The Economist, The New York Times, USA Today and many others have chosen among their must reads of the year AI Superpowers an interesting essay by Kai-fu Lee, former founding president of Google in China and artificial intelligence guru. Definitely a must read: it provides a quick and accurate overview on artificial intelligence and its state of the art, immediately deepening the analysis into a medium and long term strategic approach. A good glance at where we have arrived to understand exactly where we want to go in a competitive and geopolitical context dominated by the US-China dualism. If we can recall several strategic lines highlighted by Cool war, we finally ask ourselves what the impact of AI technologies will be on manufacturing industry, on employment, on the whole society. Mr. Lee highlights how the impact will be inevitable and extremely incisive on all aspects of our social and work life, without distinction of collar color when it will be to replace labor intensive jobs such as couriers, drivers, cashiers, etc. but also financial analysts, brokers, employees, managers. However, there are three aspects of the book that provide interesting insights:
1 – The age of the invention is over. We are in the age of implementation, in which China will play an important leadership role
The artificial intelligence sector is often treated as a cutting edge segment of innovation. In fact, a lot of the way was done from the first experiments of the 90s and 2000s with neural networks and the like. Deep learning is now a well established reality, and it is the reproduction of the thought architectures typical of living beings, capable of approaching complex problems without a starting supply of logical instructions (if this then that), but with an approach oriented to gradual learning. The comparison that often occurs is that of the discovery of electricity: the disruptive invention for our lives has already been realized, we are now in the age of implementation in which the challenge will be to electrify the various areas of society.
We need three main elements: computing skills (and we have now become widespread thanks to smartphones), huge amounts of data to feed the learning process (and we have, very concentrated in countries like China thanks to the big tech company ), and an ecosystem of entrepreneurs fueled by competitiveness and massive venture capital funds able to take up the challenge of “electrification”. The latter asset seems to be much more vibrant and present in China than the Silicon Valley model that prevails in the US and, according to Lee, destined to make a difference and soon determine the overtaking of the Asian giant.
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. […]
As I demonstrate […], that analysis is wrong. It is based on outdated assumptions about the Chinese technology environment, as well as a more fundamental misunderstanding of what is driving the ongoing AI revolution. The West may have sparked the fire of deep learning, but China will be the biggest beneficiary of the heat the AI fire is generating. That global shift is the product of two transitions: from the age of discovery to the age of implementation, and from the age of expertise to the age of data.
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.
2 – The Chinese ecosystem has important competitive advantages, despite Western clichés are hard to die
The cliché of the Chinese digital economy as an army of copycats able only to replicate the unicorns generated by Silicon Valley belongs to the past. The Chinese arena has been excellent for sharpening the teeth to a leverage of hardened entrepreneurs and able to apply winning business models and, always (think of Google, eBay, Amazon and many others in China) to frustrate attempts to access the market from leading western competitors in the old world. If many people still point the finger, in a pointless self-absolution, to the role of the Chinese authorities, some have finally come to understand how it has failed in the only key to success in the Chinese ecosystem: to create a tailor-made strategy on Chinese needs, and not to approach the market as any further foreign market on the bucket list of expansion.
Alibaba, Baidu, and many others have in fact created unique and tailor-made experiences on the Chinese consumer, coming now to overcome major Western players (think about the e-commerce industry, for example) both in volume of business and in the high level of innovation.
This brings us to the second major transition, from the age of expertise to the age of data. Today, successful AI algorithms need three things: big data, computing power, and the work of strong—but not necessarily elite—AI algorithm engineers. Bringing the power of deep learning to bear on new problems requires all three, but in this age of implementation, data is the core. That’s because once computing power and engineering talent reach a certain threshold, the quantity of data becomes decisive in determining the overall power and accuracy of an algorithm.
Given much more data, an algorithm designed by a handful of mid-level AI engineers usually outperforms one designed by a world-class deep-learning researcher. Having a monopoly on the best and the brightest just isn’t what it used to be.
The messy markets and dirty tricks of China’s “copycat” era produced some questionable companies, but they also incubated a generation of the world’s most nimble, savvy, and nose-to-the-grindstone entrepreneurs. These entrepreneurs will be the secret sauce that helps China become the first country to cash in on AI’s age of implementation.
Tying all these services together was the rise of China’s super-app, WeChat, a kind of digital Swiss Army knife for modern life. WeChat users began sending text and voice messages to friends, paying for groceries, booking doctors’ appointments, filing taxes, unlocking shared bikes, and buying plane tickets, all without ever leaving the app. WeChat became the universal social app, one in which different types of group chats—formed with coworkers and friends or around interests—were used to negotiate business deals, organize birthday parties, or discuss modern art. It brought together a grab-bag of essential functions that are scattered across a dozen apps in the United States and elsewhere.
3 – We decide where we want to get: we must rethink the concept of humanity itself
On a social and economic level, the process of diffusion of AI, or electrification in Lee’s winning metaphor, will have important and disruptive effects. If we think of the impact on jobs and employment, the social consequences can also be devastating, and we have to decide where we want to go. It is about radically rethinking the concept of humanity in a society that for millennia has built its own work ethic: get food & shelter in exchange for sweat and daily work, and have a sense of inner realization in this daily process. In a world dominated by artificial intelligence, perhaps more efficient even in non-repetitive tasks reserved for white collars (think of financial advisers or credit consultants, providing mortgages in bank offices) it will be vital to reposition our attitude towards humanity and the human being itself.
[Header Photo by Franck V. on Unsplash – edited by valeriosoldout.com]