Key Insights & Memorable Quotes
Below are the most popular and impactful highlights and quotes from AI 2041: Ten Visions for Our Future:
Regardless of these theories, I believe it’s indisputable that computers simply “think” differently from our brains.
To the AI, such things were a matter of math, not love.
IT IS BETTER TO LIVE YOUR OWN DESTINY IMPERFECTLY THAN TO IMITATE SOMEBODY ELSE’S PERFECTLY.
RENEWABLE ENERGY REVOLUTION: SOLAR + WIND + BATTERIES In addition to AI, we are on the cusp of another important technological revolution—renewable energy. Together, solar photovoltaic, wind power, and lithium-ion battery storage technologies will create the capability of replacing most if not all of our energy infrastructure with renewable clean energy. By 2041, much of the developed world and some developing countries will be primarily powered by solar and wind. The cost of solar energy dropped 82 percent from 2010 to 2020, while the cost of wind energy dropped 46 percent. Solar and onshore wind are now the cheapest sources of electricity. In addition, lithium-ion battery storage cost has dropped 87 percent from 2010 to 2020. It will drop further thanks to the massive production of batteries for electrical vehicles. This rapid drop in the price of battery storage will make it possible to store the solar/wind energy from sunny and windy days for future use. Think tank RethinkX estimates that with a $2 trillion investment through 2030, the cost of energy in the United States will drop to 3 cents per kilowatt-hour, less than one-quarter of today’s cost. By 2041, it should be even lower, as the prices of these three components continue to descend. What happens on days when a given area’s battery energy storage is full—will any generated energy left unused be wasted? RethinkX predicts that these circumstances will create a new class of energy called “super power” at essentially zero cost, usually during the sunniest or most windy days. With intelligent scheduling, this “super power” can be used for non-time-sensitive applications such as charging batteries of idle cars, water desalination and treatment, waste recycling, metal refining, carbon removal, blockchain consensus algorithms, AI drug discovery, and manufacturing activities whose costs are energy-driven. Such a system would not only dramatically decrease energy cost, but also power new applications and inventions that were previously too expensive to pursue. As the cost of energy plummets, the cost of water, materials, manufacturing, computation, and anything that has a major energy component will drop, too. The solar + wind + batteries approach to new energy will also be 100-percent clean energy. Switching to this form of energy can eliminate more than 50 percent of all greenhouse gas emissions, which is by far the largest culprit of climate change.
Imagine, a $1,000 political assassin! And this is not a far-fetched danger for the future, but a clear and present danger.
In the story of AI and humans, if we get the dance between artificial intelligence and human society right, it would unquestionably be the single greatest achievement in human history.
The popular 2020 documentary The Social Dilemma illustrates how AI’s personalization will cause you to be unconsciously manipulated by AI and motivated by profit from advertising. The Social Dilemma star Tristan Harris says: “You didn’t know that your click caused a supercomputer to be pointed at your brain. Your click activated billions of dollars of computing power that has learned much from its experience of tricking two billion human animals to click again.” And this addiction results in a vicious cycle for you, but a virtuous cycle for the big Internet companies that use this mechanism as a money-printing machine. The Social Dilemma further argues that this may narrow your viewpoints, polarize society, distort truth, and negatively affect your happiness, mood, and mental health. To put it in technical terms, the core of the issue is the simplicity of the objective function, and the danger from single-mindedly optimizing a single objective function, which can lead to harmful externalities. Today’s AI usually optimizes this singular goal—most commonly to make money (more clicks, ads, revenues). And AI has a maniacal focus on that one corporate goal, without regard for users’ well-being.
Deepfakes are built on a technology called generative adversarial networks (GAN). As the name suggests, a GAN is a pair of “adversarial” deep learning neural networks. The first network, the forger network, tries to generate something that looks real, let’s say a synthesized picture of a dog, based on millions of pictures of dogs. The other network, the detective network, compares the forger’s synthesized dog picture with genuine dog pictures, and determines if the forger’s output is real or fake.
While humans lack AI’s ability to analyze huge numbers of data points at the same time, people have a unique ability to draw on experience, abstract concepts, and common sense to make decisions. By contrast, in order for deep learning to function well, the following are required: massive amounts of relevant data, a narrow domain, and a concrete objective function to optimize. If you’re short on any one of these, things may fall apart.
So, is 100-percent detection of deepfakes hopeless? In the very long term, 100-percent detection may be possible with a totally different approach—to authenticate every photo and video ever taken by every camera or phone using blockchain technology (which guarantees that an original has never been altered), at the time of capture. Then any photo loaded to a website must show its blockchain authentication. This process will eliminate deepfakes. However, this “upgrade” will not arrive by 2041, as it requires all devices to use it (like all AV receivers use Dolby Digital today), and blockchain needs to become fast enough to process this at scale.
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.
Some of these bots are already arriving in 2021 in more primitive forms. Recently, when I was in quarantine at home in Beijing, all of my e-commerce packages and food were delivered by a robot in my apartment complex. The package would be placed on a sturdy, wheeled creature resembling R2-D2. It could wirelessly summon the elevator, navigate autonomously to my door, and then call my phone to announce its arrival, so I could take the package, after which it would return to reception. Fully autonomous door-to-door delivery vans are also being tested in Silicon Valley. By 2041, end-to-end delivery should be pervasive, with autonomous forklifts moving items in the warehouse, drones and autonomous vehicles delivering the boxes to the apartment complex, and the R2-D2 bot delivering the package to each home. Similarly, some restaurants now use robotic waiters to reduce human contact. These are not humanoid robots, but autonomous trays-on-wheels that deliver your order to your table. Robot servers today are both gimmicks and safety measures, but tomorrow they may be a normal part of table service for many restaurants, apart from the highest-end establishments or places that cater to tourists, where the human service is integral to the restaurant’s charm. Robots can be used in hotels (to clean and to deliver laundry, suitcases, and room service), offices (as receptionists, guards, and cleaning staff), stores (to clean floors and organize shelves), and information outlets (to answer questions and give directions at airports, hotels, and offices). In-home robots will go beyond the Roomba. Robots can wash dishes (not like a dishwasher, but as an autonomous machine in which you can pile all the greasy pots, utensils, and plates without removing leftover food, with all of them emerging cleaned, disinfected, dried, and organized). Robots can cook—not like a humanoid chef, but like an automated food processor connected to a self-cooking pot. Ingredients go in and the cooked dish comes out. All of these technology components exist now—and will be fine-tuned and integrated in the decade to come. So be patient. Wait for robotics to be perfected and for costs to go down. The commercial and subsequently personal applications will follow. By 2041, it’s not far-fetched to say that you may be living a lot more like the Jetsons!
EVERYTHING IS INTERWOVEN AND THE WEB IS HOLY. —MARCUS AURELIUS
To put it in technical terms, the core of the issue is the simplicity of the objective function, and the danger from single-mindedly optimizing a single objective function, which can lead to harmful externalities.
the greatest value of science fiction is not providing answers, but rather raising questions.
Go is a board game more complex than chess by one million trillion trillion trillion trillion times.
Many people think smartphones and apps already know too much about us, but XR will take things to a whole new level.
More data leads to better AI (artificial intelligence), more automation leads to greater efficiency, more usage leads to reduced cost, and more free time leads to greater productivity. All of these will grow into a mutually reinforcing virtuous circle that will continually and rapidly increase the adoption of AV (autonomous vehicles).
This means our future is one where everything digital can be forged, including online video, recorded speech, security camera footage, and courtroom evidence video.
I anticipate diagnostic AI will exceed all but the best doctors in the next twenty years. This trend will be felt first in fields like radiology, where computer-vision algorithms are already more accurate than good radiologists for certain types of MRI and CT scans. In the story “Contactless Love,” we see that by 2041 radiologists’ jobs will be mostly taken over by AI. Alongside radiology, we will also see AI excel in pathology and diagnostic ophthalmology. Diagnostic AI for general practitioners will emerge later, one disease at a time, gradually covering all diagnoses. Because human lives are at stake, AI will first serve as a tool within doctors’ disposal or will be deployed only in situations where a human doctor is unavailable. But over time, when trained on more data, AI will become so good that most doctors will be routinely rubber-stamping AI diagnoses, while the human doctors themselves are transformed into something akin to compassionate caregivers and medical communicators.
Dizziness hit Chen Nan. She had never taken a single risk in her life, and she was certain she had made the right decisions needed to survive in the COVID era. However, when she thought of Garcia in his sickbed, she was stricken by guilt. Perhaps I have only been exploiting his love, and giving little to none in return, she thought. Even a simple I love you was difficult for her, because she was afraid that once she said those magic words, the dynamics of the relationship would change, making her the weaker one, the vulnerable one, the one who cared more.
Do I really care about this relationship? Chen Nan asked herself. After a long, convoluted debate with herself, she came to the conclusion: a definitive yes. “Love” was a strong word, but no doubt she liked Garcia. They had cultivated the relationship entirely online, and she had enjoyed the time they spent together: going on missions together in the game, screaming their heads off like a pair of lunatics at virtual music festivals, or simply just communicating, via video chat, texting, or emoji wars. They came from very different cultural backgrounds, but they’d clicked almost immediately. She and Garcia were like a dumpling and a Brazilian pastel—they may look different on the outside, but their fillings were made from the same ingredients. Our souls, his and mine, are the same, thought Chen Nan.
Think about the tremendous benefits of electricity, mobile phones, and the Internet. In the course of human history, we have often been fearful of new technologies that seem poised to change the status quo. In time, these fears usually go away, and these technologies become woven into the fabric of our lives and improve our standard of living.
When CNNs were first discussed in the 1980s, there wasn’t enough data or computational power to show what they could do. It was not until about 2012 that it became clear this technology would beat all previous approaches for computer vision. It was a happy coincidence that around this time, a huge number of images and videos were being captured by smartphones and shared on social networks. Also around this time, fast computers and large storage were becoming affordable. The confluence of these elements catalyzed the maturation and proliferation of computer vision.
AI will also create efficient services that will give us back our most valuable resource—time. It will take over routine tasks and liberate us to do more stimulating or challenging jobs. Lastly, humans will work symbiotically with AI, with AI performing quantitative analysis, optimization, and routine work, while we humans contribute our creativity, critical thinking, and passion. Each human’s productivity will be amplified, allowing us to realize our potential. The profound contributions AI is poised to make to humanity need to be explored as deeply as its challenges.
Imagination indeed shapes the world.
Too many people had been made redundant by the quick and aggressive development of AI, with little guidance from leaders on new pathways to employment. Cascading failures led to a high suicide rate. Some began calling for the repeal of UBI. By 2028, social media was consumed by the Great Debate over the pros and cons of UBI. The Senate and the House sank into protracted back-and-forth warfare over a proposed plan to abolish UBI. “The
But if we stop helping people—stop loving people—because of fear, then what makes us different from machines?
When we “see,” we are actually applying our accumulated knowledge of the world—everything we’ve learned in our lives about perspective, geometry, common sense, and what we have seen previously. These come naturally to us but are very difficult to teach a computer. Computer vision is the field of study that tries to overcome these difficulties to get computers to see and understand. COMPUTER VISION APPLICATIONS We are already using computer vision technologies every day. Computer vision can be used in real time, in areas ranging from transportation to security. Existing examples include: driver assistants installed in some cars that can detect a driver who nods off autonomous stores like Amazon Go, where cameras recognize when you’ve put a product in your shopping cart airport security (counting people, recognizing terrorists)
Computer vision (CV) is the subbranch of AI that focuses on the problem of teaching computers to see. The word “see” here does not mean just the act of acquiring a video or image, but also making sense of what a computer sees. Computer vision includes the following capabilities in increasing complexity: Image capturing and processing—use cameras and other sensors to capture real-world 3D scenes in a video. Each video is composed of a sequence of images, and each image is a two-dimensional array of numbers representing the color, where each number is a “pixel.” Object detection and image segmentation—divide the image into prominent regions and find where the objects are. Object recognition—recognizes the object (for example, a dog), and also understands the details (German Shepherd, dark brown, and so on). Object tracking—follows moving objects in consecutive images or video. Gesture and movement recognition—recognize movements, like a dance move in an Xbox game. Scene understanding—understands a full scene, including subtle relationships, like a hungry dog looking at a bone.
