the history of artificial intelligence, recognizing both the theoretical foundations laid in the 1980s and the recent breakthroughs that have transformed the technology into a powerful scientific tool. The prizes highlight how AI has evolved from an academic pursuit into a transformative force across multiple scientific disciplines.

The recognition of British-educated researchers Hinton and Hassabis also underscores the UK’s significant contributions to AI development, reflecting a legacy of pioneering work in computer science and artificial intelligence stretching back to Alan Turing.

Rapid Diagnosis and Consulting

The Atomic Human: What Makes Human Intelligence Unique?

Figure: This is the drawing Dan was inspired to create for Chapter 1. It captures the fundamentally narcissistic nature of our (societal) obsession with our intelligence.

See blog post on Dan Andrews image of our reflective obsession with AI.. See also (Vallor, 2024).

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.

Real-World AI Deployment: Lessons from Amazon

Prime Air

One project where the components of machine learning and the physical world come together is Amazon’s Prime Air drone delivery system.

Automating the process of moving physical goods through autonomous vehicles completes the loop between the ‘bits’ and the ‘atoms’. In other words, the information and the ‘stuff’. The idea of the drone is to complete a component of package delivery, the notion of last mile movement of goods, but in a fully autonomous way.

Figure: An actual ‘Santa’s sleigh’. Amazon’s prototype delivery drone. Machine learning algorithms are used across various systems including sensing (computer vision for detection of wires, people, dogs etc) and piloting. The technology is necessarily a combination of old and new ideas. The transition from vertical to horizontal flight is vital for efficiency and uses sophisticated machine learning to achieve.

As Jeff Wilke (who was CEO of Amazon Retail at the time) announced in June 2019 the technology is ready, but still needs operationalization including e.g. regulatory approval.

Figure: Jeff Wilke (CEO Amazon Consumer) announcing the new drone at the Amazon 2019 re:MARS event alongside the scale of the Amazon supply chain.

When we announced earlier this year that we were evolving our Prime two-day shipping offer in the U.S. to a one-day program, the response was terrific. But we know customers are always looking for something better, more convenient, and there may be times when one-day delivery may not be the right choice. Can we deliver packages to customers even faster? We think the answer is yes, and one way we’re pursuing that goal is by pioneering autonomous drone technology.

Today at Amazon’s re:MARS Conference (Machine Learning, Automation, Robotics and Space) in Las Vegas, we unveiled our latest Prime Air drone design. We’ve been hard at work building fully electric drones that can fly up to 15 miles and deliver packages under five pounds to customers in less than 30 minutes. And, with the help of our world-class fulfillment and delivery network, we expect to scale Prime Air both quickly and efficiently, delivering packages via drone to customers within months.

The 15 miles in less than 30 minutes implies air speed velocities of around 50 kilometers per hour.

Our newest drone design includes advances in efficiency, stability and, most importantly, in safety. It is also unique, and it advances the state of the art. How so? First, it’s a hybrid design. It can do vertical takeoffs and landings – like a helicopter. And it’s efficient and aerodynamic—like an airplane. It also easily transitions between these two modes—from vertical-mode to airplane mode, and back to vertical mode.

It’s fully shrouded for safety. The shrouds are also the wings, which makes it efficient in flight.

Figure: Picture of the drone from Amazon Re-MARS event in 2019.

Our drones need to be able to identify static and moving objects coming from any direction. We employ diverse sensors and advanced algorithms, such as multi-view stereo vision, to detect static objects like a chimney. To detect moving objects, like a paraglider or helicopter, we use proprietary computer-vision and machine learning algorithms.

A customer’s yard may have clotheslines, telephone wires, or electrical wires. Wire detection is one of the hardest challenges for low-altitude flights. Through the use of computer-vision techniques we’ve invented, our drones can recognize and avoid wires as they descend into, and ascend out of, a customer’s yard.

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.

The Risks and Challenges

2016 US Elections

In the US the 2016 elections saw manipulation through social media and the Russian troll farm, the Internet Research Agency.

Figure: The General counsels from Facebook, Twitter, and Google testified at a hearing on social media and Russian influence in the 2016 elections..

See Lawrence (2024) Cambridge Analytica p. 371.

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.

Ensuring AI Serves Humanity

Figure: The relationships between trust, autonomy and embodiment are key to understanding how to properly deploy AI systems in a way that avoids digital autocracy. (Illustration by Dan Andrews inspired by Chapter 3 “Intent” of “The Atomic Human” Lawrence (2024))

This illustration was created by Dan Andrews after reading Chapter 3 “Intent” of “The Atomic Human” book. The chapter explores the concept of intent in AI systems and how trust, autonomy, and embodiment interact to shape our relationship with technology. Dan’s drawing captures these complex relationships and the balance needed for responsible AI deployment.

See blog post on Dan Andrews image from Chapter 3.

See blog post on Dan Andrews image from Chapter 3..

Figure: This is the drawing Dan was inspired to create for Chapter 4. It highlights how even if these machines can generate creative works the lack of origin in humans menas it is not the same as works of art that come to us through history.

See blog post on Art is Human..

For the Working Group for the Royal Society report on Machine Learning, back in 2016, the group worked with Ipsos MORI to engage in public dialogue around the technology. Ever since I’ve been struck about how much more sensible the conversations that emerge from public dialogue are than the breathless drivel that seems to emerge from supposedly more informed individuals.

There were a number of messages that emerged from those dialogues, and many of those messages were reinforced in two further public dialogues we organised in September.

However, there was one area we asked about in 2017, but we didn’t ask about in 2024. That was an area where the public unequivocal that they didn’t want the research community to pursue progress. Quoting from the report (my emphasis).

Art: Participants failed to see the purpose of machine learning-written poetry. For all the other case studies, participants recognised that a machine might be able to do a better job than a human. However, they did not think this would be the case when creating art, as doing so was considered to be a fundamentally human activity that machines could only mimic at best.

Public Views of Machine Learning, April, 2017

How right they were.

Policy Challenges and Societal Changes

The Shifting Nature of Economic Value

The evolution of economic value creation has gone through several distinct phases that help us understand where AI might take us:

1. Manufacturing Era (Post-WW2)
   • Power with manufacturers
   • Supply-driven economy
   • “If you manufactured something you could sell it to anyone”
2. Distribution Era (1970s-2000s)
   • Power shift to distributors
   • Supply chain optimization
   • Value in controlling distribution networks
3. Demand Control Era (Current)
   • Power in influencing demand
   • AI-driven preference shaping
   • Value in data and attention

AI’s Role in Value Creation

The current shift is characterized by:

1. Information Flow
   • Rapid, interconnected data movement
   • AI as decision support
   • Algorithmic market making
2. Value Capture
   • Advertising and attention markets
   • Behavioral prediction
   • Preference manipulation
3. Worker Impact
   • From labor rights to data rights
   • Need for new protections
   • Collective data bargaining

Investment Implications

This evolution suggests several key investment considerations:

1. Platform Economics
   • Value in demand prediction
   • Data network effects
   • Market-making capabilities
2. Worker Relations
   • Data rights importance
   • New collective bargaining
   • Privacy regulations
3. Market Structure
   • Platform concentration
   • Regulatory response
   • New intermediaries

The key insight is that AI isn’t just automating existing processes - it’s fundamentally shifting where and how economic value is created and captured.

The Path Forward

Figure: Society faces many wicked problems in health, education, security, and social care that require carefully deploying AI toward meaningful societal challenges rather than focusing on commercially appealing applications. (Illustration by Dan Andrews inspired by the Epilogue of “The Atomic Human” Lawrence (2024))

This illustration was created by Dan Andrews after reading the Epilogue of “The Atomic Human” book. The Epilogue discusses how we might deploy AI to address society’s most pressing challenges, and Dan’s drawing captures the various wicked problems we face and some of the initiatives that are looking to address them.

See blog post on Who is Stepping Up?.

See blog post on Who is Stepping Up?.

Figure: This is the drawing Dan was inspired to create for Chapter 12. It captures the challenge the analogy where the speed of information assimilation associated with machines is related to the speed assimilation associated with humans.

See blog post on the launch of Facebook’s AI lab..

Conclusion: Our AI-Driven Future

Thanks!

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

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

References