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?’ 

Article
Culture
Economics
Ethics
6 min read

The rights and wrongs of making money with meme coins

When does investing become speculating, or even addictive gambling?
A montage shows Trump with a raised fist against other images of him and the phrase 'fight fight fight'.
$Trump coin marketing image.
gettrumpmemes.com,

Donald Trump’s “liberation day” tariffs may have driven sharp swings in global financial markets, but his actions in markets a few months earlier were in some ways even more peculiar.

On the Friday before his inauguration as the 47th US President in January, the Republican surprised many with the launch of the $TRUMP memecoin, described by its website as “the only official Trump meme”. The cryptocurrency token, in which Trump’s family business owned a stake, initially soared in value to more than $14bn over that following weekend. 

Then, on the Sunday, Trump’s wife Melania launched her own memecoin, $MELANIA, which reached a value of $8.5bn. Even the pastor who spoke at the president’s inauguration subsequently launched his own memecoin. 

For those wondering what exactly a memecoin is, you are not alone. In short, they are a form of cryptocurrency - an asset class that itself has attracted plenty of questions about its substance and purpose - representing online viral moments. They have no fundamental value or business model and, according to the US securities regulator, “typically have limited or no use or functionality”. 

Donald and Melania Trump’s coins subsequently plunged in price, but still have a value of around $2.5bn and $214mn respectively, according to website CoinMarketCap. 

There are plenty of others in existence. PEPE, based on a comic frog, has a value of around $3.6bn; BONK, a cartoon dog, has a market cap of $1.5bn; and PNUT, a reference to a squirrel euthanised by authorities in New York and about which Trump was allegedly “fired up” (although doubt has since been cast on the president’s involvement in the matter), is still valued at around $174mn, despite having fallen sharply in price.  

Dogecoin, seen as the world’s first memecoin and originally created as a joke, boasts a market value of around $25bn. (There are other memecoins which may not be suitable for these pages). 

Some people’s willingness to buy an “asset” with no use or fundamental value may seem strange to more traditional investors. But it can be viewed as just one manifestation of the speculative investor behaviour evident since the onset of the coronavirus pandemic and, indeed, at times throughout history. 

The price of Bitcoin recently rose above $100,000, despite many investors still viewing it as having little or no value (in 2023 the UK’s Treasury select committee described cryptocurrencies as having “no intrinsic value, huge price volatility and no discernible social good”). In early 2021, shares in GameStop - a loss-making US video games retailer that some hedge funds were betting against - rocketed as much as 2,400 per cent, as retail investors piled in, many with the aim of inflicting pain on the hedge fund short sellers (in that respect at least, a highly successful strategy that became the subject of the film Dumb Money). The huge rise in AI and other tech stocks in recent years - until the recent tariff-driven volatility - has also been described as a bubble by some commentators. 

Whether or not such episodes can be compared to infamous bouts of speculative mania in history depends on your point of view (and often can only be judged with the benefit of hindsight) - be it the 17th century Dutch tulip bulb mania, shares in the South Sea Company in the 18th century or the dotcom boom and bust of the late 1990s and early 2000s. 

But it does give rise to the question of when investment should start to be described as speculation or even as gambling? And what are the rights and wrongs of any of those activities? 

There can be negative effects, for instance if the actions of speculators force businesses in the real economy to change their plans or divert time and resources... 

Gambling can be thought of as risking a stake on, for instance, the result of a game of chance or sport in the hope of a bigger payout. While often the result is purely down to chance, in some cases a strategy or an element of research (for instance of a horse or football team’s form) can be used. Investment, in contrast, tends to involve purported economic utility and assets believed to have some sort of underlying value, and holds the hope of future profit (although there are also plenty of bad investments or those that have gone to zero). While an investor must be prepared to lose their entire stake, in some cases such an event is relatively unlikely (for instance, if they buy a fund tracking the performance of a major stock exchange). Speculation is harder to define, but is generally seen as shorter term than investment, with more chance of a bigger gain or loss, and dependent on price fluctuations. Rightly or wrongly, the term has a more negative connotation than investment. 

One writer who explored the ethics of these activities was Oswald von Nell-Breuning, a Jesuit theologian and economist who served as an adviser to the Pope and who was banned from publishing under the Nazis. 

While he found that “one general definition cannot capture all the nuances” of speculation, he identified two different types of speculative activity - one that was purely trying to make a profit from financial market trading, and one based on trying to create a viable business. (See this article in the Catholic Social Science Review for a fuller explanation of Nell-Breuning’s views on speculation). 

As the CSSR article shows, Nell-Breuning found that there can be positive effects from speculation - one might think of better liquidity and price discovery in a market, while, in commodity futures markets, speculators allow producers to hedge risk

But he also argued that there can be negative effects, for instance if the actions of speculators force businesses in the real economy to change their plans or divert time and resources away from production. 

And whereas gambling typically takes place within a circle of players who have chosen to take part, speculation, he wrote, can affect a greater portion of society - for instance, if it affects the price of shares or bonds they hold. 

The Bible - on which Nell-Breuning’s faith and analysis was based - does not take a prescriptive approach to such activities. But it does provide some interesting guidance.  

An entrepreneurial approach to business and investment is applauded, for instance when the writer of the book of Proverbs (traditionally believed to be King Solomon) praises the virtues of “an excellent wife”. These include investing in a field and using her earnings from business to plant a vineyard, and feeding her family from her gains. 

Jesus tells a story of a master who, before going on a journey, gives his property to his servants, each according to their ability. To one he gives five “talents” (a large unit of money), to a second two and to a third servant he gives one. 

The first servant trades with his talents and makes five more talents - a 100 per cent profit - and is applauded by the master on his return. The second servant also trades and similarly makes two more talents and is again applauded. 

But the third servant, being afraid and believing the master to be “a hard man”, hides the money in a hole in the ground. He is condemned as “wicked and slothful”, and told that he should at least have put the money in the bank. 

While Jesus’s story may primarily be about how we view God’s nature, how we use our God-given abilities and whether or not we can take risks in faith for Him, it is also hard not to see investment and indeed wise speculation as being virtuous activities here. Putting the money into a bank account is, in this story anyway, more of a fallback option. 

But the Bible also warns us against putting money above all else in our lives. The love of money is, famously, a root of all sorts of evil, while we are also told to be content with what we have, and that “wealth gained hastily will dwindle”. 

Nell-Breuning similarly warns that a “get-rich-quick” mindset, when this is placed above all else, can be harmful, and advises caution in situations where the lure of big profits can lead the speculator into market manipulation or fraud. 

After all, both gambling and crypto trading have the potential to become dangerous and damaging addictions needing treatment

Ultimately, Nell-Breuning struggled to come to a simple conclusion on the question of whether speculation, in and of itself, is morally wrong. It is, he wrote, a judgment call for those involved. 

When making such decisions ourselves, his - and the Bible’s - warnings may be worth bearing in mind.