About the book
This is one of the ”The Economist’s books of the year”. It is also a very political book and everyone who is interested about foreign policy should read this.
Like electricity…. “Deep-learning pioneer Andrew Ng has compared AI to Thomas Edison’s harnessing of electricity: a breakthrough technology on its own, and one that once harnessed can be applied to revolutionizing dozens of different industries. Just as nineteenth-century entrepreneurs soon began applying the electricity breakthrough to cooking food, lighting rooms, and powering industrial equipment, today’s AI entrepreneurs are doing the same with deep learning. Much of the difficult but abstract work of AI research has been done, and it’s now time for entrepreneurs to roll up their sleeves and get down to the dirty work of turning algorithms into sustainable businesses.”
Harnessing the power of AI today—the “electricity” of the twenty-first century—requires four analogous inputs:
– abundant data,
– hungry entrepreneurs,
– AI scientists and
– an AI-friendly policy environment.
What are the key learnings?
This is a boring book if you have already read five other AI-books for business people. Kai-Fu Lee uses the same examples that are already widely used. For example why we are currently living in a AI-era? Because of the computing power and data. ”Both data and computing power were in short supply at the dawn of the field in the 1950s.” Or ”The turning point came in 2012, when a neural network built by Hinton’s team demolished the competition in an international computer vision contest.” Or “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.”
Key learnings:
– Copycat Era
o Chinese startup ecosystem. The copycat era had forged world-class entrepreneurs, and they were just beginning to apply their skills to solving uniquely Chinese problems.
§ They burn cash like crazy and rely on armies of low-wage delivery workers to make their business models work. It’s a defining trait of China’s alternate internet universe that leaves American analysts entrenched in Silicon Valley orthodoxy scratching their heads.
– Saudi-Arabia of Data
o These companies are turning China into the Saudi Arabia of data.
– O2O Evolution
o Online-Merge-Offline
o Analysts dubbed the explosion of real-world internet services that blossomed across Chinese cities the “O2O Revolution,” short for “online-to-offline.”
o Uber may have given an early glimpse of O2O, but it was Chinese companies that would take the core strengths of that model and apply it to transforming dozens of other industries.
o But the O2O revolution showcased an even deeper—and in the age of AI implementation, more impactful—divide between Silicon Valley and China—what I call “going light” versus “going heavy.” The terms refer to how involved an internet company becomes in providing goods or services. They represent the extent of vertical integration as a company links up the on-and offline worlds. When looking to disrupt a new industry, American internet companies tend to take a “light” approach. Going heavy means building walls around your business, insulating yourself from the economic bloodshed of China’s gladiator wars.
– AI expertise and government support.
Other learnings are that:
– Hail China…
o But around 2013, China’s internet took a right turn. Rather than following in the footsteps or outright copying of American companies, Chinese entrepreneurs began developing products and services with simply no analog in Silicon Valley.
– Half of the AI-market will go to China…..
o “PricewaterhouseCoopers estimates AI deployment will add $ 15.7 trillion to global GDP by 2030. China is predicted to take home $ 7 trillion of that total, nearly double North America’s $ 3.7 trillion in gains.”
– Entrepreneurs….
o The most valuable product to come out of China’s copycat era wasn’t a product at all: it was the entrepreneurs themselves.
– Key message of the book…..
o Corporate America is unprepared for this global wave of Chinese entrepreneurship because it fundamentally misunderstood the secret to The Cloner’s success.
The Four Waves of AI
The complete AI revolution will take a little time and will ultimately wash over us in a series of four waves:
1) Internet AI i.e. Optimization of user behaviour
2) Business AI i.e. Optimization of business data
3) Perception AI i.e. Optimizing online and offline environment
4) Autonomous AI i.e. Optimizing machine learning and perception
“The first two waves—internet AI and business AI—are already all around us.
Perception AI is now digitizing our physical world, learning to recognize our faces, understand our requests, and “see” the world around us. This wave promises to revolutionize how we experience and interact with our world, blurring the lines between the digital and physical worlds.
Autonomous AI will come last but will have the deepest impact on our lives. As self-driving cars take to the streets, autonomous drones take to the skies, and intelligent robots take over factories, they will transform everything from organic farming to highway driving and fast food.”
FIRST WAVE: INTERNET AI (optimization of user behaviour)
“Internet AI is largely about using AI algorithms as recommendation engines: systems that learn our personal preferences and then serve up content hand-picked for us.
Average people experience this as the internet “getting better”—that is, at giving us what we want—and becoming more addictive as it goes.”
This is the Technolandia.
SECOND WAVE: BUSINESS AI (optimization of business data)
“For instance, insurance companies have been covering accidents and catching fraud, banks have been issuing loans and documenting repayment rates, and hospitals have been keeping records of diagnoses and survival rates. All of these actions generate labeled data points.
Business AI mines these databases for hidden correlations that often escape the naked eye and human brain.
Optimizations like this work well in industries with large amounts of structured data on meaningful business outcomes. In this case, “structured” refers to data that has been categorized, labeled, and made searchable. Prime examples of well-structured corporate data sets include historic stock prices, credit-card usage, and mortgage defaults.
There’s no question that China will lag in the corporate world, but it may lead in public services and industries with the potential to leapfrog outdated systems.”
THIRD WAVE: PERCEPTION AI (optimizing online and offline environment)
“Third-wave AI is all about extending and expanding this power throughout our lived environment, digitizing the world around us through the proliferation of sensors and smart devices.
Amazon Echo is digitizing the audio environment of people’s homes. Alibaba’s City Brain is digitizing urban traffic flows through cameras and object-recognition AI. Apple’s iPhone X and Face + + cameras perform that same digitization for faces, using the perception data to safeguard your phone or digital wallet.
Important…… I call these new blended environments OMO: online-merge-offline. OMO is the next step in an evolution that already took us from pure e-commerce deliveries to O2O (online-to-offline) services. Each of those steps has built new bridges between the online world and our physical one, but OMO constitutes the full integration of the two.
True application… One KFC restaurant in China recently teamed up with Alipay to pioneer a pay-with-your-face option at some stores. Customers place their own order at a digital terminal, and a quick facial scan connects their order to their Alipay account—no cash, cards, or cell phones required…. Pay-with-your-face applications.
Central to that system is the Mi AI speaker, a voice-command AI device similar to the Amazon Echo but at around half the price, thanks to the Chinese home-court manufacturing advantage. That advantage is then leveraged to build a range of smart, sensor-driven home devices: air purifiers, rice cookers, refrigerators, security cameras, washing machines, and autonomous vacuum cleaners. Xiaomi doesn’t build all of these devices itself. Instead, it has invested in 220 companies and incubated 29 startups—many operating in Shenzhen—whose intelligent home products are hooked into the Xiaomi ecosystem. Together they are creating an affordable, intelligent home ecosystem, with WiFi-enabled products that find each other and make configuration easy.
Third-wave AI products like these are on the verge of transforming our everyday environment, blurring lines between the digital and physical world until they disappear entirely.
These third-wave AI innovations will create tremendous economic opportunities and also lay the foundation for the fourth and final wave, full autonomy.”
FOURTH WAVE: AUTONOMOUS AI
For example…. “Shenzhen is home to DJI, the world’s premier drone maker and what renowned tech journalist Chris Anderson called “the best company I have ever encountered.” DJI is estimated to already own 50 percent of the North American drone market and even larger portions of the high-end segment.”
Perfect is the enemy of the good
“Perfect vs. Incremental vs. the Chinese mentality is that you can’t let the perfect be the enemy of the good. Indeed, local officials are already modifying existing highways, reorganizing freight patterns, and building cities that will be tailor-made for driverless cars.
America’s global juggernauts seek to conquer these markets for themselves, China is instead arming the local startup insurgents.
There are already some precedents for the Chinese approach. Ever since Didi drove Uber out of China, it has invested in and partnered with local startups fighting to do the same thing in other countries: Lyft in the United States, Ola in India, Grab in Singapore, Taxify in Estonia, and Careem in the Middle East.
An alternate model of AI globalization: empower homegrown startups by marrying worldwide AI expertise to local data.”
Utopia vs. Dystopia
“It has fed a belief that we’re on the verge of achieving what some consider the Holy Grail of AI research, artificial general intelligence (AGI)—thinking machines with the ability to perform any intellectual task that a human can—and much more. Some predict that with the dawn of AGI, machines that can improve themselves will trigger runaway growth in computer intelligence. Often called “the singularity,” or artificial superintelligence, this future involves computers whose ability to understand and manipulate the world dwarfs our own, comparable to the intelligence gap between human beings and, say, insects. Such dizzying predictions have divided much of the intellectual community into two camps: utopians and dystopians.
Getting to AGI would require a series of foundational scientific breakthroughs in artificial intelligence, a string of advances on the scale of, or greater than, deep learning. These breakthroughs would need to remove key constraints on the “narrow AI” programs that we run today and empower them with a wide array of new abilities: multidomain learning; domain-independent learning; natural-language understanding; commonsense reasoning, planning, and learning from a small number of examples.
General Purpose Technologies (GPTs)
Like the utopian and dystopian forecasts for AGI, this prediction of a jobs and inequality crisis is not without controversy. A large contingent of economists and techno-optimists believe that fears about technology-induced job losses are fundamentally unfounded.
These are what economists call General Purpose Technologies, or GPTs. In their landmark book The Second Machine Age, MIT professors Erik Brynjolfsson and Andrew McAfee described GPTs as the technologies that “really matter,” the ones that “interrupt and accelerate the normal march of economic progress.”
Three technologies that receive broad support: the steam engine, electricity, and information and communication technology (such as computers and the internet).”
Consulting firm PwC predicts that AI will add $ 15.7 trillion to the global economy by 2030.
Important…. Artificial intelligence will be the first GPT of the modern era in which China stands shoulder to shoulder with the West in both advancing and applying the technology.”
Leap and Data
“In deep learning, there’s no data like more data.
The country’s massive number of internet users—greater than the United States and all of Europe combined—gives it the quantity of data, but it’s then what those users do online that gives it the quality.
Hardly anyone noticed when the world’s most powerful app waltzed onto the world stage. The January 2011 launch of WeChat, Tencent’s new social messaging app, received only one mention in the English-language press, on the technology site the Next Web.
Airbnb largely remains a lightweight platform for listing your home, the company’s Chinese rival, Tujia, manages a large chunk of rental properties itself. For Chinese hosts, Tujia offers to take care of much of the grunt work: cleaning the apartment after each visit, stocking it with supplies, and installing smart locks.
Leap…. But that leap to mobile payments wasn’t just a product of weak incumbents and independent consumer choices. Alibaba and Tencent accelerated the transition by forcing adoption through massive subsidies, a form of “going heavy” that makes American technology companies squirm.
Imporant once again…. Data from mobile payments is currently generating the richest maps of consumer activity the world has ever known, far exceeding the data from traditional credit-card purchases or online activity captured by e-commerce players like Amazon or platforms like Google and Yelp.
Something new was emerging from all those rides: perhaps the world’s largest and most useful internet-of-things (IoT) networks.
THE STUFF OF AN AI SUPERPOWER
“As I laid out earlier, creating an AI superpower for the twenty-first century requires four main building blocks: abundant data, tenacious entrepreneurs, well-trained AI scientists, and a supportive policy environment. We’ve already seen how China’s gladiatorial startup ecosystem trained a generation of the world’s most street-smart entrepreneurs, and how China’s alternate internet universe created the world’s richest data ecosystem.”
About workforce and the future of workforce
Jobs….” The OECD team instead proposed a task-based approach, breaking down each job into its many component activities and looking at how many of those could be automated. In this model, a tax preparer is not merely categorized as one occupation but rather as a series of tasks that are automatable (reviewing income documents, calculating maximum deductions, reviewing forms for inconsistencies, etc.) and tasks that are not automatable (meeting with new clients, explaining decisions to those clients, etc.). The OECD team then ran a probability model to find what percentage of jobs were at “high risk” (i.e., at least 70 percent of the tasks associated with the job could be automated). As noted, they found that in the United States only 9 percent of workers fell in the high-risk category. Applying that same model on twenty other OECD countries, the authors found that the percentage of high-risk jobs ranged from just 6 percent in Korea to 12 percent in Austria.”
Kai-Fu Lee: “I also respectfully disagree with the low-end estimates of the OECD.”
“But beyond that disagreement over methodology, I believe using only the task-based approach misses an entirely separate category of potential job losses: industry-wide disruptions due to new AI-empowered business models. Separate from the occupation-or task-based approach, I’ll call this the industry-based approach.”
Theory or Paradox… “Core to this logic is a tenet of artificial intelligence known as Moravec’s Paradox. Hans Moravec was a professor of mine at Carnegie Mellon University, and his work on artificial intelligence and robotics led him to a fundamental truth about combining the two: contrary to popular assumptions, it is relatively easy for AI to mimic the high-level intellectual or computational abilities of an adult, but it’s far harder to give a robot the perception and sensorimotor skills of a toddler.”
“Within fifteen years I predict that we will technically be able to automate 40 to 50 percent of all jobs in the United States. That does not mean all of those jobs will disappear overnight, but if the markets are left to their own devices, we will begin to see massive pressure on working people.
What about Europe? Is our future the Dataslavery…..
No Europeans in the AI race…. “The seven that have emerged as the new giants of corporate AI research—Google, Facebook, Amazon, Microsoft, Baidu, Alibaba, and Tencent.” If Europe cannot race with the AI Superpowers we have to cope.
How to cope with AI? THE THREE R’S:
– REDUCE,
– RETRAIN
– REDISTRIBUTE
“Many of the proposed technical solutions for AI-induced job losses coming out of Silicon Valley fall into three buckets: retraining workers, reducing work hours, or redistributing income.”
Those advocating the retraining of workers tend to believe that AI will slowly shift what skills are in demand, but if workers can adapt their abilities and training, then there will be no decrease in the need for labor. Those advocates of reducing work hours believe that AI will reduce the demand for human labor and feel that this impact could be absorbed by moving to a three-or four-day work week, spreading the jobs that do remain over more workers. The redistribution camp tends to be the most dire in their predictions of AI-induced job losses. Many of them predict that as AI advances, it will so thoroughly displace or dislodge workers that no amount of training or tweaking hours will be sufficient.
How should we change according to the book?
“AI today—the “electricity” of the twenty-first century”. We should invent the applications that will be using AI?
What should I personally do?
Check this out…. www.arxiv.org
Summary
The book in six words – “You can’t connect the dots looking forward. You can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future.” (Steve Jobs)
