Article
Culture
Film & TV
Monsters
Weirdness
Zombies
5 min read

Zombies: a philosopher's guide to the purpose-driven undead

Don’t dismiss zombiecore as lowbrow.

Ryan is the author of A Guidebook to Monsters: Philosophy, Religion, and the Paranormal.

A regency woman dabs her mouth with a bloody hankerchief.
Lilly James in Pride and Prejudice and Zombies.
Lionsgate.

Writing from his new book, A Guidebook to Monsters, Ryan Stark delves into humanity’s fascination for all things monsterous. In the second of a two-part series, he asks what and where zombies remind us of, and why they caught the eyes of C.S. Lewis and Salvador Dali 

 

On how Frankenstein’s monster came to life nobody knows for sure, but he is more urbane than zombies tend to be. Nor do Jewish golems and Frosty the Snowman count as zombiecore. The latter sings too much, and both are wrongly formulated. Frosty comes from snow, obviously, and the golems—from mere loam, not what the Renaissance playwrights call “gilded loam,” that is, already pre-assembled bodies, which is a zombie requirement. Tolkien’s orcs function likewise as golem-esque monsters, cast from miry clay and then enlivened by the grim magic of Mordor. We do not, for instance, discover scenes with orc children. 

And neither is Pinocchio a zombie, nor Pris from Blade Runner, but dolls, automatons, and C3POs border upon the land of zombies insofar as they all carry a non-human tint. Zombies, however, carry something else as well, a history of personhood, and so in their present form appear as macabre parodies of the human condition writ large. They are gruesome undead doppelgangers, reminding us of who we are not and perhaps—too—of where we are not. Hell is a place prepared for the Devil and his angels, Christ tells us in the book of Matthew. And maybe, subsequently, for zombies. 

Kolchak, in an episode of Kolchak: The Night Stalker aptly titled “The Zombie,” correctly discerns the grim scenario at hand: “He, sir, is from Hell itself!”  

C.S. Lewis pursues a similar line of thinking in The Problem of Pain: “You will remember that in the parable, the saved go to a place prepared for them, while the damned go to a place never made for men at all. To enter Heaven is to become more human than you ever succeeded in being on earth; to enter Hell is to be banished from humanity. What is cast (or casts itself) into Hell is not a man: it is ‘remains.’” Lewis makes an intriguing point, which has as its crescendo the now-famous line about the doors of Hell: “I willingly believe that the damned are, in one sense, successful, rebels to the end; that the doors of Hell are locked on the inside by zombies.” I added that last part about zombies. 

I make this point—in part—to correct those in the cognoscenti who dismiss zombies as a subject too lowbrow for serious consideration.

Not everyone believes in Hell, of course, yet most concede that some people behave worse than others, which also helps our cause. Indeed, part of zombiecore’s wisdom is to show that bad people often produce more horror than the zombies themselves. Such is the character of Legendre Murder, a case in point from the film White Zombie. Not fortunate in name, Mr. Murder runs a dark satanic mill populated by hordes of zombie workers, which is the film’s heavy-handed critique of sociopathic industrialization. The truth to be gleaned, here, is that zombies did not invent the multinational corporation; rather, they fell prey to it. 

We might think, too, of Herman Melville’s dehumanized characters from Bartleby the Scrivener: Nippers, Turkey, Ginger Nut, and the other functionaries whose nicknames themselves indicate the functions. From an economic standpoint, their value becomes a matter of utility, not essence, which is Melville’s reproach of the despairingly corporate drive to objectify personhood—of which zombies are an example beyond the pale. They might as well be fleshy mannequins, in fact, and as such provide the perfect foil for the human being properly conceived. 

Here, then, is why we do not blame zombies for eating brains, nor do we hold them accountable for wearing white pants after Labor Day, as some inevitably do. They cannot help it—in ethics and in fashion. Perhaps especially in fashion. The best we can hope for in the realm of zombie couture is Solomon Grundy, the quasi-zombie supervillain who holds up his frayed pants with a frayed rope, a fashion victory to be sure, however small it might be, though “zombie fashion” is a misnomer in the final analysis. They wear clothes, but not for the same reasons we do. 

The point holds true for Salvador Dali’s zombies as well, most of whom find themselves in nice dresses. I make this point—in part—to correct those in the cognoscenti who dismiss zombies as a subject too lowbrow for serious consideration. Not so. Exhibit A: the avant-garde Dali, darling of the highbrow, or at least still of the middlebrow, now that his paintings appear on t-shirts and coffee mugs. Burning giraffe. Mirage. Woman with Head of Roses. All zombies, too ramshackle and emaciated to live, never mind the missing head on the last one, and yet there they are posed for the leering eye, not unlike those heroin-chic supermodels from Vogue magazine in the late 1990s. Necrophilia never looked so stylish. 

The zombie’s gloomy predicament bears a striking resemblance to that of the Danaids in the classical underworld, those sisters condemned to fill a sieve with water for all eternity...

But never let it be said that zombies are lazy. They are tired, to be sure. Their ragged countenances tell us this, but they are not indolent. Zombies live purpose-driven undead lives. They want to eat brains, or any human flesh, depending on the mythos, and their calendars are organized accordingly. No naps. No swimming lessons. Just brains.  

But we quickly discern that no amount of flesh will satisfy. There is always one more hapless minimart clerk to ambush, one more sorority girl in bunny slippers to chase down the corridor. In this way, the zombie’s gloomy predicament bears a striking resemblance to that of the Danaids in the classical underworld, those sisters condemned to fill a sieve with water for all eternity, an emblem of the perverse appetite unchecked, which has at its core the irony of insatiable hunger. And as the pleasure becomes less and less, the craving becomes more and more. The law of diminishing returns. So, it is with all vices. The love of money demands more money, and the love of brains, more brains. 

And so, in conclusion, a prayer. God bless the obsessive-compulsive internet shoppers, the warehouse workers on unnecessarily tight schedules, and the machine-like managers of the big data algorithms. God bless the students who sedate themselves in order to survive their own educations, taking standardized test after standardized test. And God bless the Emily Griersons of the world, who keep their petrified-boyfriend corpses near them in the bedroom, an emblem of what happens when one tries too mightily to hold on to the past. And God help us, too, when we see in our own reflections a zombie-like affectation, the abyss who stares back at us and falsely claims that we are not the righteousness of God, as Paul says we are in 2 Corinthians. And, finally, Godspeed to Gussie Fink-Nottle from the P.G. Wodehouse sagas: “Many an experienced undertaker would have been deceived by his appearance, and started embalming on sight.”  

  

From A Guidebook to Monsters, Ryan J. Stark.  Used by permission of Wipf and Stock Publishers.   

Article
AI
Culture
10 min read

We’ll learn to live with AI: here’s how

AI might just help us with life’s dilemmas, if we are responsible.

Andrew is Emeritus Professor of Nanomaterials at the University of Oxford. 

Two construction workers stand and talk with a humanoid AI colleague.
Nick Jones/Midjourney.ai

Anxiety about algorithms is nothing new.  Back in 2020, It was a bad summer for the public image of algorithms. ‘I am afraid your grades were almost derailed by a mutant algorithm’, the then Prime Minister told pupils at a school. No topic in higher education is more sensitive than who gets a place at which university, and the thought that unfair decisions might be based on an errant algorithm caused understandable consternation. That algorithms have been used for many decades with widespread acceptance for coping with examination issues ranging from individual ill health to study of the wrong set text by a whole school seems quietly to have slipped under the radar.  

Algorithmic decision-making is not new. Go back thousands of years to Hebrew Deuteronomic law: if a man had sex with a woman who was engaged to be married to another man, then this was unconditionally a capital offence for the man. But for the woman it depended on the circumstances. If it occurred in a city, then she would be regarded as culpable, on the grounds that she should have screamed for help. But if it occurred in the open country, then she was presumed innocent, since however loudly she might have cried out there would have been no one to hear her. This is a kind of algorithmic justice: IF in city THEN woman guilty ELSE woman not guilty.  

Artificial intelligence is undergoing a transition from classification to decision-making. Broad artificial intelligence, or artificial general intelligence (AGI), in which the machines set their own goals, is the subject of gripping movies and philosophical analysis. Experts disagree about whether or when AGI will be achieved. Narrow artificial intelligence (AI) is with us now, in the form of machine learning. Where previously computers were programmed to perform a task, now they are programmed to learn to perform a task.  

We use machine learning in my laboratory in Oxford. We undertake research on solid state devices for quantum technologies such as quantum computing. We cool a device to 1/50 of a degree above absolute zero, which is colder than anywhere in the universe that we know of outside a laboratory, and put one electron into each region, which may be only 1/1000 the diameter of a hair on your head. We then have to tune up the very delicate quantum states. Even for an experienced researcher this can take several hours. Our ‘machine’ has learned how to tune our quantum devices in less than 10 minutes.  

Students in the laboratory are now very reluctant to tune devices by hand. It is as if all your life you have been washing your shirts in the bathtub with a bar of soap. It may be tedious, but it is the only way to get your shirts clean, and you do it as cheerfully as you can … until one day you acquire a washing machine, so that all you have to do is put in the shirts and some detergent, shut the door and press the switch. You come back two hours later, and your shirts are clean. You never want to go back to washing them in the bathtub with a bar of soap. And no one wants to go back to doing experiments without the machine. In my laboratory the machine decides what the next measurement will be.  

Suppose that a machine came to know my preferences better than I can articulate them myself. The best professionals can already do this in their areas of expertise, and good friends sometimes seem to know us better than we know ourselves. 

Many tasks previously reserved for humans are now done by machine learning. Passport control at international airports uses machine learning for passport recognition. An experienced immigration officer who examines one passport per minute might have seen four million faces by the end of their career. The machines were trained on fifty million faces before they were put into service. No wonder they do well.  

Extraordinary benefits are being seen in health care. There is now a growing number of diagnostic studies in which the machines outperform humans, for example, in screening ultrasound scans or radiographs. Which would you rather be diagnosed by? An established human radiologist, or a machine with demonstrated superior performance? To put it another way, would you want to be diagnosed by a machine that knew less than your doctor? Answer: ‘No!’ Well then, would you want to be diagnosed by a doctor who knew less than the machine? That’s more difficult. Perhaps the question needs to be changed. Would you prefer to be treated by a doctor without machine learning or by a doctor making wise use of machine learning?  

If we want humans to be involved in decisions involving our health, how much more in decisions involving our liberty. But are humans completely reliable and consistent? A peer-reviewed study suggested that the probability of a favourable parole decision depended on whether the judges had had their lunch. The very fact that appeals are sometimes successful provides empirical evidence that law, like any other human endeavour, involves uncertainty and fallibility. When it became apparent that in the UK there was inconsistency in sentencing for similar offences, in what the press called a postcode lottery, the Sentencing Council for England and Wales was established to promote greater transparency and consistency in sentencing. The code sets out factors which judges must consider in passing sentence, and ranges of tariffs for different kinds of crimes. If you like, it is another step in algorithmic sentencing. Would you want a machine that is less consistent than a judge to pass sentence? See the sequence of questions above about a doctor.  

We may consider that judicial sentencing has a special case for human involvement because it involves restricting an individual’s freedom. What about democracy? How should citizens decide how to vote when given the opportunity?  Voter A may prioritise public services, and she may seek to identify the party (if the choices are between well identified parties) which will best promote education, health, law and order, and other services which she values. She may also have a concern for the poor and favour redistributive taxation. Voter B may have different priorities and seek simply to vote for the party which in his judgement will leave him best off. Other factors may come into play, such as the perceived trustworthiness of an individual candidate, or their ability to evoke empathy from fellow citizens.  

This kind of dilemma is something machines can help with, because they are good at multi-objective optimisation. A semiconductor industry might want chips that are as small as possible, and as fast as possible, and consume as little power as possible, and are as reliable as possible, and as cheap to manufacture as possible, but these requirements are in tension with one another. Techniques are becoming available to enable machines to make optimal decisions in such situations, and they may be better at them than humans. Suppose that a machine came to know my preferences better than I can articulate them myself. The best professionals can already do this in their areas of expertise, and good friends sometimes seem to know us better than we know ourselves. Suppose also that the machine was better than me at analysing which candidate if elected would be more likely to deliver the optimal combination of my preferences. Might there be something to be said for benefitting from that guidance?  

If we get it right, the technologies of the machine learning age will provide new opportunities for Homo fidelis to promote human flourishing at its best.

By this point you may be sucking air through your intellectual teeth. You may be increasingly alarmed about machines taking decisions that should be reserved for humans. What are the sources of such unease? One may be that, at least in deep neural networks, the decisions that machines make may be only as good as the data on which they have been trained. If a machine has learned from data in which black people have an above average rate of recidivism, then black people may be disadvantaged in parole decisions taken by the machine. But this is not an area in which humans are perfect; that is why we have hidden bias training. In the era of Black Lives Matter we scarcely need reminding that humans are not immune to prejudice.  

Another source of unease may be the use to which machine learning is put for commercial and political ends. If you think that machine learning is not already being applied to you, you are probably mistaken. Almost every time you do an online search or use social media, the big data companies are harvesting your data exhaust for their own ends. Even if your phone calls and emails are secure, they still generate metadata. European legislation is better than most, and the Online Safety Act 2023 will make the use of Internet services safer for individuals in the United Kingdom. But there is a limit to what regulation can protect, and 2024 is likely to see machine learning powerfully deployed to sway voters in elections in half the world. Targeted persuasion predates AI, as Othello’s Iago knew, but machine learning has brought it to an unprecedented level of industrialisation, with some of the best minds in the world paid some of the highest salaries in the world to maximise the user’s screen time and the personalisation of commercial and political influence.  

Need it be so? In some ways advances in machine learning are acting as the canary in the mine, alerting us to fundamental questions about what humans are for, and what it means to be human. The old model of Homo economicus—rational, selfish, greedy, lazy man—has passed its sell-by date. It is being replaced by what I like to call Homo fidelis—ethical, caring, generous, energetic woman and man. For as long as AGI remains science fiction, it is up to humans to determine what values the machines are to implement. If we get it right, the technologies of the machine learning age will provide new opportunities for Homo fidelis to promote human flourishing at its best.  

Whatever the future capabilities of machines, they cannot be morally load-bearing because humans are self-aware and mortal, whereas machines are not.

Paul Collier and John Kay

Christians have been thinking about what it means to be human for two millennia, building on what came before, and so they ought to have something to contribute to how humans flourish. In It Keeps Me Seeking, my co-authors and I ask our readers to imagine that they were writing about three thousand years ago for people who knew nothing of modern genetics or psychological science about what it means to be human. ‘You are writing for a storytelling culture, and so you would probably put it in the form of a story. Let’s say you set it in a garden. The garden is pleasant, but it is also designed for character formation, and so there is work to do, and also the possibility for a hard moral choice. You want to convey that humans need social interactions (for the same reason that solitary confinement is a severe punishment), and so you try the literary thought experiment of having one solitary man and letting him encounter animals and name them. Animals can be useful and they can be good company. But ultimately no animals, not even a dog, are fully satisfactory as partners in work and companions in life. Humans need humans. An enriching component of human relationships is sex. So, the supreme gift to the solitary man in our story is companionship with an equal who is both like and unlike; a woman. It is hardly a complete account, but it is a good start. Oh, and there is one other aspect. They should be free of the shame which lies at the root of so much psychological disorder.’  

As far as it goes, would you regard such an account as complete? If not, what would you add next? You can see where this is going. To be human you need to be responsible. So, you let the humans face the moral choice. You can even include an element of disinformation to make the choice harder. And then when it goes horribly wrong you let them discover that they are responsible for their actions, and that blaming one another does not help. If you have God in your story, then (uniquely for the humans) responsibility consists of accountability to God. This is how human distinctiveness was addressed in early Jewish thought. As an early articulation that to be human means to be responsible, the story of Adam and Eve is unsurpassed.  

In Greed is Dead, Paul Collier and John Kay reference Citizenship in a Networked Age as brilliantly elucidating the issue of morally pertinent decision-taking. They write, ‘Whatever the future capabilities of machines, they cannot be morally load-bearing because humans are self-aware and mortal, whereas machines are not. Machines can be used not only to complement and enhance human decision-making, but for bad: search optimisation has already morphed into influence-optimisation. We must keep morally pertinent decision-taking firmly in the domain of humanity.’  

The nature of humanity includes responsibility—for wise use of machine learning and much more besides. Accountability is part of life for people with widely differing philosophical, ethical, and religious world views. If we are willing to concede that accountability follows responsibility, then we should next ask, ‘Accountable to whom?’