Editor’s note: Today’s Radical Reads is guest-authored by novelist, essayist and cultural commentator, Stephen Marche. He is the author of half a dozen books, including The Next Civil War, The Unmade Bed: The Messy Truth About Men and Women in the Twenty-First Century and The Hunger of the Wolf. He has written opinion pieces and essays for The New Yorker, The New York Times, The Atlantic, Esquire, and many others. Today’s commentary is adapted from a feature published in Literary Hub last week (read the full article here).
I’ve been working on AI fiction since 2017 when I wrote my first “algo-story” for Wired. What I’ve come to realize, especially since getting access to Transformer-based text generation, is that what we’re dealing with here is really a new medium. I think creative AI is roughly where film was when the Lumiere brothers showed clips of trains arriving in stations and crowds jumped out of the way. The field is wide open. I think there’s going to be a lot of beauty to come out of this.
A few months ago, I received access to the Cohere API (Cohere is a Radical Ventures portfolio company), which allows for sophisticated, nimble manipulations of Natural Language Processing. Through Cohere, I was able to create algorithms derived from various styles. These included Thomas Browne, Eileen Chang, Dickens, Shakespeare, Chekhov, Hemingway and others, including anthologies of love stories and Chinese nature poetry. I then took those algorithms and had them write sentences and paragraphs for me on selected themes: a marketplace, love at first sight, a life played out after falling in love. The ones I liked I kept. The ones I didn’t I threw out. Then I took the passages those algorithms had provided and input them to Sudowrite, the stochastic writing tool.
To generate Autotuned Love Story I had to develop a separate artistic practice around the technology. The love story below is my attempt to develop an idealized love story out of all the love stories that I have admired. It exists on the line between art and criticism. Autotuned Love Story certainly isn’t mine. I built it but it’s not my love story. It’s the love story of the machines interacting with all the love stories I have loved. I confess that I find it eerie; there is something true and moving in it that I recognize but which I also can’t place.
AUTOTUNED LOVE STORY
[This story was generated by means of natural language processing, using the Cohere AI and Sudowrite]
The rain in the market smelled like rusting metal and wet stones. The stallholders had no real need to sell nor did they care much for their customers. There was a cookery demonstration. There was a magician. There was a video games stall. There was a beauty parlour. The rain was like a mist at first, fine and barely noticeable, but not long after the streets were flowing with a torrent of mud and water.
Among huddles of people, they met in a stall that sold umbrellas. The eyes of one were large and green, soft and milky. The other’s eyes were like iced coffee.
Shyness came upon them at once. Shyness and fear. A butcher’s boy, with a beautiful nose, stood beside a post, making grimaces at a plan that was chalked out on the top of it. A ragged little boy, barefooted, and with his face smeared with blood, from having just grazed his nose against the corner of a post, began playing at marbles with other boys of his own size. Their smiles were interminable, wavering and forgetful, and it seemed as though they could not control their lips, that they smiled against their will while they thought of something else.
The rain became like a dirty great mop being wrung out above their heads. The market became more uneasy, and gave place to a sea of noises that on both sides added to the general clamour. The crowd began to press in on them, to snatch at their coats, to groan, to criticize and to complain of cold and hunger, of want of clean clothes, of lack of decent shelter. The rain was unremitting—just like the flow of people, the flow of traffic, the flow of tired animals. The crowd erupted and all at once it seemed that there were too many people.
When the crowd closed up again, the two were separated from one another. The rain died down and the market was now very different. They looked for each other like lost children in a train station. It was a different kind of a market, darker, older, dingier, more chaotic. The pavement was covered with mud and mire and straw and dung.
They met by accident, which is only a way of saying that we have not looked for something before it comes forward, that they were both in the world and the world is small.
They never met again, or maybe they did.
Maybe, at first, they had the same delight in touching, in meeting, in forming, in blurring, in drawing out. They had secrets, and they shared those secrets. As one’s hands rolled over the other, they lay as still as fish. It seemed to both of them that they could not live in the old way; they could not go on living as though there were nothing new in their lives. They had to settle down together somewhere, to live for themselves, alone, to have their own home, where they would be their own masters. They went abroad, changed their lives. One was a manager of a railway branch line. The other became a teacher in a school. And the large study in which they spent their evenings was so full of pictures and flowers that it was difficult to move about without upsetting something. Pictures of all sorts, landscapes in water-colour, engravings after the old masters, and the albums filled with the photographs of relatives, friends, and children, were scattered everywhere about the bookcases, on the tables, on the chairs. Love is like money: the kind you have and do not want to lose, the kind you lose and treasure. The thought of death, which had moved them so profoundly, no longer caused in either the former fear and remorse, a sound that lost its echo in the endless, sad retreat, a phantom of caresses down hallways empty and forsaken.
Maybe they lived that life. Maybe they didn’t. But in the market, among the detritus, the splintered edges, they had once found each other, and found each other and lost each other again. They had said only that, yes, they were alone.
The rain had smelled like sodden horses and rusting metal and wet stones.
5 Noteworthy AI and Deep Tech Articles: week of July 3, 2022
1) AI makes strides in virtual worlds more like our own (Quanta Magazine)
Fei-Fei Li, Stanford professor and computer vision pioneer, has produced a standardized set of virtual activities to help evaluate AI’s progress. This new endeavour builds on Fe-Fei’s legacy of creating benchmarks to better measure advances in AI. Over fifteen years ago, Fei–Fei created ImageNet – “a dataset that would change the history of AI.” The ImageNet data set included millions of labelled images that could train sophisticated machine-learning models to recognize objects in a picture. In 2012 a team led by Geoffrey Hinton used the ImageNet dataset to show that machines could surpass human recognition abilities. It was a watershed moment in the field. Fei-Fei Li is now working on other “North Star” projects that could give AI another push toward true intelligence. Currently, she is researching what is known as embodied intelligence which includes any agent that can probe and change its environment.
A deep learning tool that identifies distress calls made by chickens could improve the welfare of the animals housed on commercial farms. While some farms use human observers to gauge chickens’ distress levels, the process is difficult if not impossible for large commercial flocks, according to researchers. City University of Hong Kong researchers trained their AI tool on manually-labelled recordings of chickens housed in cages at a Chinese poultry producer. AI is increasingly being applied to livestock management to monitor farm animals’ health and welfare automatically.
3) AI is using fake data to learn to be less discriminatory (Bloomberg)
More AI developers are using synthetic data to train AI systems and cut down on potential bias. This allows training sets that rely on images of people to have more diversity. Synthetic data enables practitioners to digitally generate the data that they need, on-demand, in whatever volume they require, tailored to their precise specifications. Radical Ventures Partner Rob Toews discussed the technology’s transformative power in a previous edition of Radical Reads: “One of the main reasons that tech giants like Google, Facebook and Amazon have achieved such market dominance in recent years is their unrivalled volumes of customer data. Synthetic data will change this. By democratizing access to data at scale, it will help level the playing field, enabling smaller upstarts to compete with more established players that they otherwise might have had no chance of challenging.”
4) How neurons really work is being elucidated (The Economist – subscription may be required)
Understanding human neural networks directly influences AI research. Over 140 years ago Ramón y Cajal stained neurons with silver nitrate making them visible under the microscope and sparking the scientific imagination. Modern AI can trace its roots to the first artificial neuron model, the perceptron, which was created in 1957. To make the artificial neural networks of today, perceptrons are encoded as software and organized into several interconnected layers (putting the “deep” into “deep learning”). In more recent years, a clearer understanding has emerged of the computing going on inside individual biological neurons. Work started in the early 2000s, and advanced by David Beniaguev in 2021, has led to a focus on more sophisticated artificial neurons that are then connected to networks. Such devices learn faster and at a lower computing cost than perceptrons. The question of how brains apply knowledge from one domain to others remains a mystery, but some believe that a richer understanding of biological neurons will help uncover this as well.
“Every bit of data is changing the outcome of the race.” Racing at speeds of more than 200 miles per hour requires extremely quick decision-making. McLaren is using a software platform designed to stream data in real-time from each race car’s 300 sensors, which monitor details including fuel levels, tire pressure, speed, and battery health. Those sensors generate more than a terabyte and a half of data each race weekend. The AI system’s real-time analysis of vast quantities of telemetry data surfaces safety-critical issues far more quickly than humans.