Summary

You cannot (nor should you) summarize a book like this. But the gist: China is not as far behind as you might think. They’ll (probably) be ahead of USA in terms of AI in the long run. They have much more data and are more willing to adapt to the necessary conditions for implementing AI in day-to-day life - being tracked, for example.

After the above, the book starts discussing the consequences of AI. The good and the bad. A critical issue is that, while the AI creators will marvel at their creations, those who get replaced by machines will question what it means to be human, then. They spend their whole lives mastering a skill, only to have a robot do it better and faster. The human consequences will be huge.

Lastly, the book details how we may live with AI. We do this by embracing what makes us human.

Highlights & Notes

China’s Sputnik Moment

Deep learning was a huge step forward in AI technology.

China became very motivated to make AI after AlphaGo’s victories.

Neural networks require large amounts of two things: computing power and data. The data “trains” the program to recognize patterns by giving it many examples, and the computing power lets the program parse those examples at high speeds.

How Deep Learning works

So how does deep learning do this? Fundamentally, these algorithms use massive amounts of data from a specific domain to make a decision that optimizes for a desired outcome. It does this by training itself to recognize deeply buried patterns and correlations connecting the many data points to the desired outcome. This pattern-finding process is easier when the data is labeled with that desired outcome—“cat” versus “no cat”; “clicked” versus “didn’t click”; “won game” versus “lost game.” It can then draw on its extensive knowledge of these correlations—many of which are invisible or irrelevant to human observers—to make better decisions than a human could.

Doing this requires massive amounts of relevant data, a strong algorithm, a narrow domain, and a concrete goal. If you’re short any one of these, things fall apart. Too little data? The algorithm doesn’t have enough examples to uncover meaningful correlations. Too broad a goal? The algorithm lacks clear benchmarks to shoot for in optimization.

Deep learning is what’s known as “narrow AI”—intelligence that takes data from one specific domain and applies it to optimizing one specific outcome. While impressive, it is still a far cry from “general AI,” the all-purpose technology that can do everything a human can.

Copycats in the Coliseum

The work ethic in china is almost maniacal.

The Chinese entrepreneurs weren’t cut from the same cloth that the Silicon Valley guys were. They’re ruthless in copying and adapting. They’re gladiators in a competitive environment.

The most valuable product to come out of China’s copycat era wasn’t a product at all: it was the entrepreneurs themselves.

WHY SILICON VALLEY GIANTS FAIL IN CHINA

They fail because they think their product is a one-size-fits-all. They ship their product to China, but do not tailor it to the Chinese browsing habits etc. So it fails to the Chinese equivalent. This happened to eBay vs. Alibaba.

Dismissing these companies as copycats relying on government protection in order to succeed blinds analysts to world-class innovation that is happening elsewhere.

Chinese startups cannot be dismissed as copycats. Their ‘copies’ are, in some cases, doing better than the ‘originals’. Their Uber is doing more rides in China than under is doing worldwide.

The Chinese learned the Lean Startup methodology.

That methodology was first explicitly formulated in Silicon Valley and popularized by the 2011 book The Lean Startup. Core to its philosophy is the idea that founders don’t know what product the market needs—the market knows what product the market needs. Instead of spending years and millions of dollars secretly creating their idea of the perfect product, startups should move quickly to release a “minimum viable product” that can tease out market demand for different functions. Internet-based startups can then receive instant feedback based on customer activity, letting them immediately begin iterating on the product: discard unused features, tack on new functions, and constantly test the waters of market demand. Lean startups must sense the subtle shifts in consumer behavior and then relentlessly tinker with products to meet that demand. They must be willing to abandon products or businesses when they don’t prove profitable, pivoting and redeploying to follow the money.

In a market where copying was the norm, these entrepreneurs were forced to work harder and execute better than their opponents. Silicon Valley prides itself on its aversion to copying, but this often leads to complacency. The first mover is simply ceded a new market because others don’t want to be seen as unoriginal. Chinese entrepreneurs have no such luxury. If they succeed in building a product that people want, they don’t get to declare victory. They have to declare war.

A Tale of Two Countries

As artificial intelligence filters into the broader economy, this era will reward the quantity of solid AI engineers over the quality of elite researchers. Real economic strength in the age of AI implementation won’t come just from a handful of elite scientists who push the boundaries of research. It will come from an army of well-trained engineers who team up with entrepreneurs to turn those discoveries into game-changing companies.

You don’t need cutting edge scientists to implement AI. Just some great AI engineers.

How the Chinese learned AI

In the post-AlphaGo world, Chinese students, researchers, and engineers are among the most voracious readers of www.arxiv.org. They trawl the site for new techniques, soaking up everything the world’s top researchers have to offer. Alongside these academic publications, Chinese AI students also stream, translate, and subtitle lectures from leading AI scientists like Yann LeCun, Stanford’s Sebastian Thrun, and Andrew Ng

The Four Waves of AI

A Vision for AI and Education

The perception, recognition, and recommendation abilities of AI can tailor the learning process to each student and also free up teachers for more one-on-one instruction time.

The AI-powered education experience takes place across four scenarios: in-class teaching, homework and drills, tests and grading, and customized tutoring. Performance and behavior in these four settings all feed into and build off of the bedrock of AI-powered education, the student profile. That profile contains a detailed accounting of everything that affects a student’s learning process, such as what concepts they already grasp well, what they struggle with, how they react to different teaching methods, how attentive they are during class, how quickly they answer questions, and what incentives drive them. To see how this data is gathered and used to upgrade the education process, let’s look at the four scenarios described above.

The Current Education Model

Present-day education systems are still largely run on the nineteenth-century “factory model” of education: all students are forced to learn at the same speed, in the same way, at the same place, and at the same time. Schools take an “assembly line” approach, passing children from grade to grade each year, largely irrespective of whether or not they absorbed what was taught. It’s a model that once made sense given the severe limitations on teaching resources, namely, the time and attention of someone who can teach, monitor, and evaluate students.

The Wisdom of Cancer

Yes, this technology will both create enormous economic value and destroy an astounding number of jobs. If we remain trapped in a mindset that equates our economic value with our worth as human beings, this transition to the age of AI will devastate our societies and wreak havoc on our individual psychologies.

But there is another path, an opportunity to use artificial intelligence to double down on what makes us truly human. This path won’t be easy, but I believe it represents our best hope of not just surviving in the age of AI but actually thriving. It’s a journey that I’ve taken in my own life, one that turned my focus from machines back to people, and from intelligence back to love.

Double down on what makes us human: embracing love.

For all of AI’s astounding capabilities, the one thing that only humans can provide turns out to also be exactly what is most needed in our lives: love. It’s that moment when we see our newborn babies, the feeling of love at first sight, the warm feeling from friends who listen to us empathetically, or the feeling of self-actualization when we help someone in need. We are far from understanding the human heart, let alone replicating it. But we do know that humans are uniquely able to love and be loved, that humans want to love and be loved, and that loving and being loved are what makes our lives worthwhile.

This is the synthesis on which I believe we must build our shared future: on AI’s ability to think but coupled with human beings’ ability to love. If we can create this synergy, it will let us harness the undeniable power of artificial intelligence to generate prosperity while also embracing our essential humanity.