What happened when computers learned to read

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cComputers love to read. And it’s not just fiction before you go to bed. They read voraciously: all literature, all the time – novels, encyclopedias, scientific articles, private messages, advertisements, love letters, news stories, hate speech and crime reports – everything written and sent, no matter how insignificant.

This swallowed print contains the messiness of human wisdom and emotion – the information and disinformation, fact and metaphor. While we built railroads, fought wars, and bought shoes online, the machine child went to school.

Literary computers now scribble everywhere in the background, powering search engines, recommendation systems, and customer service chatbots. They flag offensive content on social networks and remove spam from our inbox. At the hospital, they help convert patient-doctor conversations into billing codes for insurance. Sometimes they alert law enforcement to possible terrorist plots and predict (poorly) the threat of violence on social media. Legal professionals use them to hide or discover evidence of corporate fraud. Students write their next school paper using a smart word processor that can not only complete sentences but generate entire essays on any topic.

In the industrial age, automation came to the shoemaker and the factory worker. Today it has come for the writer, the professor, the doctor and the lawyer. All human activities now pass through a computer pipeline – even the sanitation worker converts wastewater into data. Like it or not, we are all subject to automation. To survive intact, we must also learn to become part software engineers and part, well, whatever you do is great!

Read more: AI and the rise of mediocrity

If any of the above comes as a surprise to you, I think my job is mostly done. Curiosity aroused, you will now start noticing literary robots everywhere and join me in pondering their origins. Those who aren’t surprised may believe (wrongly!) that these siliconites only recently learned to chat, somewhere in the field of computer science or software engineering. I’m here to tell you that machines have been getting smarter in this way for centuries, long before computers, and have made advances in much more arcane fields such as rhetoric, linguistics, hermeneutics, literary theory, semiotics, and philology.

To be able to hear them speak – to be able to read and understand a vast library of machine texts – I want to introduce some essential ideas that underlie the ordinary magic of literary computers. Hidden deep within the circuits of everyday appliances – yes, even ‘smart’ light bulbs and refrigerators – we will find little poems that have yet to name their genre. In this sense, these computers are not only full of instrumental capacity (to keep food cold or to provide light), but also of potential for creativity and collaboration.

It’s tempting to ask existential questions about the nature of artificially intelligent things: “How smart are they?” Do they really “think” or “understand” our language?” ‘Will they ever – have they – become conscious?’

Such questions are impossible to answer (in the way they are asked) because the categories of consciousness arise from human experience. To understand alien life forms, we have to think in alien ways. And instead of debating definitions (“Are they smart or not?”), we can start by describing the ways in which the meaning of intelligence continues to evolve.

Not long ago, one way to appear smart was to memorize some obscure facts to become a walking encyclopedia. Today, that way of knowing seems like a waste of precious mental space. Large online databases make effective search habits more important than rote memorization. Intelligence changes. The puzzle of its essence cannot therefore be composed of sharp, binary attributes, which are always and everywhere classified in the same way: “Can machines think: yes or no?” Instead, we can begin to put the pieces together contextually, at specific times and places, and from the perspective of an evolving, shared capacity: “How do they think?” and “How do we think along with them?” and “How does that change the meaning of thought?”

In answering the “how” questions, we can discover a strange kind of connected history, encompassing the arts and sciences. People have been thinking this way – with and through machines – for centuries, just as they have thought with and through us. The mind, the hand and the tool move at the same time, in harmony. But the way we train minds, hands, or tools treats them almost as entirely separate appendages, located in different buildings, on unrelated fields on a college campus. Such an educational model isolates ends from means and means from ends, disempowering the audience. Instead, I would like to imagine an alternative, more integrated curriculum, offered to poets and engineers alike – ultimately tied to a machine reader as part of a different training corpus.

The next time you pick up a “smart” device, like a book or a phone, pause halfway through use to think about your body position. You watch a video or maybe write an email. The mind moves and requires mental skills such as perception and interpretation. But the hand also moves and animates the body in combination with the technology. Pay attention to the posture of the intellect: the way your head tilts, the movement of individual fingers, pressing buttons or pointing in a certain way. Feel the glass of the screen, the roughness of paper. Scroll and swipe. Such physical rituals—incantations that manifest thoughts, bodies, and tools—produce the artifice of the intellect. The whole thing is ‘it’. And that’s really the point: thinking happens in the mind, by hand, with a tool – and by extension, with the help of others. Thought moves through mental forces, in addition to the physical, the instrumental and the social.

What separates natural from artificial forces in that chain? Does natural intelligence cease when I think something to myself, in silence, alone? How about using a notebook or calling a friend for advice? What about going to the library or consulting an encyclopedia? Or talking to a machine? None of the boundaries seem convincing. Intelligence requires artifice. Webster’s dictionary defines intelligence as the “skillful use of reason.” ‘Artifice’ itself comes from the Latin ‘ars’, meaning skilled work, and “facere,‘ means ‘to make’. In other words, artificial intelligence simply means ‘reason + skill’. There are no hard boundaries here – just synergy between the human mind and its extensions.

What about smart objects? In the morning I stretch and pick up my phone at the same time: to check my calendar, read the news and enjoy the faint glow of compliments, hearts and likes from various social apps. How did I end up in this position? I ask next to Kafka’s beetle The Metamorphosis. Who taught me to move like that?

It wasn’t actually planned. Nor are we actually beetles living in our natural habitat, an ancient forest floor. Our intimate rituals change organically in response to a changing environment. We live in artisanal spaces, containing the designs for a purposeful life. The room says: ‘Eat here, sleep there’; the bed: “Lie on me like this”; the screen: “Hold me like this.” Smart objects continue to change in response to our input. To do that, they need to be able to communicate: include a layer of written instructions. Somewhere in the connection between the tap of my finger and the responsive pixel on the screen, an algorithm has registered my preference for a morning routine. I am the input and output: the tools evolve as they transform me in turn. And so I go back to bed.

Originating from Literary Theory for Robots: How Computers Learned to Write. Copyright 2024 by Dennis Yi Tenen. Used by permission of the publisher, WW Norton & Company, Inc. All rights reserved.

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