Week : The AI Moment: Decisions, Information, and the Atomic Human
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Abstract:
This session reframes AI for leaders: the “AI moment” as a shift in information flow and scaled decision-making, grounded in The Atomic Human and updated for the post‑2024 landscape (LLMs, agents, and new failure modes).
The AI moment
This Scribeysense image is Narcissus staring into his reflection (after Caravaggio’s Narcissus, housed in the Galleria Nazionale d’Arte Antica in Rome). It’s a deliberately “mythic” framing: gods and robots are how we tempt ourselves to think about AI, but we also centre the intelligence in our context. It is a distorted reflection of us that fascinates us, whether or not that’s how the technology operates. For leaders, that framing is usually a trap. The real risk is not a human-like entity that outsmarts us, but the scaling of systems that make (and automate) decisions, shaped by incentives and feedback loops. Keep the conversation anchored on decisions, information, failure modes, and accountability.
Figure: This is the drawing Dan was inspired to create for Chapter 1: Narcissus staring into his reflection (after Caravaggio’s Narcissus). It captures the fundamentally narcissistic nature of our (societal) obsession with our intelligence.
See Lawrence (2024) Terminator image embodies p. 7, 9, 12, 13, 21, 30, 31, 216, 220, 257, 333, 353. See Lawrence (2024) Terminator (movie character) p. 7, 9, 12, 13, 21, 30, 31, 216, 220, 257, 333, 353. See Lawrence (2024) anthropomorphization (‘anthrox’) p. 30-31, 90-91, 93-4, 100, 132, 148, 153, 163, 216-17, 239, 276, 326, 342.
The new flow of information
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.
See Lawrence (2024) topography, information p. 34-9, 43-8, 57, 62, 104, 115-16, 127, 140, 192, 196, 199, 291, 334, 354-5. See Lawrence (2024) anthropomorphization (‘anthrox’) p. 30-31, 90-91, 93-4, 100, 132, 148, 153, 163, 216-17, 239, 276, 326, 342.
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.
See Lawrence (2024) ignorance: HAMs p. 347. See Lawrence (2024) test pilot p. 163-8, 189, 190, 192-3, 196, 197, 200, 211, 245.
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.
Evolved Relationship with Information
The high bandwidth of computers has resulted in a close relationship between the computer and data. Large amounts of information can flow between the two. The degree to which the computer is mediating our relationship with data means that we should consider it an intermediary.
Originally our low bandwidth relationship with data was affected by two characteristics. Firstly, our tendency to over-interpret driven by our need to extract as much knowledge from our low bandwidth information channel as possible. Secondly, by our improved understanding of the domain of mathematical statistics and how our cognitive biases can mislead us.
With this new set up there is a potential for assimilating far more information via the computer, but the computer can present this to us in various ways. If its motives are not aligned with ours then it can misrepresent the information. This needn’t be nefarious it can be simply because of the computer pursuing a different objective from us. For example, if the computer is aiming to maximize our interaction time that may be a different objective from ours which may be to summarize information in a representative manner in the shortest possible length of time.
For example, for me, it was a common experience to pick up my telephone with the intention of checking when my next appointment was, but to soon find myself distracted by another application on the phone and end up reading something on the internet. By the time I’d finished reading, I would often have forgotten the reason I picked up my phone in the first place.
There are great benefits to be had from the huge amount of information we can unlock from this evolved relationship between us and data. In biology, large scale data sharing has been driven by a revolution in genomic, transcriptomic and epigenomic measurement. The improved inferences that can be drawn through summarizing data by computer have fundamentally changed the nature of biological science, now this phenomenon is also influencing us in our daily lives as data measured by happenstance is increasingly used to characterize us.
Better mediation of this flow requires a better understanding of human-computer interaction. This in turn involves understanding our own intelligence better, what its cognitive biases are and how these might mislead us.
For further thoughts see Guardian article on marketing in the internet era from 2015.
You can also check my blog post on System Zero. This was also written in 2015.
See Lawrence (2024) System Zero p. 242-7, 306, 309, 329, 350, 355, 359, 361, 363, 364.
Embodiment Factors
| bits/min | billions | 2,000 |
|
billion calculations/s |
~100 | a billion |
| embodiment | 20 minutes | 5 billion years |
Figure: Embodiment factors are the ratio between our ability to compute and our ability to communicate. 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, it is a matter of thousands of millions of years. See also “Living Together: Mind and Machine Intelligence” Lawrence (2017)
There is a fundamental limit placed on our intelligence based on our ability to communicate. Claude Shannon founded the field of information theory. The clever part of this theory is it allows us to separate our measurement of information from what the information pertains to.1
Shannon measured information in bits. One bit of information is the amount of information I pass to you when I give you the result of a coin toss. Shannon was also interested in the amount of information in the English language. He estimated that on average a word in the English language contains 12 bits of information.
Given typical speaking rates, that gives us an estimate of our ability to communicate of around 100 bits per second (Reed and Durlach, 1998). Computers on the other hand can communicate much more rapidly. Current wired network speeds are around a billion bits per second, ten million times faster.
When it comes to compute though, our best estimates indicate our computers are slower. A typical modern computer can process make around 100 billion floating-point operations per second, each floating-point operation involves a 64 bit number. So the computer is processing around 6,400 billion bits per second.
It’s difficult to get similar estimates for humans, but by some estimates the amount of compute we would require to simulate a human brain is equivalent to that in the UK’s fastest computer (Ananthanarayanan et al., 2009), the MET office machine in Exeter, which in 2018 ranked as the 11th fastest computer in the world. That machine simulates the world’s weather each morning, and then simulates the world’s climate in the afternoon. It is a 16-petaflop machine, processing around 1,000 trillion bits per second.
See Lawrence (2024) embodiment factor p. 13, 29, 35, 79, 87, 105, 197, 216-217, 249, 269, 353, 369.
Figure: The Lotus 49, view from the rear. The Lotus 49 was one of the last Formula One cars before the introduction of aerodynamic aids.
So, when it comes to our ability to compute we are extraordinary, not compute in our conscious mind, but the underlying neuron firings that underpin both our consciousness, our subconsciousness as well as our motor control etc.
If we think of ourselves as vehicles, then we are massively overpowered. Our ability to generate derived information from raw fuel is extraordinary. Intellectually we have formula one engines.
But in terms of our ability to deploy that computation in actual use, to share the results of what we have inferred, we are very limited. So, when you imagine the F1 car that represents a psyche, think of an F1 car with bicycle wheels.
Figure: Marcel Renault races a Renault 40 cv during the Paris-Madrid race, an early Grand Prix, in 1903. Marcel died later in the race after missing a warning flag for a sharp corner at Couhé Vérac, likely due to dust reducing visibility.
Just think of the control a driver would have to have to deploy such power through such a narrow channel of traction. That is the beauty and the skill of the human mind.
In contrast, our computers are more like go-karts. Underpowered, but with well-matched tires. They can communicate far more fluidly. They are more efficient, but somehow less extraordinary, less beautiful.
Figure: Caleb McDuff driving for WIX Silence Racing.
For humans, that means much of our computation should be dedicated to considering what we should compute. To do that efficiently we need to model the world around us. The most complex thing in the world around us is other humans. So, it is no surprise that we model them. We second guess what their intentions are, and our communication is only necessary when they are departing from how we model them. Naturally, for this to work well, we need to understand those we work closely with. It is no surprise that social communication, social bonding, forms so much of a part of our use of our limited bandwidth.
There is a second effect here, our need to anthropomorphize objects around us. Our tendency to model our fellow humans extends to when we interact with other entities in our environment. To our pets as well as inanimate objects around us, such as computers or even our cars. This tendency to over interpret could be a consequence of our limited ability to communicate.2
For more details see this paper “Living Together: Mind and Machine Intelligence”, and this TEDx talk and Chapter 1 in Lawrence (2024).
The Centrifugal Governor
Figure: Centrifugal governor as held by “Science” on Holborn Viaduct
Boulton and Watt’s Steam Engine
Figure: Watt’s Steam Engine which made Steam Power Efficient and Practical.
James Watt’s steam engine contained an early machine learning device. In the same way that modern systems are component based, his engine was composed of components. One of which is a speed regulator sometimes known as Watt’s governor. The two balls in the center of the image, when spun fast, rise, and through a linkage mechanism.
The centrifugal governor was made famous by Boulton and Watt when it was deployed in the steam engine. Studying stability in the governor is the main subject of James Clerk Maxwell’s paper on the theoretical analysis of governors (Maxwell, 1867). This paper is a founding paper of control theory. In an acknowledgment of its influence, Wiener used the name cybernetics to describe the field of control and communication in animals and the machine (Wiener, 1948). Cybernetics is the Greek word for governor, which comes from the latin for helmsman.
A governor is one of the simplest artificial intelligence systems. It senses the speed of an engine and acts to change the position of the valve on the engine to slow it down.
Although it’s a mechanical system a governor can be seen as automating a role that a human would have traditionally played. It is an early example of artificial intelligence.
The centrifugal governor has several parameters, the weight of the balls used, the length of the linkages and the limits on the balls’ movement.
Two principal differences exist between the centrifugal governor and artificial intelligence systems of today.
- The centrifugal governor is a physical system, and it is an integral part of a wider physical system that it regulates (the engine).
- The parameters of the governor were set by hand, our modern artificial intelligence systems have their parameters set by data.
Figure: The centrifugal governor, an early example of a decision-making system. The parameters of the governor include the lengths of the linkages (which effect how far the throttle opens in response to movement in the balls), the weight of the balls (which effects inertia) and the limits of to which the balls can rise.
This has the basic components of sense and act that we expect in an intelligent system, and this system saved the need for a human operator to manually adjust the system in the case of overspeed. Overspeed has the potential to destroy an engine, so the governor operates as a safety device.
The first wave of automation did bring about sabotage as a worker’s response. But if machinery was sabotaged, for example, if the linkage between sensor (the spinning balls) and action (the valve closure) was broken, this would be obvious to the engine operator at start up time. The machine could be repaired before operation.
See Lawrence (2024) Watt’s governor p. 122-5, 127, 131, 143, 144, 184, 198, 202-3, 206, 207, 221, 231, 234, 251, 254, 256-7, 263. See Lawrence (2024) cybernetics founded by p. 131, 143, 306.
Conversation, narrative, and trust
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.
See Lawrence (2024) telepathy p. 248-50. See Lawrence (2024) anthropomorphization (‘anthrox’) p. 30-31, 90-91, 93-4, 100, 132, 148, 153, 163, 216-17, 239, 276, 326, 342.
Trust, Autonomy and Embodiment
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.
Trust is not a slogan; it is the infrastructure that allows autonomy to be devolved without losing control. Autonomy is always conditional: it depends on what information is available, what incentives shape behaviour, and whether escalation and accountability are real. In executive settings, the practical question is: where do we allow delegation (to people or machines), and where do we insist on human judgement and responsibility?
See Lawrence (2024) trust p. 43, 79, 100. See Lawrence (2024) embodiment factor p. 13, 29, 35, 79, 87, 105, 197, 216-217, 249, 269, 327, 353, 363, 369. See Lawrence (2024) topography, information p. 34-9, 43-8, 57, 62, 104, 115-16, 127, 140, 192, 196, 199, 291, 334, 354-5.
What comes next
Challenges
The field of data science is rapidly evolving. Different practitioners from different domains have their own perspectives. We identify three broad challenges that are emerging. Challenges which have not been addressed in the traditional sub-domains of data science. The challenges have social implications but require technological advance for their solutions.
- Paradoxes of the Data Society
- Quantifying the Value of Data
- Privacy, loss of control, marginalization
See Lawrence (2024) System Zero p. 242-7, 306, 309, 329, 350, 355, 359, 361, 363, 364.
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
Further Reading
Chapter 8 of Lawrence (2024)
Chapter 1 of Lawrence (2024)