AI Superpowers: China, Silicon Valley, and the New World Order

by Kai-Fu Lee

Groupon officially

combatants

For Wang Xing, venture funding was his grain, a superior product was his wall, and a billion-dollar market would be his throne.

prime example of a market-driven lean startup.

artificial intelligence is the new electricity, big data is the oil that powers the generators. And as China’s vibrant and unique internet ecosystem took off after 2012, it turned into the world’s top producer of this petroleum for the age of artificial intelligence.

Chinese companies don’t have this kind of luxury. Surrounded by competitors ready to reverse-engineer their digital products, they must use their scale, spending, and efficiency at the grunt work as a differentiating factor.

is the volume of data that will be the most important going forward.

Going heavy means building walls around your business, insulating yourself from the economic bloodshed of China’s gladiator wars.

required market-driven entrepreneurs, mobile-first users, innovative super-apps, dense cities, cheap labor, mobile payments, and a government-sponsored culture shift.

what, where, and when

quantity of food deliveries and fifty to one in spending on mobile payments.

When it comes to rides on shared bikes, China is outpacing the United States at an astounding ratio of three hundred to one.

But building an AI-driven economy requires more than just gladiator entrepreneurs and abundant data. It also takes an army of trained AI engineers and a government eager to embrace the power of this transformative technology. These two factors—AI expertise and government support—are the final pieces of the AI puzzle. When put in place, they will complete our analysis of the competitive balance between the world’s two superpowers in the defining technology of the twenty-first century.

abundant data, tenacious entrepreneurs, well-trained AI scientists, and a supportive policy environment.

It will come from an army of well-trained engineers who team up with entrepreneurs to turn those discoveries into game-changing companies.

AI startups and generous government contracts to accelerate adoption.

This is particularly true when the payoff of a breakthrough is diffused throughout society rather than concentrated in a few labs or weapons systems.

tinkerers of this age. Today, those tinkerers are putting AI’s superhuman powers of pattern recognition to use making loans, driving cars, translating text, playing Go, and powering your Amazon Alexa.

Artificial intelligence researchers tend to be quite open about publishing their algorithms, data, and results.

That openness grew out of the common goal of advancing the field and also from the desire for objective metrics in competitions. In many physical sciences, experiments cannot be fully replicated from one lab to the next—minute variations in technique or environment

can greatly affect results. But AI experiments are perfectly replicable, and algorithms are directly comparable. They simply require those algorithms to be trained and tested on identical data sets. International competitions frequently pit different computer vision or speech recognition teams against each...

So instead of sitting on that research, they opt for instant publication on websites like www.arxiv.org

In the post-AlphaGo world, Chinese students, researchers, and engineers are among the most voracious readers of www.arxiv.org

These AI-focused outlets boast over a million registered users, and half of them have taken on venture funding that values them at more than $10 million each.

“Weekly Paper Discussion Group,” just one of the dozens of WeChat groups that come together to dissect a new AI research publication each week.

Alibaba, Tencent, Lenovo, and Huawei. In 2015, a team from Microsoft Research Asia blew the competition out of the water at the global image-recognition competition, ImageNet. The team’s breakthrough algorithm was called ResNet, and it identified and classified objects from 100,000 photographs into 1,000 different categories with an error rate of just 3.5 percent.

Algorithms are also being used to sniff out “fake news” on the platform, often in the form of bogus medical treatments. Originally, readers discovered and reported misleading stories—essentially, free labeling of that data. Toutiao then used that labeled data to train an algorithm that could identify fake news in the wild. Toutiao even trained a separate algorithm to write fake news stories. It then pitted those two algorithms against each other, competing to fool one another and improving both in the process.

That’s because humans normally make predictions on the basis of strong features, a handful of data points that are highly correlated to a specific outcome, often in a clear cause-and-effect relationship.

This act of seeking out various correlations and making predictions is exactly what deep learning excels at.

iFlyTek has taken the lead in applying AI to the courtroom, building tools and executing a Shanghai-based pilot program that uses data from past cases to advise judges on both evidence and sentencing.

speech recognition and natural-language processing to compare all evidence presented—testimony, documents, and background material—and

and seek out contradictory fact patterns. It then alerts the judge to these disputes, allowing for further investigation a...

yet another AI tool for advice on...

The sentencing assistant starts with the fact pat...

defendant’s criminal record, age, damages incurred, and so on—then its algorithms scan millions of co...

Judges can also view similar cases as data points scattered across an X–Y graph, clicking on each...

One Chinese province is even using AI to rate and rank all prosecutors on their performance.

the “risk” level of prisoners up for parole, though the role and lack of transparency of these AI tools have already been challenged in higher courts.

tools that aid a real human in making informed decisions.

“liveness algorithm” to ensure no one can use a photograph of someone else’s face to pay for a meal.

“Hi, Mr. Lee, how’ve you been?” he says. “We’ve just got in a shipment of some fantastic Napa wines. I understand that your wife’s birthday is coming up, and we wanted to offer you a 10 percent discount on your first purchase of the 2014 Opus One. Your wife normally goes for Overture, and this is the premium offering from that same winery. It has some wonderful flavors, hints of coffee and even dark chocolate. Would you like a tasting?”

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.

During in-class teaching, schools will employ a dual-teacher model that combines a remote broadcast lecture from a top educator and more personal attention by the in-class teacher. For the first half of class, a top-rated teacher delivers a lecture via a large-screen television at the front of the class. That teacher lectures simultaneously to around twenty classrooms and asks questions that students must answer via handheld clickers, giving the lecturer real-time feedback on whether students comprehend the concepts. During the lecture, a video conference camera at the front of the room uses facial recognition and posture analysis to take attendance, check for student attentiveness, and assess the level of understanding based on gestures such as nodding, shaking one’s head, and expressions of puzzlement. All of this data—answers to clicker questions, attentiveness, comprehension—goes directly into the student profile, filling in a real-time picture of what the students know and what they need extra help with.

From a teacher’s perspective, these same tools can be used to alleviate the burden of routine grading tasks, freeing up teachers to spend more time on the students themselves.

Heat-resistant drone swarms will fight forest fires with hundreds of times the current efficiency of traditional fire crews.

DJI is estimated to already own 50 percent of the North American drone market and even larger portions of the high-end segment.

By 2016, Google had taken six years to accumulate 1.5 million miles of real-world driving data. In just six months, Tesla had accumulated 47 million miles.

American companies remain two to three years ahead of China. In technology timelines, that’s light-years of distance.

will the primary bottleneck to full deployment be one of technology or policy?

general purpose technologies,