It's our mistakes that make us human

What we learn distinguishes us from tech.

Silvianne Aspray

Silvianne Aspray is a theologian and postdoctoral fellow at the University of Cambridge.

A man staring at a laptop grimmaces and holds his hands to his head.

Francisco De Legarreta C. on Unsplash.

The distinction between technology and human beings has become blurry: AI seems to be able to listen, answer our questions, even respond to our feelings. It becomes increasingly easy to confuse machines with humans. In this situation, it is increasingly important to ask: What makes us human, in distinction from machines? There are many answers to this question, but for now I would like to focus on just one aspect of what I think is distinctively human: As human beings, we live and learn in time.

To be human means to be intrinsically temporal. We live in time and are oriented towards a future good. We are learning animals, and our learning is bound up with the taking of time. When we learn to know or to do something, we necessarily make mistakes, and we take practice. But keeping in view something we desire – a future good – we keep going.

Let’s take the example of language. We acquire language in community over time. Toddlers make all sorts of hilarious mistakes when they first try to talk, and it takes them a long time even to get single words right, let alone to try and form sentences. But they keep trying, and they eventually learn. The same goes with love: Knowing how to love our family or our neighbours near and far is not something we are good at instantly. It is not the sort of learning where you absorb a piece of information and then you ‘get’ it. No, we learn it over time, we imitate others, we practice and even when we have learned, in the abstract, what it is to be loving, we keep getting it wrong.

This, too, is part of what it means to be human: to make mistakes. Not the sort of mistakes machines make, when they classify some information wrongly, for instance, but the very human mistake of falling short of your own ideal. Of striving towards something you desire – happiness, in the broadest of terms – and yet falling short, in your actions, of that very goal. But there’s another very human thing right here: Human beings can also change. They – we – can have a change of heart, be transformed, and at some point in time, actually start to do the right thing – even against all the odds. Statistics of past behaviours, do not always correctly predict future outcomes. Part of being human means that we can be transformed.

Transformation sometimes comes suddenly, when an overwhelming, awe-inspiring experience changes somebody’s life as by a bolt of lightning. Much more commonly, though, such transformation takes time. Through taking up small practices, we can form new habits, gradually acquire virtue, and do the right thing more often than not. This is so human: We are anything but perfect. As Christians would say: We have a tendency to entangle ourselves in the mess of sin and guilt. But we also bear the image of the Holy One who made us, and by the grace and favour of that One, we are not forever stuck in the mess. We are redeemed: are given the strength to keep trying, despite the mistakes we make, and given the grace to acquire virtue and become better people over time. All of this to say that being human means to live in time, and to learn in time.

So, this is a real difference between human beings and machines: Human beings can, and do strive toward a future good.

Now compare this to the most complex of machines. We say that AI is able to “learn”. But what does it mean to learn, for AI? Machine learning is usually categorized into supervised learning, unsupervised and self-supervised learning. Supervised learning means that a model is trained for a specific task based on correctly labelled data. For instance, if a model is to predict whether a mammogram image contains a cancerous tumour, it is given many example images which are correctly classed as ‘contains cancer’ or ‘does not contain cancer’. That way, it is “taught” to recognise cancer in unlabelled mammograms. Unsupervised learning is different. Here, the system looks for patterns in the dataset it is given. It clusters and groups data without relying on predefined labels. Self-supervised learning uses both methods: Here, the system uses parts of the data itself as a kind of label – such as, for instance, predicting the upper half of an image from its lower half, or the next word in a given text. This is the predominant paradigm for how contemporary large-scale AI models “learn”.

In each case, AI’s learning is necessarily based on data sets. Learning happens with reference to pre-given data, and in that sense with reference to the past. It may look like such models can consider the future, and have future goals, but only insofar as they have picked up patterns in past data, which they use to predict future patterns – as if the future was nothing but a repetition of the past.

So this is a real difference between human beings and machines: Human beings can, and do strive toward a future good. Machines, by contrast, are always oriented towards the past of the data that was fed to them. Human beings are intrinsically temporal beings, whereas machines are defined by temporality only in a very limited sense: it takes time to upload data, and for the data to be processed, for instance. Time, for machines, is nothing but an extension of the past, whereas for human beings, it is an invitation to and the possibility for being transformed for the sake of a future good. We, human beings, are intrinsically temporal, living in time towards a future good – which machines do not.

In the face of new technologies we need a sharpened sense for the strange and awe-inspiring species that is the human race, and cultivate a new sense of wonder about humanity itself.

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Conspiracy theory

Death & life

29 August 2024

4 min read

A Bayesian theory of death

The sinking of the superyacht displays the probability, and banality, of death.

George Pitcher

George is a visiting fellow at the London School of Economics and an Anglican priest.

Rescue workers look at the plan of a yacht.

The search for the Bayesian.

Vigili del Fuoco.

On any statistical calculation, the probability of dying by drowning when your luxury yacht suddenly and inexplicably sinks at anchor in the Mediterranean has to be extremely low.

So it’s the cruellest of ironies that tech tycoon Mike Lynch should so die, along with his daughter and five others, having devoted his commercial life to the application of such statistical probabilities. He had named his yacht Bayesian after the 18th-century theorem that introduced the idea that probability expresses a degree of belief in an event.

That doesn’t expressly mean religious belief. But, intriguingly, it doesn’t exclude it either. According to Thomas Bayes, who published his theorem in 1763, the calculable degree of belief may be based on prior knowledge about an event, such as the results of previous experiments, or on personal beliefs about it.

In essence, you don’t believe your yacht will capsize in the night and sink in seconds, because your experience tells you so. That belief can mathematically be included in the probability of it happening.

We can transfer the method into religious praxis. Christian belief in the event of resurrection, for instance, can be calculated in the probability that the deaths of the Lynches and others aboard the Bayesian are not the end of their existence.

It’s an intriguing legacy of Lynch’s work for theologians. But it’s the sheer lack of probability of the lethal event occurring at all that lends it its random banality. It’s that death visited those asleep on a yacht in the small hours that lends this news story such tireless legs, not just that these were super-rich masters and mistresses of the universe.

There have been bitter observations on social media that the Bayesian’s victims have commanded limitlessly greater attention than the many thousands of refugees who die in small-boat crossings of the Mediterranean every year.

This is a category mistake. And again, Bayesian theory can be deployed. Experience supports our belief that crossing the sea in overcrowded and unseaworthy vessels can all too often lead to tragically terminal events. The probability of death is plain. Again, it’s the sheer randomness of the Bayesian yacht event that sets it apart.

If death can visit at any time, there can be no difference in the valuation of long or short lives.

That randomness brings us back to the banality of sudden death among us, almost its ordinariness, something that just happens, often entirely out of the blue. The prayer book has the funeral words “in the midst of life we are in death”, meaning that death is our constant living companion. But that doesn’t quite cut it for me, because it tells us it’s there, but nothing of its true significance.

The tenets of Christian faith are regularly said to be those of a death cult; that it’s a deep-seated fear of death that leads us to avoid it with assurances of eternal life. But it’s the sheer banality of death, as displayed in the randomness of the Bayesian event, that seems to knock down that idea. In its randomness, death looks ridiculous rather than evil.

Conspiracy theories around the sinking of the Bayesian are a kind of denial of the reality of death too. We want there to be more to it than the utterly banal.

Author Hannah Arendt coined the phrase “the banality of evil” when covering the trial of Nazi holocaust architect Adolf Eichmann in Jerusalem. I’d want to suggest that it’s that same banality, that basic human ordinariness, that is the real nature of the supposed grim reaper, rather than his evil.

None of this can comfort the Lynch family, who mourn the loss of a much-loved father and his young daughter, or the families of the others who lost their lives on the Bayesian. But it is meant to go some way towards an explanation of what we mean in Christian theology when we bandy about phrases such as “the defeat of death”. Because it’s not a wicked serpent that’s been defeated, more of a pointless clown.

There is something especially painful about the death of the young, such as that of 18-year-old Hannah Lynch on the Bayesian that night, a young woman on the threshold of life. And – God knows – the even younger lives we’ve read about being taken lately.

But the concept of banality may lead us to another tenet of faith: The completeness of every life. If death can visit at any time, there can be no difference in the valuation of long or short lives.

A poem, often ascribed to a former dean of St Paul’s cathedral, begins with the line: “Death is nothing at all.” That’s wrong, as an idea. Death is as significant an event as birth. But its defeat is in keeping it in its place.

The dignity in simplicity with which football manager Sven-Göran Eriksson greeted his final illness is a masterclass in this tactic for life. Death isn’t to be negotiated, it’s just there.

In the end, death isn’t a Bayesian probability, it’s a certainty, for all of us. The difference, in Bayesian theory, must be the belief we bring to our personal calculations of the probability of the event.