Essay

AI - Artificial Intelligence

Comment

30 October 2023

11 min read

The summit of humanity: decoding AI's affectations

An AI summit’s prophecies need to be placed in the right philosophical register, argues Simon Cross. Because being human in an AI age still means the same thing it has for millennia.

Simon Cross

Simon Cross researches ethical aspects of technology and advises on the Church’s of England's policy and legislative activity in these areas.

An AI generated image of robot skulls with bulging eyes on a shelf receding diagonally to the left.

Alessio Ferretti on Unsplash.

The UK’s global artificial intelligence (AI) conference is nearly upon us. If the UK had a ‘prophecy office’ it would have issued a yellow or even amber warning for the first days of November by now. Prophecy used to be a dangerous business, the ancient text of Deuteronomy sanctioned death for false prophets, equating its force with a leading away from God as the ultimate ground of truth. But risks duly acknowledged, here is a prophecy about the prophecies to come. The global AI conference will loudly proclaim three core prophecies about AI.

This time it’s different. Yes, we said that before but this time it really is different.
Yes, we need global regulation but, you know, it’s complicated so only the kind of regulation we advise is going to work.
Look, if we don’t do this someone else will. So, you should get out of our way as much as you possibly can. We are the good guys and if you slow us down the bad guys will win.

I feel confident about this prediction not because I wish to claim the office of prophet but because just like Big Tobacco and Big Oil, Big Tech’s lobbyists will redeploy a tried and tested playbook. And here are the three plays at the heart of it.

Tech exceptionalism. (We deserve to be treated differently under the law.)

Regulatory capture. (We got lucky, last time, with the distinction between platform and publisher that permitted self-regulation of social media, the harvesting of personal data and manipulative design for attention, but the costs of defeating Uber in California and now defending rearguard anti-trust lawsuits means lesson learned, we need to go straight for regulatory capture this time).

Tech determinism. (If we don’t do it, someone else will. We are the Oppenheimers here.)

Speaking of Pandora

What should we make of these claims? We need to start by exploring an underlying premise. One that typically goes like this “AI is calling into question what it means to be human”.

This premise has become common currency, but it is flawed because it is too totalising. AI emphatically is calling into question a culturally dominant version of human anthropology – one specific ‘science of humanity’. But not all anthropologies. Not the Christian anthropology.

A further, unspoken, premise driving this claim becomes clearer when we survey the range of responses to the question “what does the advent of what the government is now calling ‘frontier’ AI portend?”

Either, it means we have finally prized open Pandora’s box; the last thing humans will ever create. AI is our Darwinian evolutionary heir, soon to make us homo sapiens redundant, extinct, even. Which could happen in two very different ways. For some, AI is the vehicle to a new post-human eternal life of ease, roaming the farthest reaches of the universe in disembodied digital repose. To others, AI is now on the very cusp of becoming abruptly and infinitely cleverer than us. To yet others, we are too stupid to avoid blowing ourselves up on the way to inventing so-called artificial general intelligence.

Cue main global summit speaking points…

Or,

AI is just a branch of computing.

Which of these two starkly contrasting options you choose will depend on your underlying beliefs about ‘what it means to be human’.

Universal machines and meat machines

Then again, what does it mean to be artificially intelligent? Standard histories of AI always point to two seminal events. First, Alan Turing published a paper in the 1930s in which he proposed a device called a Universal Turing Machine.

Turing’s genius was to see a way of writing a type of programme to control a computer’s underlying binary on/off in ways that could vary depending on the task required and yet perform any task a computer can do. The reason your computer is not just a calculator but an excel spreadsheet and a word processor and a video player as well is because it is a kind of Universal Turing Machine. A UTM can compute anything that can be computed. If it has the right programme.

The second major event in AI folklore was a conference at Dartmouth College in the USA in the early 1950s bringing together the so-called ‘godfathers of AI’.

This conference set the philosophical and practical approaches from which AI has developed ever since. That this happened in America is important because of the strong link between universities, government, the defence and intelligence industry and the Big Tech Unicorns that have emerged from Silicon Valley to conquer the world. That link is anthropological; it is political, social, and economic and not just technical.

Let’s take this underlying question of ‘what does it mean to be human?’ and recast it in a binary form as befits a computational approach; ‘Is a human being a machine or is a human being an organism?’

Cognitive scientist Daniel Dennett was recently interviewed in the New York Times. For Dennett our minds and bodies are a “consortia of tiny robots”. Dennett is an evolutionary biologist and a powerful voice for a particular form of atheism and its answer to the question ‘what does it mean to be human?’ Dennett regards consciousness as ephemera, a by-product of brain activity. Another godfather of AI, Marvin Minsky, famously described human beings as ‘meat machines.’

By contrast, Joseph Weizenbaum was also one of the early computer pioneers in the 1960s and 1970s. Weizenbaum created one of the first ever chatbots, ELIZA– and was utterly horrified at the results. His test subjects could not stop treating ELIZA as a real person. At one point his own secretary sat down at the terminal to speak to ELIZA and then turned to him and asked him to leave the room so she could have some privacy. Weizenbaum spent the latter part of his professional life arguing passionately that there are things we ought not to get computers to do even if they can, in principle, perform them in a humanlike manner. To Joseph Weizenbaum computers were/are fundamentally different to human beings in ways that matter ineluctably, anthropologically. And it certainly seems as if the full dimensionality of human being cannot yet be reduced to binary on/off internal states without jettisoning free will, consciousness and transcendence. Prominent voices like Dennett and Yuval Noah Harari are willing to take this intellectual step. Their computer says ‘no’. By their own logic it could not say otherwise. In which case here’s a third way of asking that seemingly urgent and pressing question about human being;

“Are we just warm, wet, computers?”

The immanent frame

A way to make sense of this, for many people, influential and intuitively attractive meaning of human being is to understand how the notion of artificial intelligence fits a particular worldview that has come to dominate recent decades and, indeed, centuries.

In 2007 Charles Taylor wrote A Secular Age. In it he tracks the changing view of what it means to be human as the Western Enlightenment unfolds. Taylor detects a series of what he calls ‘subtraction stories’ that gradually explain away the central human experience of transcendence until society is left with what he calls an ‘immanent frame’. Now we are individual ‘buffered selves’ insulated by rational mind so that belief in any transcendent reality, let alone God, is just one possible choice among personal belief systems. But, says Taylor, this fracturing of a shared overarching answer to the question ‘What does it mean to be human’ over the past, say, 500 years doesn’t actually answer the question or resolve the ambiguities. Rather, society is now subject to what Taylor calls ‘cross pressures’ and a lack of societal consensus about the answers to the biggest questions of human meaning and purpose.

In this much broader context, it becomes easier to see why as well as how it can be the case that AI is either a profound anthropological threat or just a branch of computing – depending on who you talk to…

The way we describe AI profoundly influences our understanding of it. When Dennett talks about a ‘consortia of tiny robots’ is he speaking univocally or metaphorically? What about when we say that AI “creates”, or “decides” or “discovers” or ‘seeks to maximise its own reward function’. How are we using those words? If we mean words like ‘consortia’ or ‘choose’ and ‘reward’ in as close to the human sense as makes no difference, then of course the difference between us and our machines becomes paper-thin. But are human beings really a kind of UTM? Are UTMs really universal? Are you a warm wet computational meat-machine?

Or is AI just the latest and greatest subtraction story?

To say AI is just a branch of computing is not to say the harms of outsourcing key features of human being to machines are trivial. Quite the opposite.

How then should we judge prophecies about AI emanating from this global conference or in the weeks and months to follow? I suggest two responses. The first follows from my view of AI, the other from my view of human being.

Our view of current AI should be clear eyed, albeit open to revision should future development(s) so dictate. I am firmly on the side of those who, without foreclosing the possibility, see no philosophical breakthrough in the current crop of tools and techniques. These are murky philosophical waters but clocks don’t really have human hands now do they, and a collapsed metaphor can’t validate itself however endemic the reference to the computational theory of mind has become.

Google’s large language model, Bard, for example, has no sense of what time it is where ‘he’ is, let alone can freely choose to love you or not, or to forgive you if you hurl an insult at ‘him’. But all kinds of anthropological harms already flow from the unconscious consequences of re-tuning human being according to the methodological image of our machines. To say AI is just a branch of computing is not to say the harms of outsourcing key features of human being to machines are trivial. Quite the opposite.

Which brings me to the second response. When you hear the now stock claim that AI is calling into question what it means to be human, don’t buy it. Push back. Point out the totalising lack of nuance. The latest tools and techniques of AI are calling a culturally regnant but philosophically reductive anthropology into question. That much is definitely true. But that is all.

And it is important to resist this totalising claim because if we don’t, an increasingly common and urgent debate about the fullness of human being and the limitations of UTMs will struggle from the start. One of the biggest mistakes I think public theology made twenty-some years ago was to cede a normative use of language that distinguished between people of faith and people of no faith. There is no such thing as being human without faith commitments of one kind or another. If you have any doubt about this, I commend No One Sees God: The Dark Night of Atheists and Believers by Michael Novak. But the problem with accepting the false distinction between ‘having faith’ and having ‘no faith’ is that it has allowed the Dennetts and Hararis of this world to insist that atheism is on a stronger philosophical footing than theism. After which all subsequent debate had, first, to establish the legitimacy of faith per se before getting to the particular truth claims in, say, Christianity.

What it means to be human

I see a potentially similar misstep for anthropology – the science of human being – in this new and contemporary context of AI. Everywhere at the moment, and I mean but everywhere, a totalising claim is being declared ever more loudly and urgently: that the tools and techniques of AI are calling into question the very essence of human identity. The risk in ceding this claim is that we get stuck in an arid debate about content instead of significance; a debate about ‘what it means to be human’ instead of a debate about ‘what it means to be human.’

This global AI summit’s proclamations and prophecies need to be placed in the right philosophical register, because to be human in an age of AI still means the same thing it has for millennia.

Universals like wonder, love, justice, the need for mutually meaningful relationships and a sense of purpose, and so too personal idiosyncrasies like a soft spot for the moose are central features of what it means to be this human being.

Suchlike are the essential ingredients of the ‘me’ that is reading this article. They are not tertiary. Perhaps they can be computationally mimicked but that does not mean they are, in themselves, ephemeral or mere artifice. In which case their superficial mimicry carries substantial risks, just as Joseph Weizenbaum prophesied in Computer Power and Human Reason in the 1970s.

Of course, you may disagree. You may even disagree in good faith, for there are no knockdown arguments in metaphysics. And in my worldview, you are free to do so. But fair warning. If the human-determinism of Dennett or the latest prophecies of Harari are right, no credit follows. You, and they, are right only because by arbitrary alignment of the metaphysical stars, you, and they, have never been free to be wrong. It was all decided long ago. No need for prophecies. We are all just UTMs with the soul of a marionette

But when you hear the three Global summit prophecies I predicted earlier, consider these three alternatives;

This time is not different, it is not true that AI is calling into question all anthropologies. AI is (only) calling into question a false and reductive Enlightenment prophecy about ‘what it means to be human.’

The perennial systematic and doctrinal anthropology of Christianity understands human being as free-willed, conscious, unified body soul and spirit. It offers credible answers to the urgent questions and cross-pressures society is now wrestling with. It also offers an ethical framework for answering the question ‘what ought computers to be used for and what ought computers not to be used for – even if they appear able to be used for anything and everything?

This Christian philosophical perspective on the twin underlying metaphysical questions of human being and purpose are not being called into question, either at this global summit or by any developments in AI today or the foreseeable future. They can, however, increasingly be called into service to answer those questions – at least for those with ears to hear.

Essay

AI - Artificial Intelligence

Culture

19 March 2024

9 min read

Here’s why AI needs a theology of tech

As AI takes on tasks once exclusively human, we start to doubt ourselves. We need to set the balance right.

Oliver Dürr

Oliver Dürr is a theologian who explores the impact of technology on humanity and the contours of a hopeful vision for the future. He is an author, speaker, podcaster and features in several documentary films.

In the style of an icon of the Council of Nicea, theologians look on as a cyborg and humanoid AI shake hands

The Council of Nicaeai, reimagined.

Nick Jones/Midjourney.ai

AI is all the rage these days. Researchers branching into natural and engineering sciences are thriving, and novel applications enter the market every week. Pop culture explores various utopian and dystopian future visions. A flood of academic papers, journalistic commentary and essays, fills out the picture.

Algorithms are at the basis of most activities in the digital world. AI-based systems work at the interface with the analogue world, controlling self-driving cars and robots. They are transforming medical practices - predicting, preventing, diagnosing and supporting therapy. They even support decision-making in social welfare and jurisprudence. In the business sector, they are used to recruit, sell, produce and ship. Much of our infrastructure today crucially depends on algorithms. But while they foster science, research, and innovation, they also enable abuse, targeted surveillance, regulation of access to information, and even active forms of behavioural manipulation.

The remarkable and seemingly intellectual achievements of AI applications uniquely confront us with our self-understanding as humans: What is there still categorically that distinguishes us from the machines we build?

In all these areas, AI takes on tasks and functions that were once exclusive to humans. For many, the comparison and competition between humans and (algorithmically driven) machines are obvious. As these lines are written, various applications are flooding the market, characterized by their ‘generative' nature (generative AI). These algorithms, such OpenAI’s the GPT series, go further than anyone expected. Just a few years ago, it was hard to foresee that mindless computational programs could autonomously generate texts that appear meaningful, helpful, and in many ways even ‘human’ to a human conversation partner. Whether those innovations will have positive or negative consequences is still difficult to assess at this point.

For decades, research has aimed to digitally model human capabilities - our perception, thinking, judging and action - and allow these models to operate autonomously, independent of us. The most successful applications are based on so-called deep learning, a variant of AI that works with neural networks loosely inspired by the functioning of the brain. Technically, these are multilayered networks of simple computational units that collectively encode a potentially highly complex mathematical function.

You don’t need to understand the details to realize that, fundamentally, these are simple calculations but cleverly interconnected. Thus, deep learning algorithms can identify complex patterns in massive datasets and make predictions. Despite the apparent complexity, no magic is involved here; it is simply applied mathematics.

Moreover, this architecture requires no ‘mental' qualities except on the part of those who design these programs and those who interpret their outputs. Nevertheless, the achievements of generative AI are astonishing. What makes them intriguing is the fact that their outputs can appear clever and creative – at least if you buy into the rhetoric. Through statistical exploration, processing, and recombination of vast amounts of training data, these systems generate entirely new texts, images and film that humans can interpret meaningfully.

The remarkable and seemingly intellectual achievements of AI applications uniquely confront us with our self-understanding as humans: Is there still something categorically that distinguishes us from the machines we build? This question arises in the moral vacuum of current anthropology.

Strictly speaking, only embodied, living and vulnerable humans really have problems that they solve or goals they want to achieve... Computers do not have problems, only unproblematic states they are in.

The rise of AI comes at a time when we are doubting ourselves. We question our place in the universe, our evolutionary genesis, our psychological depths, and the concrete harm we cause to other humans, animals, and nature as a whole. At the same time, the boundaries between humans and animals and those between humans and machines appear increasingly fuzzy.

Is the human mind nothing more than the sum of information processing patterns comparable to similar processes in other living beings and in machine algorithms? Enthusiastic contemporaries believe our current AI systems are already worthy of being called ‘conscious’ or even ‘personal beings.’ Traditionally, these would have been attributed to humans exclusively (and in some cases also to higher animals). Our social, political, and legal order, as well as our ethics, are fundamentally based on such distinctions.

Nevertheless, companies such as OpenAI see in their product GPT-4 the spark of ‘artificial general intelligence,’ a form of intelligence comparable to or even surpassing humans. Of course, such statements are part of an elaborate marketing strategy. This tradition dates to John McCarthy, who coined the term “AI” and deliberately chose this over other, more appropriate, descriptions like “complex information processing” primarily because it sounded more fundable.

Such pragmatic reasons ultimately lead to an imprecise use of ambiguous terms, such as ‘intelligence.’ If both humans and machines are indiscriminately called ‘intelligent,’ this generates confusion. Whether algorithms can sensibly be called ‘intelligent’ depends on whether this term refers to the ability to perform simple calculations, process data, the more abstract ability to solve problems, or even the insightful understanding (in the sense of Latin intellectus) that we typically attribute only to the embodied reason of humans.

However, this nuanced view of ‘intelligence’ was given up under the auspices of the quest for an objectively scientific understanding of the subject. New approaches deliberately exclude the question of what intelligence is and limit themselves to precisely describing how these processes operate and function.

Current deep learning algorithms have become so intricate and complex that we can’t always understand how they arrive at their results. These algorithms are transparent but not in how they reach a specific conclusion; hence, they are also referred to as black-box algorithms. Some strands in the cognitive sciences understand the human mind as a kind of software running on the hardware of the body. If that were the case, the mind could be explained through the description of brain states, just like the software on our computers.

However, these paradigms are questionable. They cannot explain what it feels like to be a conscious person, to desire things, be abhorred by other things and to understand when something is meaningful and significant. They have no grasp on human freedom and the weight of responsibility that comes with leading a life. All of these human capacities require, among other things, an understanding of the world, that cannot be fully captured in words and that cannot be framed as a mathematical function.

There are academic studies exploring the conception of embodied, embedded, enactive, and extended cognition, which offer a more promising direction. Such approaches explore the role of the body and the environment for intelligence and cognitive performance, incorporating insights from philosophy, psychology, biology, and robotics. These approaches think about the role our body as a living organism plays in our capacity to experience, think and live with others. AI has no need for such a living body. This is a categorical difference between human cognition and AI applications – and it is currently not foreseeable that those could be levelled (at least not with current AI architectures). Therefore, in the strictest sense, we cannot really call our algorithms ‘intelligent' unless we explicitly think of this as a metaphor. AI can only be called 'intelligent' metaphorically because these applications do not 'understand' the texts they generate, and those results do not mean anything to them. Their results are not based on genuine insight or purposes for the world in which you and I live. Rather they are generated purely based on statistical probabilities and data-based predictions. At most, they operate with the human intelligence that is buried in the underlying training data (which human beings have generated).

However, all of this generated material has meaning and validity only for embodied humans. Strictly speaking, only embodied, living and vulnerable humans really have problems that they solve or goals they want to achieve (with, for example, the help of data-based algorithms). Computers do not have problems, only unproblematic states they are in. Therefore, algorithms appear 'intelligent' only in contexts where we solve problems through them.

When we do something with technology, technology always also does something to us.

AI does not possess intrinsic intelligence and simulates it only due to human causation. Therefore, it would be more appropriate to speak of ‘extended intelligence': algorithms are not intelligent in themselves, but within the framework of human-machine systems, they represent an extension of human intelligence. Or even better would be to go back behind McCarthy and talk about 'complex information processing.’

Certainly, such a view is still controversial today. There are many philosophical, economic, and socio-political incentives to attribute human qualities to algorithms and, at the same time, to view humans as nothing more than biological computers. Such a view already shapes the design of our digital future in many places. Putting it bluntly, calling technology ‘intelligent’ makes money.

What would an alternative, more holistic view of the future look like that took the makeup of humanity seriously?

A theology of technology (Techniktheologie) tackles this question, ultimately placing it in the horizon of belief in God. However, it begins by asking how technology can be integrated into our lives in such a way that it empowers us to do what we truly want and what makes life better. Such an approach is neither for or against technology but rather sober and critical in the analytical sense. Answering those questions requires a realistic understanding of humans, technology, and their various entanglements, as well as the agreement of plural societies on the goals and values that make a good life.

When we do something with technology, technology always also does something to us. Technology is formative, meaning it changes our experience, perception, imagination, and thus also our self-image and the future we can envision. AI is one of the best examples of this: designing AI is designing how people can interact with a system, and that means designing how they will have to adapt to it. Humans and technology cannot be truly isolated from each other. Technology is simply part of the human way of life.

And yet, we also need to distinguish humans from technology despite all the entanglements: humans are embodied, rational, free, and endowed with incomparable dignity as images of God, capable of sharing values and articulating goals on the basis of a common (human) way of life. Even the most sophisticated deep learning applications are none of these. Only we humans live in a world where responsibility, sin, brokenness, and redemption matter. Therefore it is up to us to agree on how we want to shape the technologized future and what values should guide us on this path.

Here is what theology can offer the development of technology. Theology addresses the question of the possible integration of technology into the horizon of a good life. Any realistic answer to this question must combine an enlightened understanding of technology with a sober view of humanity – seeing both human creative potential and their sinfulness and brokenness. Only through and with humans will our AI innovations genuinely serve the common good and, thus, a better future for all.

Find out more about this topic: Assessing deep learning: a work program for the humanities in the age of artificial intelligence