How AI Ate the World E-Book

Chris Stokel-Walker

• Herausgeber: Canbury
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch
• Veröffentlichungsjahr: 2024

Seitenzahl: 419

Beschreibung

• Popular 'start here' guide to the next big tech wave • People want to know about AI because of its power (AI is a top web search topic) • An accessible, expert guide by the author of the first popular book on TikTok

Leseprobe

How AI Ate the World

Chris Stokel-Walker

How AI Ate the World

A Brief History of Artificial Intelligence – and Its Long Future

Chris Stokel-Walker

Canbury Press

First published by Canbury Press 2024

This edition published 2024

Canbury Press

Kingston upon Thames, Surrey, United Kingdom

www.canburypress.com

Printed and bound in Great Britain

Typeset in Athelas (body), Futura PT (heading)

All rights reserved © 2024 – Chris Stokel-Walker

Chris Stokel-Walker has asserted his right to be identified

as the author of this work in accordance with Section 77

of the Copyright, Designs and Patents Act 1988

This is a work of non-fiction

FSC® helps take care of forests for future generations

ISBN:

Paperback 9781914487323

Ebook 9781914487330

Contents

INTRODUCTION 7

PART 1: ORIGINS 12

1: Magic Mushrooms and a Magic Tool 15

2: The Fathers of AI 21

3: The AI Winter Looms 35

4: Over to Japan 43

5: Shall We Play a Game? 53

6: Ready, Steady, Go! 61

7: The Battle for AI Chips 68

8: The Birth of OpenAI 80

9: Attention Transforms Fortunes 91

10: Enter Microsoft 98

PART 2: IMPACTS 109

11: Making Work Work 111

12: AI Doomers 123

13: A Day in the Life of Midjourney 136

14: Meet the Prompt Engineers 147

15: AI’s Environmental Impact 153

16: AI Art 162

17: Misinformation 177

18: Disinformation 190

19: Profit Before People 208

20: Coded Biases 213

PART 3: ISSUES 226

21: The Human Side of AI 229

22: AI Music, Movies and Books 238

23: Fighting Back through Copyright 248

24: AI and Loneliness 253

25: Moats and Defences 261

26: The AI Field Expands (and Contracts) 266

27: Taming Big Tech 274

28: Openai at War 289

29: Sovereign AI 297

30: Where are We Going? 303

Endnotes 309

Index 317

Chris Stokel-Walker 324

INTRODUCTION

Imagine a world where machines could think, learn, and make decisions just like humans. A world where robots could understand human emotions, drive cars without human intervention, and even create art or compose music. This world, once confined to the realm of science fiction, has now become a tangible reality, thanks to the remarkable journey of artificial intelligence (AI).

The story of AI begins in the 1950s when pioneering researchers such as Alan Turing and John McCarthy laid the groundwork for the field. These visionaries believed that machines could be created to mimic human intelligence, and their groundbreaking ideas sparked a new era of scientific exploration and technological innovation.

In the early years, AI was mostly confined to academic and research settings, with limited practical applications. But as computing power grew exponentially and algorithms became more sophisticated, AI began to evolve rapidly, transforming from a theoretical concept to a practical tool.

One of the most significant breakthroughs came in the 1960s when researchers at Stanford University developed the first AI program that could understand and respond to natural language. This groundbreaking achievement laid the foundation for modern-day language processing technologies, such as virtual assistants like Siri and Alexa, which have become ubiquitous in our daily lives.

As the years passed, AI continued to make strides in diverse domains. In the 1970s, AI systems were developed for medical diagnosis, and in the 1980s, AI was used in industrial automation and robotics. In the 1990s, AI-powered search engines revolutionised the way we access information on the internet.

However, the field of AI has not been without its challenges. During the 1980s and 1990s, the field experienced a period of disillusionment, known as the 'AI winter', as progress stalled, and funding declined. But AI, like a phoenix rising from the ashes, made a remarkable comeback in the 21st Century.

In recent years, we have witnessed unprecedented advancements in AI. Machine learning, a subset of AI, has enabled computers to learn from data without explicit programming, leading to breakthroughs in image recognition, natural language processing, and recommendation systems. Deep learning, a form of machine learning, has pushed the boundaries of AI even further, enabling machines to recognise patterns and make decisions at a level previously thought impossible.

The impact of AI has extended far beyond the realm of academia and research. Today, AI is being used in various industries, including healthcare, finance, transportation, and entertainment, to improve efficiency, enhance decision-making, and create new business models.

Despite the remarkable progress, AI is not without its controversies. Ethical concerns, such as bias in AI algorithms, privacy issues, and the impact of AI on the job market, have sparked debates and discussions. The rise of powerful AI technologies has also raised concerns about transparency, accountability, and the potential for misuse.

As we stand at the cusp of a new era of AI, it is crucial to reflect on the incredible journey of AI from science fiction to scientific fact. What was once a mere idea in the minds of visionary scientists has now become a transformative force that is reshaping our world. In the following chapters, we will delve deeper into the history, challenges, and implications of AI, exploring its evolution, impact on industries, ethical concerns, and the potential risks and dangers. Through this journey, we will strive to uncover the true potential of AI and its implications for humanity, and offer insights and recommendations for policymakers, scientists, and society at large to navigate the uncharted territory of AI responsibly and shape a future where AI benefits all of humanity.

…

Not bad, huh? Those 593 words took only two minutes – and the power of artificial intelligence – to produce. It’s an indication of how far AI has advanced in the last 18 months that ChatGPT, the 'large language model' developed by OpenAI in the United States, could produce something so lucid in a matter of moments.

I gave the AI model six bulletpoints summarising the overview in my contract for this book and asked for a detailed chapter structure. It produced a reasonable outline for eight chapters, though it was quite vague. The first chapter on The Evolution of Artificial Intelligence introduced the concept of AI and its science-fiction origins, traced its history from the 1950s to today, and highlighted its key developments from theoretical concepts to practical reality.

So I typed four words into ChatGPT and hoped for the best: ‘Please write chapter one.’ What you see above is what it produced. Without editing. Without further prompting.

This book will not be as clever again – I promise. Nor will it be the product of AI. You didn’t pay for this book to learn what AI thinks about itself (or rather, its understanding of our species’ recorded collective thoughts on the subject), but for the narrative craft, investigative journalism, and deep thinking that comes from a human brain. But it’s important to know what we’re dealing with, given the way AI is changing the way we think and work. And it’s interesting to find out what AI thinks about its own story.

Its findings may be imprecise, vague and full of generalisations, but at a broad sweep, they are right. We are on the cusp of a world ‘where machines can think, learn, and make decisions just like humans... understand human emotions, drive cars without human intervention, and even create art or compose music.’

Indeed, one early cover idea for this book, a cartoonishly anthropomorphised pink and blue planet devouring the world’s knowledge, was the product of Microsoft Bing’s AI image generator. I generated 14 others in a variety of different styles in 15 minutes. (The cover you see was designed by a human. Why that’s important, you’ll learn in subsequent chapters.)

It's also true to say ‘we have witnessed unprecedented advancements in AI’ in the last few years.

Since the release of ChatGPT in November 2022, AI has reached even dizzier heights. It is mutating and spreading like a virulent pandemic.

Some companies, sectors and industries already lie mortally wounded in the wake of its relentless onslaught. It’s an attack that is also changing humanity: AI boosters or AI pessimists will tell you we’re being either supercharged or suborned by its power.

Like a pandemic, there are tough questions about what we should do. Should we just let AI explode and hope for the best, or try to contain it? As the world’s richest companies develop ever more extraordinary AI systems, is it too late to even hold this discussion?

We’ll answer all those questions and more in the coming chapters. But first let me take you on a detour. Unlike computers, humans can be impulsive.

PART 1: ORIGINS

1

MAGIC MUSHROOMS AND A MAGIC TOOL

Pablo Xavier had, by his own admission, taken a few too many magic mushrooms on the afternoon of 24th March 2023. (Would an AI ever open a chapter like that?)

The 31-year-old construction worker from the Chicago area used the psychedelic drugs to foster his creativity and pass the time. After the untimely loss of his brother, they also helped ease his concerns and Pablo (who asked me not to use his last name so he couldn’t be identified) was looking for a way to remember him.

He encountered Midjourney, an AI-powered image generator made available to the public in 2022, and thought it could help. Like all generative AI tools, Midjourney is trained on a vast volume of data. In Midjourney’s case, 100 million images ripped from the internet without permission, which its founder David Holz admitted when he told a Forbes interviewer: ‘There isn’t really a way to get a hundred million images and know where they’re coming from.’

An AI image generator learns by poring over each image it’s presented with, using computer vision to work out what it’s seeing. Sometimes, the identity of the object is unclear. A cluster of red and white pixels next to each other could be a beach ball, a barber’s pole, or a candy cane. When the AI model examines the surrounding area, however, it can make better guesses. It’s often helped by human labelling: someone coming in, drawing a metaphorical circle around a beach ball and saying: ‘This is a beach ball.’ Information about its shape, colour, size and usual position in relation to other objects is then stored. Over time, the AI gathers more contextual clues from multiple images of beach balls, allowing it to create a composite understanding of what a beach ball looks like.

Train an AI model enough times on enough different objects and the end result is a tool like Midjourney. A user enters a text prompt – for instance, 'Draw me a beach ball on the moon' – and the software duly creates a picture.

‘I needed some type of outlet,’ Pablo told me. He thought Midjourney could be that outlet, helping him turn a real image of his brother into a cartoon character, enabling him to live on long past his untimely death. He ordered up a cartoon character that was intentionally larger than life, and he sent his virtual brother on heroic adventures. ‘It pretty much just all started with that, just dealing with grief and making images of my past brother,’ he said. ‘I fell in love with it after that.’ Soon, Pablo was using it for more than comic book-style depictions of his brother. He started using Midjourney to make art.

‘I try to do funny stuff or trippy art – psychedelic stuff,’ he told me. Then the ’shrooms hit. ‘It just dawned on me: I should do the Pope,’ he recalls. ‘Then it was just coming like water. “The Pope in Balenciaga puffy coat, Moncler [another luxury fashion brand], walking the streets of Rome, Paris, stuff like that.”’

Pablo put the prompts into Midjourney at 1.48pm that Friday in late March 2023. Three images came out the other end. The construction worker thought they were perfect. He, or rather Midjourney, had managed to create eerily accurate depictions of Pope Francis wearing an oversized white puffy parka jacket. The images were curious, occupying the ‘uncanny valley’ of realism. As the Pope is a man of a certain age living in Italy, with enough expendable income for fancy clothes, his fashion choices seemed to be at least plausible.Pablo shared his creations to a Facebook group called AI Art Universe, and in the r/midjourney subreddit, where generative AI enthusiasts share their handiwork. From there, it was downloaded, shorn of its all-important context, and disseminated widely on Twitter where it fooled lots of people.

When Pablo woke up from a psychedelic stupor, his girlfriend asked him whether he had seen his Facebook feed. The internet had exploded with comments about the Pope. The Hollywood celebrity Chrissy Teigen tweeted: ‘I thought the Pope’s puffer jacket was real and didn’t give it a second thought. No way am I surviving the future of technology.’

Pablo’s two-word reaction? ‘Holy crap.’ ‘I was just blown away,’ he said. ‘I didn’t want it to blow up like that.’ Nor was Pablo prepared for the backlash from people feeling he had set out to deliberately deceive – or worse, to offend the world’s 1.3 billion Catholics. Pablo grew up Catholic but no longer feels part of the religion. He was worried what his family might think, and sent the image to his surviving brother, a Catholic, who luckily thought it was funny. He also thought it was real. ‘I had to tell him I made it through Midjourney,’ Pablo admitted.

When I spoke to him amid the media storm, Pablo seemed wholly unprepared to face the consequences of his hallucinogenic behaviour. He batted away requests, brokered through me, to speak to Google, the BBC, Canada’s public broadcaster, TIME, Popular Science, El Mundo and the New York Times. He was too nervous he might say the wrong thing and raise hackles further.

Pablo couldn’t quite fathom that people believed his image was real. When uploading it, he thought the image was clearly generated by AI. It hadn’t crossed his mind that it would be severed from its context and spread widely, nor that people would be so credulous.

Just as Frankenstein had his moment of reckoning when confronted by the havoc of his monster’s actions, so did Pablo. ‘I didn’t mean no ill will,’ he told me. ‘I just thought it was funny to see the Pope in a funny jacket.’ He was disheartened that his creation had been coopted: he had already seen his images used to illustrate articles attacking the Catholic church’s spending.

Bizarrely, in a collision of the digital and real worlds, the AI-created images were often run alongside a real-life photograph, taken in 2017, of Pope Francis blessing a $200,000 white Lamborghini with two golden go-faster stripes on its bonnet.

‘I feel like shit,’ Pablo told me. ‘I didn’t give birth [to it] because I’m pretty sure it was going to happen, but I made it possible for people to make fake news using Midjourney.’

The images, now known as the Balenciaga Pope or the ‘Pope in a puffer,’ were the second deepfakes to hoodwink the internet in a single week. Ahead of the rumoured arrest of former US President Donald Trump, Eliot Higgins, best known for being the founder of investigative journalism website Bellingcat, had used Midjourney to create lifelike ‘photographs’ of what Trump’s arrest and incarceration could look like. When Pablo Xavier’s images of the pontiff hit a similar nerve to Higgins’, he thought twice about sharing his other Midjourney-generated images of the pontiff throwing up gang signs, but eventually he did so on his Instagram, where they were seen by far fewer people. He enforced a new rule for any of his subsequent Midjourney creations: no public figures. He had no problem with people using the image generator to create works in the style of artists like Vincent Van Gogh, he told me – though later we’ll discover that the art world does not share that view – but he felt depicting living people was a line he shouldn’t cross again.

The labourer’s dalliance with generative AI and his work’s subsequent explosion into the public consciousness highlights two of this book’s key pillars.

Number one: by his own admission, Pablo Xavier didn’t think about the consequences of how he was using AI. He hadn’t considered the image he created to entertain his mushroom-influenced self would take on a half-life of its own and spread around the internet before he could say ‘computer-generated fraud’.

Secondly: when confronted by the vastness of what AI can do unwittingly, many AI content creators have second thoughts. In later chapters, we’ll encounter hip-hop fans who congregate on chat apps like Discord and tinker with tools like the innocuously named So-Vits-SVC to create pitch-perfect recreations of musicians like Jay-Z, Drake and Kanye West performing songs they’ve never previously sung. Those people, like Pablo, have nuanced views on whether what they do should be regulated. A surprising number come to the conclusion, when they see how easy it is, that AI should be cracked down on.

As for Pablo’s papal pics, the Pope himself has not publicly addressed the fakes, but he did take the time, when announcing that the 2024 World Peace Day theme would be AI, to ask for an ‘open dialogue’ about the meaning and implementation of the new technology. The Holy See’s press office said in a statement that there was an ‘urgent need to orient the concept and use of artificial intelligence in a responsible way, so that it may be at the service of humanity and the protection of our common home’.

Amid the fear AI will stifle creativity and end all human individuality, we often overlook that two agents are involved in any AI-generated creation: the technology and the human. The former can’t exist without the latter. At least not yet, some argue.

We’ve plenty of time to get onto that. For now, let’s wind back the clock to before World War II, before racing forward to the summer of 1956, and the birth of AI– the moment that bequeathed the technology that enabled Pablo to dress up the Pope in Balenciaga.

2

THE FATHERS OF AI

Today, a number of individuals currently debating the potential and pitfalls of our generative AI revolution have been given the mantle of 'godfathers of AI' by the media. But expand the timeline back to the 1940s and 1950s, and AI had two godfathers who predate those currently laying claim to the title: one in the UK and the other in the USA. Both were developing their thinking just after World War II. And in a way, both were outsiders ahead of their time.

In Britain, Alan Turing, a mathematician who helped crack Germany’s Enigma codes during World War II, thought the human brain was, in many ways, a biological version of a digital computing machine. 

Turing was a strange and isolated figure who was decades ahead of his time. Growing up, he and his brother, John, rarely saw their parents (his father was part of the Madras government in India) and were left in the care of a retired colonel who lived in Hastings. Turing’s mind was prodigious, and a natural fit for mathematics but he didn’t fit in at school, where the classics, and writing, were seen as more important than numeracy: his English teacher said his writing was ‘the worst I have ever seen, and I try to view tolerantly his unswerving inexactitude and slipshod, dirty, work.’ His headmaster was no kinder: he was 'the sort of boy who is bound to be a problem for any school or community.’

Yet the young Turing excelled at physics, quantum mechanics and the working of the mind. He devoured Einstein’s theory of relativity, and went to Cambridge University where he graduated with a maths degree. After he was dragooned into Bletchley Park’s Government Code and Cypher School, he earned his PhD at Princeton in the US. Although Turing became known for his codebreaking prowess in cracking Enigma, he would play several key roles in the early years of British computing. His work was often theoretical, because the hardware required to bring his inventions to life simply didn’t exist in his lifetime. He ended up having an outsized impact on the development of AI.

Take one moment in 1948 as an example of Turing developing ideas on paper that he couldn’t yet put into practice. Since the early 1940s, Turing had believed that machines could mimic how human brains worked through programmed instructions to conduct some tasks, like playing games. One of those games he believed was a potential early use for computers was chess. In 1948, while working at Cambridge, Turing and a colleague, David Gowen Champernowne, developed a computer program called Turochamp that would follow rules laid down by its creator to play the right moves in a game of chess.

Turing and Champernowne didn’t get to test out Turochamp (named after its inventors) on an actual computer because the tech at the time wasn’t powerful enough to run the program. Turing would actually die before the program was loaded into a computer. But it worked – and chess, as we’ll learn in a subsequent chapter, would be intrinsically tied up with the development of AI.

Each step along his journey, from breaking Enigma to developing Turochamp, imbued Turing with the skills he’d need to start helping create alongside others, in broad outline, what would become the field of artificial intelligence. Unlike others who believed we humans are born with innate abilities – the so-called ‘nativist’ approach – Turing believed humans are born with an ‘unorganised machine’: the brain, which is trained to become a universal machine by our lives and experiences. Turing reckoned there wasn’t much difference between the cold logic of the early computers he was tinkering with in the 1940s and the human brain.

He put forward his ideas in a discussion at the University of Manchester, where he was deputy director of the computing laboratory, on 27th October 1949. The others present were Turing’s colleague, Max Newman; a chemist and philosopher called Michael Polanyi; and a zoologist-cum-neurophysiologist, John Zachary Young. Polanyi and Young argued against Turing’s idea that the human mind was programmable like a computer; Turing replied that they had simply not yet thought about it. ‘The mind is only said to be unspecifiable because it has not yet been specified,’ Turing reportedly said, according to contemporary minutes.

Turing didn’t manage to convince either philosopher or neurophysiologist that the human brain and the logic that dictated computers were similar. He did, however, convince himself. He kept plugging away at the problem. He designed a test to ascertain whether a computer could, in theory, ‘think’ the same way a human does. Turing thought determining whether computers could think outright was too tricky, but designed his test to see whether it could imitate the process. He put forward his test in an academic paper in the Mind journal titled ‘Computing machinery and intelligence' in 1950. The ‘Turing test’ was a version of the Imitation Game, a classic parlour game in which a player would hide their gender while answering questions from another, who would try to guess whether they were man or woman from their answers.

Turing’s test asked a simple question: could a computer talk like a human? The idea was that a human judge would take part in a text-based conversation, mediated through computers, with different players. The judge would look at the responses to their questions, and decide whether they sounded human, even if they weren’t. If the judge considered a computer indistinguishable from human participants, it would be considered ‘intelligent’.

Turing believed that was the benchmark for intelligent behaviour from a computer – and he believed that while it wasn’t likely to be possible in 1950, it would be by the year 2000. At that point, he reasoned, computers would have 100 megabytes of memory, which he thought would be enough to pass the test:

An average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning.… I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.

Sure enough, Turing was right about computer memory, also called RAM, or random access memory. An Apple PowerBook G4 released in 2001 had 128 megabytes of RAM as standard. Turing was wrong, however, in his confident prediction that a computer could pass the Turing test with that level of memory – at present, no artificial intelligence system has managed to convince someone they’re human, despite their computational power now far outstripping the 100 megabytes he calculated would be sufficient. (They have got closer, however.) But Turing wasn’t the only computer scientist pushing the boundaries of the discpline.

...

John McCarthy was the US-born child of pro-communist Irish and Lithuanian immigrant journalists. Young John’s childhood was steeped equally in radicalism and science, encouraging him to question things frequently without worry, and to embrace technological innovation without fear.

‘There was a general confidence in technology as being simply good for humanity,’ he recalled. A precocious child, his parents gave him a Soviet technology book published in 1933 called 100,000 Whys: A Trip Around the Room, the English translation of which Nature called a ‘small guide to general knowledge [which] will serve in lessening to a slight degree the load of ignorance which so many carry’. (McCarthy read the book in its native Russian; his parents' Communist zeal meant that Russian was spoken frequently in their home, and John junior was fluent.)

100,000 Whys helped stoke in young John McCarthy a passion for technology that continued through his teenage years. Like Alan Turing, McCarthy showed a huge aptitude for mathematics. Unlike Turing, whose interest was downplayed and diminished by his teachers, McCarthy was given the chance to foment that interest. 

While at high school in California, McCarthy managed to get his hands on the recommended reading list for maths degrees at the California Institute of Technology (Caltech). He then bought the books and taught himself university-level mathematics – which would come in handy when he applied to Caltech in 1944. He graduated high school two years early, despite starting his schooltime education a year later than his peers because of sickness. A life lived on fast-forward continued at Caltech: he was allowed to skip the first two years of study when the admissions tutors saw what he could do.

But it wasn’t all plain sailing at the university. McCarthy was booted out of Caltech because he refused to attend physical education classes. He had a short stint in the army before returning to university and graduating with a bachelor’s degree in mathematics in 1948.

In September that same year, at the same time as Alan Turing was eagerly sketching out his idea for Turochamp, McCarthy, then 21, attended a talk by the celebrated Hungarian-American mathematician and computer scientist John von Neumann. At the time McCarthy, who would become a central figure in the development of artificial intelligence, was back at Caltech taking a one-year master’s programme.

Caltech was where von Neumann delivered the Hixon Symposium on Cerebral Mechanisms in Behavior. His subject was self-replicating automata: hypothetical machines that could manufacture copies of themselves‌‌‌.

That idea would linger in McCarthy’s mind throughout the maths PhD that he began at Princeton University the following year, a little over a decade after Turing gained his PhD from the same institution. McCarthy thought about von Neumann’s concept of simulating human intelligence on early computers. He even booked an appointment to present his early findings to von Neumann, who encouraged McCarthy to set them out in a paper. ‘But I didn’t write it up because I didn’t feel it was really good,’ McCarthy later said. So McCarthy kept plugging away at his PhD, receiving a doctorate in differential equations in 1951. From there, he moved quickly into teaching at Princeton, helping graduate students with their work. One of those graduate students, Jerry Rayna, came up with an inspired idea: that McCarthy should gather together like-minded people to produce papers on machine intelligence into a single collection.

In the 1950s, disparate academics were all working away on ideas that would push the principle that nascent computer hardware could be made to tackle tasks previously thought uniquely achievable by the human brain. In the US, a mathematics undergraduate at Harvard University, Marvin Minsky, had built a simple neural net learning machine, a way of mimicking the human brain through computer hardware. Minsky was egged on to work in what would become artificial intelligence, going on to get his PhD from Princeton in the field, after reading the short story Runaround by the science fiction author Isaac Asimov in the March 1942 issue of Astounding.

The story was set in the year 2015, when three of Asimov’s regular recurring characters, Gregory Powell, Mike Donovan and Robot SPD-13, go to Mercury on a mission to restart an abandoned selenium mine. The humans, after landing on the planet, send out SPD-13, who they call Speedy, to fetch a selenium sample and come back. After five hours, Speedy still hasn’t returned. When Powell and Donovan look at Speedy’s tracks, they realise he’s going round and round a major selenium deposit, but hasn’t obtained any.

The humans find Speedy going back and forth, spouting gibberish. One human says it seems like the robot is drunk.

As part of the story, Asimov wrote down the three laws of robotics which are now inexorably connected with his name:

A robot may not injure a human being or, through inaction, allow a human being to come to harm.

A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

Speedy was protecting himself under rule three from damage caused by the selenium, despite trying to achieve the second: obey its human orders. In the end, Powell and Donovan have to invoke the first law: without the selenium, they can’t power their temperature shield. They would die. It works: Speedy grabs the selenium, and saves the humans.

When Minsky read Runaround, he was enthralled. ‘I never stopped thinking about how minds might work,’ he said. ‘Surely we’d someday build robots that think. But how would they think and about what? Surely logic might work for some purposes, but not for others. And how to build robots with common sense, intuition, consciousness and emotion? How, for that matter, do brains do those things?’

Simultaneously in the United States, Claude Shannon, who would later be known as the father of information theory, a key concept behind computing, was trying to develop a computer program that could ‘play’ chess, as well as developing small relay-powered machines to play different roles, such as a mouse in a maze searching for a target.

By 1952, things were starting to come together. All three men – McCarthy, Minsky (egged on by Rayna) and Shannon – were working on the same fundamental problem. They came at it from different points of view, but they all believed in the principle that one day, technology could attempt to tackle issues previously thought solely the preserve of the human brain. They also knew that in the UK, Alan Turing was having similar thoughts, as were other academics across the globe. 

So the three American researchers decided to try and convene together those disparate academic theories into a single collection of papers that would kickstart the development of a new, formal field of research. The question was what to call the collection when it was published. 

Opinions differed. McCarthy favoured calling the collection Artificial Intelligence, he later said. But McCarthy and Minsky deferred to Shannon, a decade older than them, when Shannon suggested their collected volume of research papers on the emerging field of computer intelligence should be called Automata Studies‌. ‘Shannon thought that artificial intelligence was too flashy a term and might attract unfavourable notice,’ McCarthy recalled.

He later regretted accepting his elder’s decision. ‘The papers started coming in and I was disappointed,’ McCarthy said. ‘Not enough of them were about intelligence.’ He felt the publication’s title led people down the wrong path.

So three years later, with Automata Studies still waiting to be published, McCarthy, now an assistant professor at the private Ivy League university Dartmouth College, had a second crack at the task. In August 1955, with the support of colleagues including Minsky, Shannon and Nathaniel Rochester – the co-designer of the IBM 701: the first mass-produced scientific computer – he drafted a proposal to the Rockefeller Foundation asking them to fund a conference on what he felt could become the next big thing. And this time he was going to call it artificial intelligence.

The 17-page proposal outlined a two-month, ‘10 man study of artificial intelligence’ to try and crack this new area. ‘The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it,’ the authors wrote. They asked for $13,500 funding – equivalent to nearly $150,000 today, with adjustments for inflation and relative purchasing power – to support innovative thinking. The money would cover train fares and accommodation for attendees, as well as buying them out of their positions with universities and big businesses. The scientists wrote: ‘We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.’

The conference was based on something McCarthy had heard of but never attended: a military summer conference on air defence. ‘I thought major projects could be undertaken during the course,’ he said.

In the end, the Rockefeller Foundation only funded half of the grant application for the Dartmouth Summer Research Project on Artificial Intelligence. (The name was McCarthy’s doing: he wanted to coin the phrase and ensure no confusion over the focus of their research.) The foundation’s reticence was well-founded. The months-long summer project was a disappointment.

Between 18th June and 17th August 1956, more than 35 presentations were given, with 20 leading academics from across the world presenting their latest research. ‘I believed if only we could get everyone who was interested in the subject together to devote time to it and avoid distractions, we could make real progress,’ McCarthy later said. But their intended goal of getting the smartest people from across the globe in the same room so sparks could fly didn’t materialise. Some big names dropped by for a day or two; few stayed longer than a week; and most talked at their peers, rather than with them.

‘Anybody who was there was pretty stubborn about pursuing the ideas that he had before he came, nor was there, as far as I could see, any real exchange of ideas,’ lamented McCarthy. ‘It was a great disappointment to me because it really meant that we couldn’t have regular meetings.’

Still, the conference brought about some innovations. Researchers at Carnegie Mellon University presented the second iteration of their Information Processing Language, or IPL, intended to act as a translation tool between the way humans think and the way computers process information. Although IPL is now mostly overlooked, at the time it was a significant moment and a useful impetus for early AI developers. It was the first computer language specifically designed to enable AI coding, and laid the groundwork for future high-level programming languages such as LISP (an acronym for list processing), developed by McCarthy, which became a mainstay of AI.

Beyond the granular, there were broader advances that benefited the growing world of AI research. The conference grouped AI into four broad areas: natural language processing, problem-solving, perception, and learning. Researchers who attended the Dartmouth conference – and a good many after that who didn’t – could quickly divide their work into these four areas.

The simple act of hosting an event focusing on a field variously described as ‘information processing’, ‘cybernetics’, and ‘automata studies’, meant they could be now presented under a single banner. ‘Artificial intelligence' had arrived.

This one small change opened doors previously shut to researchers – an indication of how a snappy title can help smooth progress. Universities began to support the burgeoning AI labs, with the three big beasts, Massachusetts Institute of Technology (MIT), Stanford University and Carnegie Mellon University, having their funders support new breakthrough hubs.

Not that they all agreed how artificial intelligence would work. The conference participants – and the wider field of AI researchers – divided broadly into two tribes: the neural network camp, who believed you could build a computer structured like the human brain, learning the way humans do; and the symbolic AI group, who believed neural networks (Turing’s idea) weren’t technologically possible.

The symbolic AI group thought any AI system would have to be painstakingly presented with a series of rules to follow. For instance, to train an AI to think it was a car, you would have to outline how it was constructed, how it worked, and under what circumstances it could go or stop, before even thinking about asking it to go from A to B.

As we’ll go on to see, that divide would be significant in the early stages of AI development. Given the technological capabilities of mid-20th Century computer hardware, the symbolic group took the lead because it was easier to encode computers with a long set of rules that allowed them free range to understand the world innately.

But the supporters of neural networks refused to give up. One of them was a psychologist called Frank Rosenblatt. In the late 1950s, Frank Rosenblatt invented the concept of the perceptron, developed with funding from the United States Office of Naval Research. The US armed forces were involved because they had a very real-world problem to solve. During the Cold War, the US Navy was keen to automatically interpret sonar signals, in order to distinguish a submarine from other underwater forms. The perceptron seemed promising, a sort of algorithmic sailor sorting harmless fish from harmful subs.

A perceptron made decisions based on received inputs. This was the underlying concept behind the neural network supporters’ vision of AI.

In the 13th July 1958 edition of the New York Times, under the headline ‘Electronic “Brain” Teaches Itself,’ the newspaper breathlessly reported the existence of the perceptron. Described as ‘the embryo of an electronic computer’, the newspaper claimed that ‘when completed in about a year, [it was] expected to be the first non-living mechanism able to “perceive, recognize and identify its surroundings without human training or control”.’

But some believed the perceptron wasn’t the saviour of AI. Marvin Minsky would be its strongest opponent, publishing a 1969 book, Perceptrons, with one of his students, Seymour Papert. Over the 360 pages of Perceptrons, re-released in a new, expanded edition in 1988, Minsky and Papert knocked back every argument for neural networks. The pair looked at the mathematics, finding that although a perceptron could perform basic pattern recognition, it couldn’t do much more.

Minsky and Papert identified what was then a deep flaw in neural networks: their inability to handle the exclusive-OR problem.

Imagine you have a tried and tested rule for whether or not to turn on your heating, dependent on two factors: the heat outside your home – whether it’s chilly or not – and the temperature inside.

It’s a balmy day outside, and neither warm nor cold inside: you probably won’t turn on the heating. If it’s cold outside and not warm inside, you probably will (otherwise the temperature inside may get colder). Now imagine it’s not cold outside, and also warm inside. You have no need to turn the heating on… so you don’t. If it’s cold outside, but warm inside, you’re also unlikely to reach for the thermostat.

This is a (very simplistic) exclusive-OR (or XOR) situation. You will only knock the thermostat up a few degrees if one condition is true – it’s either cold outside, or not warm inside. If both are true: it’s cold outside, and neither warm nor cold inside, you probably won’t.

Now imagine that a computer – which, remember, is the cornerstone of the neural networks intended to power these early stages of AI – tries to tackle this problem. Computers rely on binary decisions: if something happens, then do this. If something else happens, then don’t do that. But what if life is more complex than that (as it often is)?

Single-layer perceptrons as designed by Rosenblatt couldn’t handle that complexity. It would eventually be fixed with the development of multi-layer perceptrons but in the afterglow of the first Dartmouth conference – and far beyond Perceptrons’ 1969 publication – that wasn’t possible. So most AI researchers pursued the symbolic method, drawing up long lists of rules to teach computers about the world. At this stage, computers were not clever; they were drudges.

3

THE AI WINTER LOOMS

We are currently in the throes of an AI renaissance – but it hasn’t always been that way. AI hasn’t advanced in a single, upward exponential curve that those tech companies currently at the forefront of the generative AI revolution love to suggest. Instead, it’s a stuttering story of two steps forward, one step back.

To understand why, we have to go back to the Cold War.

For the United States, artificial intelligence held huge promise because of its ability to help the country maintain a scientific and military edge over its big ideological and geopolitical rival, the Soviet Union. The US government saw the potential of AI to provide near-simultaneous translation of diplomatic and spying messages sent in Russian that they were tapping. Similarly, the US government, in a race for scientific supremacy, would also want to know what Soviet scientists were publishing in academic papers. Speedier translations of technical reports would help them achieve that. In short, AI could aid the US in peeking behind the inscrutable Iron Curtain.

Machine translation, then (remember AI as a term would not be developed until the Dartmouth summer conference of 1956), was put at the forefront of development. And Georgetown University and tech titan IBM were in the vanguard. The two entities worked together to try and develop a machine translation tool throughout the early 1950s, with some success.

A New York Timesarticle, published on 8th January 1954, indicated just how far the Georgetown–IBM partnership had come in a short space of time. A day earlier, the partners in the project – Professor Leon Dostert and Dr Paul Garvin of Georgetown, and Dr Cuthbert C Hurd, of IBM’s division of applied science – had demonstrated the fruits of their partnership in the skyscraper IBM owned at 590 Madison Avenue in New York.

In front of the world’s media, Dostert, Garvin and Hurd explained how a program they had developed on the IBM Type 701 Electronic Data Processing Machine – which we met in the last chapter, and only 12 of which had been sold, all to military, commercial and university laboratories since its release the previous April – could translate 250 words in Russian into English almost instantaneously. But they didn’t just explain; they demonstrated it.

A female typist (typically of the time, she goes unnamed in the contemporary reports, despite her importance in the history of the technology) typed a sentence in Russian into the Type 701 computer: ‘Mi pyeryedayem mislyi posryedstvom ryechi.’

Within a flash, the computer spits back a sentence in English via a printer – the translation of the phrase: ‘We transmit thoughts by means of speech.’ Those 39 characters took around one second to print.

Emboldened, the typist input another sentence in Russian: ‘Vyelyichyina ugla opryedyelyayatsya otnoshyenyiyem dlyini dugi k radyiusu.’ Again, the computer returned with a near-simultaneous translation: ‘Magnitude of angle is determined by the relation of length of arc to radius.’

More sentences and phrases were tested out, including ones covering politics, communications and military affairs. ‘The sentences were turned into good English without human intervention,’ the New York Timesreported. The way the IBM Type 701 machine – technically a calculator, rather than a computer – did this was by following six broad rules for Russian syntax and grammar it had been ‘taught’ through a series of punch card commands. It then interrogated its memory of 250 Russian words to try and discern meaning from the sentence in English. Sometimes it would have to add in words to make sense in English; other times, it had to remove extraneous ones. And when a Russian word had multiple possible English translations, the computer was commanded to pick the one that best fitted the context.

IBM’s Hurd was exuberant about the experiment and its potential. While this trial had been tested on just 250 words, the Type 701 calculator had enough memory to store one million five-letter words. The Georgetown academics were equally bullish: Russian had been chosen as the language first tackled, naturally because of the Cold War, but also because of its grammatical complexity. A system able to understand Russian could be put to work on any language.

Taking the successful trial in their stride, the team confidently predicted they would then move on to broadening out the Russian language corpus, then on to French and German. ‘Then other Slavic, Germanic and Romance languages can be set up at will,’ the New York Times wrote. Georgetown’s Leon Dostert, who had worked for US President Dwight D Eisenhower during World War II, and was a translator by trade, was defiant about its future. ‘Five, perhaps three, years hence, interlingual meaning conversion by electronic process in important functional areas of several languages may well be an accomplished fact,’ he told reporters.

That confidence would turn out to be misplaced.

Like many, the US government read the New York Timesreport of the demonstration. And like many, they were wowed by what it had managed to do. US government agencies began supporting machine translation research in June 1956, around the same time the world’s pre-eminent thinkers in the field met at Dartmouth College. In part, the government’s willingness to splash the cash was down to the successful trial at IBM headquarters in January 1954.

Three US government departments– the Department of Defense, the National Science Foundation, and the Central Intelligence Agency (CIA) – clubbed together to create the Joint Automatic Language Processing Group (JALPG). The JALPG’s mission was to support the further development of machine translation technology such as that produced by the Georgetown-IBM researchers.

An estimated $20 million was spent on machine translation and other, closely related subjects in the decade after that first New York test – the equivalent of $186 million today. And despite being good at that small vocabulary, the Georgetown-IBM team struggled to get similar results when the dictionary expanded.

To find out why, a US government inquiry was opened by JALPG in 1964, led by the Automatic Language Processing Advisory Committee (ALPAC). Overseeing the ALPAC report was John R Pierce, a poindexterish employee of Bell Labs, supported by a phalanx of researchers, including those at the Massachusetts Institute of Technology.

The 124-page report’s findings, when they arrived in November 1966, were damning.

The last decade or more of development, bankrolled by the government, had been for nought. According to Pierce and his team, the experiment in January 1954 was the high watermark of the Georgetown-IBM project, rather than the dawn of a new age. Sometimes, the translations were wrong. Sometimes, they needed significant editing post-translation. Even when they were decent, they hadn’t evolved nearly as quickly as those behind them claimed they would.

As for trying to automatically translate Russian scientific publications as and when they appeared using technology, there was no point. The number of papers was actually low and there were plenty of human translators who could render them into English.

The report’s authors concluded that funding of machine translation should be halted until it could demonstrate that it could provide measurable returns. At that moment, it couldn’t provide those assurances, the ALPAC advised. Nor could four other machine translation systems that had followed in the Georgetown-IBM team’s footsteps: in fact, they were worse than the decade-old technology.

‘Early machine translations of simple or selected text,’ the report claimed, ‘were as deceptively encouraging as "machine translations" of general scientific text have been uniformly discouraging. […] We do not have useful machine translation [and] there is no immediate or predictable prospect of useful machine translation.’

The government-backed investigation into the promise of machine translation – an early form of AI– could not have been more transparent. And while the funding didn’t disappear overnight for AI projects, it started to dwindle as confidence in the feasibility of the technology waned.

Little did those working on the early sparks of AI development reading that ALPAC report in 1966 know that worse was to come, because the US was far from the only country investigating the promise of AI.

The UK, home to the inventor of the Turing test, was, too. And it would imminently come to similar conclusions, which signalled the death knell of the first AI revolution and the chill of the first AI winter.

Prior to 1971, Sir James Lighthill was best known not for his expertise in AI, but instead for his knowledge of fluid dynamics. A prolific publisher of academic research, he was the Lucasian Chair of Applied Mathematics at Cambridge University – a role that Stephen Hawking would later fill. Lighthill’s connection to Cambridge went back years: he joined Trinity College at the university in the late 1930s, at the tender age of 15, a true child prodigy. He was involved in designing the wing of Concorde, the supersonic aircraft, and was a known entity to government. Which is why, in September 1971, Brian Flowers, then the chair of the UK’s Science Research Council (SRC