Review
Books
Culture
7 min read

Cormac McCarthy's harrowed inheritance

Written before the death of Cormac McCarthy, Austin Stevenson reviews the acclaimed author's last sibling novels, exploring the frugal conversations within them and how dialogues shape virtue.

Austin is a philosophical theologian who works at the intersection of philosophy, religion, and culture.

A diver swims above a crashed plane lying on the sea bed.
A diver investigates a crashed plane on the seabed.
Mael Balland via Unsplash.

This review was first published in March 2023, before Cormac McCarthy's death in June 2023.

When reading The Passenger, the first novel Cormac McCarthy has published since his Pulitzer Prize winning book The Road came out in 2006, I was reminded of a comment E. M. Forster jotted in his notebook about Henry James. ‘However hard you shake his sentences no banality falls out.’ McCarthy has drawn forth prodigious lyricism and acuity by some syntactical alchemy. Rarely in contemporary fiction have I drawn so much delight from just the words on the page. Much of his prose is poetry shrouded in paragraphs.  

He scanned the landscape.  

Here’s a dream. 

This man was a forger of antiquities. 

He travelled in documentation. 

In the instruments for their preparation. 

An old world figure. A dark suit, somewhat travelled in. 

A down at the heels formality 

to which yet clung the odor of the exotic. 

A spectre of saccharine sincerity haunts modern fiction, and the fear of it has all but eviscerated mainstream novels of the polyphonic ornamentation of classical literature. What McCarthy has accomplished here is to recover the elegance, musicality, and intricacy of such great works, but in the context of a spare and denuded grammatical landscape. Sentimentality could not survive for a moment in these two novels, and yet they are genuine and raw to the core.   

The Passenger follows Bobby Western, a deep-sea salvage diver who is inspecting a private jet that crashed off the Gulf Coast. He observes that, among the bodies strapped to the seats in this sunken tomb, one passenger from the manifest is missing. This kicks off the plot of the novel, wherein shadowy figures interrogate and surveil Bobby to ascertain what he knows about the missing passenger, seizing his assets and pushing him to an itinerant existence on the road. And yet, to explain the plot of The Passenger is largely to conceal what it is about, for it is primarily a book about ideas: physics, metaphysics, mathematics, and language. 

The Passenger’s sibling novel, Stella Maris, is set eight years earlier, in 1972, and follows Bobby’s younger sister, Alicia. It is named for the midwestern psychiatric institution Alicia checks herself into and consists of conversations between Alicia and her psychiatrist. Bobby and Alicia are the children of a physicist who worked on the Manhattan Project with Oppenheimer. “His father. Who had created out of the absolute dust of the earth an evil sun by whose light men saw like some hideous adumbration of their own ends through cloth and flesh the bones in one another’s bodies.” Both initially followed his footsteps into academia, but Bobby dropped out of Caltech to race cars in Europe. Alicia quit after having exhausted the intellectual grist internal to mathematics and failed to resolve the foundational questions haunting the discipline (and reality) itself. “She knew that in the end you really cant know. You cant get hold of the world. You can only draw a picture.” 

Bobby is lying in a coma in Europe for the entirety of Stella Maris after crashing in a Formula 2 race. By the time he wakes, Alicia has died by suicide. She is ever-present in The Passenger but only as a memory, and the novel is punctuated by chapters that recount her conversations with the Kid, a hallucinatory figure that has followed her since puberty. “The Thalidomide Kid and the old lady with the roadkill stole and Bathless Grogan and the dwarves and the Minstrel Show. All of them gathered at the foot of her bed.” Alicia may or may not be schizophrenic. And autistic. She is also a world-class violinist.  

The philosopher Alasdair MacIntyre has argued that it is from those who came before us that we receive the depth or poverty of our language and, to some degree, our conversational habits, and it is through the right kinds of conversations that we learn the relationship between the various goods to which we order our lives and become educated in the virtues. The poverty of conversational idioms that many of us have received does much to cut us off from participation in and pursuit of the goods that contribute to our flourishing. I wonder if literature is a possible antidote to this. Specifically, literature with rich dialogue. And this is one of McCarthy’s great strengths. 

'McCarthy is intent on exploring the nature of reality in this novel.'

In dialogue, his characters often start with the end in mind, and then find their way together. Or don’t. Their conversations are frugal, consisting primarily of three- or four-word sentences, and yet they almost always stumble onto to questions of deep significance. There are a lot of rough characters in these novels, but they share a surprising vulnerability. As always, McCarthy doesn’t use quotation marks or tell us who is speaking. When he wants us to, it is easy to follow the flow of dialogue, but occasionally he throws us off the scent. Particularly when Alicia is conversing with her hallucinations, their voices often meld together. The effect amplifies the ethereal quality of their exchanges. 

Bobby is in the habit of asking people if they believe in God—a practise he seems to have picked up from his Granellen (his grandmother).    

Do you believe in God, Bobby? 

I don’t know, Granellen. You asked me that before. I told you. I dont know anything. The best I can say is that I think he and I have pretty much the same opinions. On my better days anyway. 

No one has confident answers to this question, but it often serves to push the conversation along an interesting direction. “I dont know who God is or what he is. But I dont believe all this stuff got here by itself.” McCarthy is intent on exploring the nature of reality in this novel, and for him, the question of God is clearly part of that exploration, wherever it may lead. Fortunately, he is well aware that the question of God is not the same question under debate between fundamentalists and atheists.  

Do you think of yourself as an atheist?  

God no. Those were the good old days.  

In their own ways, these characters exhibit an immanence that is haunted by transcendence. This search for some kind of meaning in the everyday stuff of existence might stand behind McCarthy’s frequent use of sacramental imagery drawn from the Catholicism of his youth. Evil cannot be depicted adequately without a conception of the good of which it is a privation. One might read McCarthy as reverse-engineering this process—ascertaining goodness by staring down its absence.  

There is a tension in these novels between words and numbers. Which is more real? These questions are closely bound up with the characters’ struggles with mental illness and grief. For Alicia, “intelligence is numbers. It’s not words. Words are things we’ve made up. Mathematics is not.” She insists on the transcendent nature of mathematics and many of her conversations with her therapist centre on precisely these questions about what is real, true, stable, with frequent mention of Platonism. This brought to mind Viktor Frankl’s insistence that treating mental illness requires that we acknowledge its existential dimension. ’Man’s search for meaning is the primary motivation in his life.’ Alicia’s mental illness is bound up with her own search for meaning, and vice versa, as well as with the dark cloud that hangs over her family’s legacy. “For a long time I’ve suspected that we might be simply incapable of imagining the epochal evils of which we stand rightly accused and I thought it at least a possibility that the structure of reality itself harbors something like the forms of which our sordid history is only a pale reflection.” History falls short of the forms of the age.   

Transcendence isn’t the only spectre that haunts these pages, and there is a kind of paranoia running through the narrative that seems fitting in an era rife with conspiracy thinking. Given his father’s exploits, Bobby is not particularly surprised to discover documents missing from Granellen’s home, or his own apartment rifled through while he’s gone. As Joseph Heller wrote, 'Just because you’re paranoid doesn’t mean they’re not after you.' It’s clear that someone is after Bobby, and the entire family may or may not be subject to clandestine observation. But there is also a broader sense of powers beyond our control watching, hounding, manipulating.   

You think somebody’s after you? 

I don’t know. I just wonder if maybe lots of people dont feel that way. 

For no reason. 

Yeah. 

They have inherited a troubled legacy, but each, in their own way, has learned to talk about it, and that’s no small thing. This may be McCarthy’s most ambitious work, and you don’t need to understand it to find it extremely enjoyable.   

 

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?’