Named a best book of the year by The Wall Street Journal • The Washington Post • Financial Times
Long before the advent of ChatGPT, Kai-Fu Lee and Chen Qiufan understood the enormous potential of artificial intelligence to transform our daily lives. But even as the world wakes up to the power of AI, many of us still fail to grasp the big picture. Chatbots and large language models are only the beginning.
In this “inspired collaboration” (The Wall Street Journal), Lee and Chen join forces to imagine our world in 2041 and how it will be shaped by AI. In ten gripping, globe-spanning short stories and accompanying commentary, their book introduces readers to an array of eye-opening settings and characters grappling with the new abundance and potential harms of AI technologies like deep learning, mixed reality, robotics, artificial general intelligence, and autonomous weapons. The result is a “dazzling” (The New York Times) book that “brims with intriguing insights” (Financial Times).
|Publisher:||Crown Publishing Group|
|Product dimensions:||9.20(w) x 6.20(h) x 1.50(d)|
About the Author
Chen Qiufan (aka Stanley Chan) is an award-winning author, translator, creative producer, and curator. He is the president of the World Chinese Science Fiction Association. His works include Waste Tide, Future Disease, and The Algorithms for Life. The founder of Thema Mundi, a content development studio, he lives in Beijing and Shanghai.
Read an Excerpt
I believe it’s indisputable that computers simply “think” differently from our brains. The best way to increase computer intelligence is to develop general computational methods (like deep learning and GPT-3) that scale with more processing power and more data. In the past few years, we’ve seen the best NLP models ingest ten times more data each year, and with each factor of ten, we saw qualitative improvements. In January 2021, just seven months after the release of GPT-3, Google announced a language model with 1.75 trillion parameters, which is nine times larger than GPT-3. This continued the trend of language model prowess growing by about ten times per year. This language model has already read more than any one of us could in millions of lifetimes. This progress will only grow exponentially.
While GPT-3 makes many basic mistakes, we are seeing glimmers of intelligence, and it is, after all, only version 3. Perhaps in twenty years, GPT-23 will read every word ever written and watch every video ever produced and build its own model of the world. This all-knowing sequence transducer would contain all the accumulated knowledge of human history. All you’ll have to do is ask it the right questions.
So, will deep learning eventually become “artificial general intelligence” (AGI), matching human intelligence in every way? Will we encounter “singularity” (see chapter 10)? I don’t believe it will happen by 2041. There are many challenges that we have not made much progress on or even understood, such as how to model creativity, strategic thinking, reasoning, counter-factual thinking, emotions, and consciousness. These challenges are likely to require a dozen more breakthroughs like deep learning, but we’ve had only one great breakthrough in over sixty years, so I believe we are unlikely to see a dozen in twenty years.
In addition, I would suggest that we stop using AGI as the ultimate test of AI. As I described in chapter 1, AI’s mind is different from the human mind. In twenty years, deep learning and its extensions will beat humans on an ever-increasing number of tasks, but there will still be many existing tasks that humans can handle much better than deep learning. There will even be some new tasks that showcase human superiority, especially if AI’s progress inspires us to improve and evolve.
What’s important is that we develop useful applications suitable for AI and seek to find human-AI symbiosis, rather than obsess about whether or when deep-learning AI will become AGI. I consider the obsession with AGI to be a narcissistic human tendency to view ourselves as the gold standard.
Table of Contents
Introduction Kai-Fu Lee: The Real Story of AI xi
Introduction by Chen Qiufan: How We Can Learn to Stop Worrying and Embrace the Future with Imagination xix
1 The Golden Elephant 3
Analysis: Deep Learning, Big Data, Internet/Finance Applications, AI Externalities 22
2 Gods Behind the Masks 33
Analysis: Computer Vision, Convolutional Neural Networks, Deepfakes, Generative Adversarial Networks (GANs), Biometrics, AI Security 55
3 Twin Sparrows 67
Analysis: Natural Language Processing, Self-Supervised Training, GPT-3, AGI and Consciousness, AI Education 108
4 Contactless Love 121
Analysis: AI Healthcare, Alphafold, Robotic Applications, COVID Automation Acceleration 152
5 My Haunting Idol 167
Analysis: Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), Brain-Computer Interface (BCI), Ethical and Societal Issues 200
6 The Holy Driver 211
Analysis: Autonomous Vehicles, Full Autonomy and Smart Cities, Ethical and Social Issues 246
7 Quantum Genocide 255
Analysis: Quantum Computers, Bitcoin Security, Autonomous Weapons and Existential Threat 302
8 The Job Savior 313
Analysis: AI Job Displacement, Universal Basic Income (UBI), What AI Cannot Do, 3RS as a Solution to Displacement 344
9 Isle of Happiness 357
Analysis: AI and Happiness, General Data Protection Regulation (GDPR), Personal Data, Privacy Computing Using Federated Learning and Trusted Execution Environment (Tee) 389
10 Dreaming of Plenitude 401
Analysis: Plenitude, New Economic Models, The Future of Money, Singularity 423