- Gebundene Ausgabe: 272 Seiten
- Verlag: Houghton Mifflin Harcourt (25. September 2018)
- Sprache: Englisch
- ISBN-10: 132854639X
- ISBN-13: 978-1328546395
- Größe und/oder Gewicht: 15,2 x 2,4 x 22,9 cm
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AI Superpowers: China, Silicon Valley, and the New World Order (Englisch) Gebundenes Buch – 25. September 2018
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Über den Autor und weitere Mitwirkende
Dr. Kai-Fu Lee is the Chairman and CEO of Sinovation Ventures, which is a leading technology-savvy investment firm focusing on developing the next generation of Chinese high-tech companies. Prior to founding Sinovation in 2009, Dr. Lee was the President of Google China. Previously, he held executive positions at Microsoft, SGI, and Apple.
Dr. Lee received his Bachelor's degree in Computer Science from Columbia University, and his Ph.D. from Carnegie Mellon University. Dr. Lee holds honorary Doctorate Degrees from the City University of Hong Kong and Carnegie Mellon. He is also a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). Selected as one of the 100 most influential people in the world by Time Magazine in 2013, Dr. Lee has authored ten US patents, and more than one hundred journal and conference papers. He has written eight top-selling books in Chinese, and has more than 50 million followers on social media.
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The inciting incident for both Lee’s book and another comparable recent effort (Artificial Intuition: The Improbable Deep Learning Revolution, by Carlos Perez) is the recent victory in Go of an AI-based system over the best human champion of that ancient game. This had about the weight of an empty beer can in the USA and other Western news cycles, but shook the Asian intelligentsia at their core (because they care so much more about Go). Both Lee and Perez make a big deal out of the Go victory as a Sputnik moment, awakening entire East Asian populations, and their central planners, to the urgency of becoming the dominant AI superpower. Meanwhile, apart from some corporate research,
the USA snoozes blithely on. We may wake like Rip Van Winkle in 20 years (or 20 months) to find ourselves hopelessly lagging China. AI Superpowers skillfully exploits and intensifies the fear factor. A cynic would say the hidden agenda here is to trigger another 1980’s-style AI panic, when it seemed that Japan would conquer the world with their Prolog (logic programming) initiative. But I am not cynical. I appreciate this book on its own terms.
Anyway, after both books (Lee and Perez) lead off with humanity’s miserable Go game beatdown, they then diverge sharply in quality, and Lee quickly pulls way ahead of Perez. Where Perez gets lost in an impenetrable thicket of his own miserably confusing writing style and rambling topical garden paths (or garden mazes), Lee drives straight for the goal line: a clear and compelling picture of the current state of play, and a crisp delineation of where things will end up.
The depiction of China’s hi-technology business culture is the stronger element, relative to the presentation of AI as technology. Although Lee knows the tech methods and architectures inside and out, this is a popular treatment and you won’t encounter a single equation or circuit layout anywhere. Basically, you’ll get the key message that ‘narrow’ AI (task-specific systems that learn and perform well on human expert functions) has made a giant leap in a brief recent period. These systems are lumped under the term ‘deep learning’, an extension of a fairly simple neural modeling concept dating back to the 1950’s that has just now broken free of the pack and left the field behind. That breakout has been enabled by more data, more computing power, and some architectural upgrades to the original concept. Lee then zooms in on how fast and how furious the deep learning tsunami will hit.
AI Superpowers is strongest in its contrasting portraits of the Chinese high tech scene vs. the USA’s Silicon Valley. Lee offers numerous real-world cases illustrating parallels and divergences, sprinkled with entertaining personal tales from the trenches on both sides.
He traces the ascendance of China’s tech giants such as WeChat, AliBaba, and many others that aren’t household names in the West, digging deep into exactly how each one succeeded – pound for pound, blow by blow, user by user – a view from the trenches. Basically he portrays Chinese entrepreneurial high tech as sharing much in common with
organized crime, possibly minus the sicarios. (See the book Narconomics: How to Run a Drug Cartel by Tom Wainwright if you want the real dirt.)
All that leads into Lee’s clear-sighted take on the employment implications of the new AI. Lee is no Elon Musk, in that he doesn’t see AI posing any immediate existential threat to the human species. Nor does he spend much ink on the roseate Kurzweil ‘singularity’ stuff (which is essentially religious fantasy in my view). True to form, Lee
moves soberly and smoothly, like the no-nonsense businessman he is, to consider something closer to home: the possible loss or diminution of up to 40% or more of current jobs with a decade or less. Many of these threats are well known, particularly the driverless vehicles thing, automated medical radiology, and many others. Analyses of this
type go back at least as far as Jeremy Rifkin’s classic The End of Work (1996) and many more recent treatments. But Lee’s examination is particularly lucid and right up to the minute in its full attention to the new AI (deep learning).
The book then takes a personal twist as Lee details his battle with an abrupt cancer diagnosis and how the recovery ordeal opened his eyes to elements of the emotional and social landscape he’d skated lightly past in his meteoric ascent to the top of the transPacific high tech dogpiles, both Silicon Valley and Zhongguancun. Newly sensitized to the human side of life, he prescribes human-to-human (or heart-to-heart) operations as something we can turn to, a need that will persist, even when all the truck driver spots dry up. This is a really laudable aspect of the book. How many tough tech exec’s and macho VC posers would have the spine to reveal this much of themselves and lay down their pugnacious facades, to go this deep? Truly admirable and unique.
In the final section, Dr. Lee offers his prescription, which is a social do-gooder program of make-work in areas that computers still have trouble with, such as cheering lonely elders and such. Lee believes government mandated social-service jobs programs have advantages over the resurgent Universal Basic Income proposals (basically air-dropping free money on people from helicopters).
The strengths of this book are the great high-tech anecdotes and ringside recent history accounts, the straight-forward descriptions of some key technical advances, and future directions, as well as the uniquely heart-centric infusion of that emo human touch in considering palliatives for the upcoming unemployment wave.
Minor downsides include a certain Chinese cultural chauvinism. Lee is very convincing in proudly calling out all the strengths of China relative to the West (basically, the USA) in the AI’s Brave New World. But he sometimes gets a little carried away. For example, he makes more than one admiring reference to a recent Chinese sci-fi hit, Folding Beijing by Hao Jingfang, which depicts a future world of extreme class and functional stratification.
That’s an appealing effort but hardly as unique as Lee seems to believe, given that this basic scenario was chillingly and unforgettably depicted in one of the very first science fiction novels ever published (The Time Machine by H. G. Wells - a future world of the surface dwelling Elio vs. the Morlocks, ape-like troglodytes who live in darkness underground and surface only at night). That’s just one example of several where Lee’s understandable cultural pride gets the better of his basically dispassionate instincts.
He also paints too strong a contrast between Silicon Valley culture and the organized crime ethos of Zhonguancun (China’s Silicon Valley). For example, Lee writes that the Valley (USA high tech culture) despises copycats. Is this true of Larry Ellison, secretly copying the original IBM research paper on relational databases that became the signature tech of Oracle? Is it true of Steve Jobs, ripping off for his Macintosh every element demonstrated to him on the Star office system when he toured the Palo Alto Xerox research lab one day? And we all know the saga of DOS and Windows. How original was Facebook? I could go on. And on. Things are more similar than different.
Where he really has a gift is in making concepts that would be way too scary or boring to a lay reader perfectly understandable and accessible. Here, Lee really shines. For just one of many examples, consider is his seamlessly smooth rendition of a blazing hot method in current AI research, Generative Adversarial Networks (GANs). This would scare the pants off most lay readers if they encountered it in a technical book, but look how easily the medicine goes down when administered by Dr. Lee:
“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 making each other better in the process.”
Rendering this powerful frontline concept perfectly lucid in a couple dozen words – that’s a gift (but again, note the touch of Chinese chauvinism, implying that Toutiao, a Chinese company, somehow thought up this approach for their own little application, while in fact the GAN configuration is entirely the innovation of a Western AI
But enough carping. It’s a very good book and well worth reading. Now the big question opens before us: after reading this, am I worried? Am I convinced to be at least as worried (yet cautiously hopeful) as Lee himself? No, I’m not worried at all, and here’s why.
First, some background. Despite the fact that these high-tech icons and visionaries pretend to revere intelligence and genius and talent and brains and innovation and creativity and all that, secretly every one of them must know that the greatest economic resource is not intelligence at all. The greatest economic resource, by far, is stupidity. They all know this, and now you know it too. I can prove it.
Consider where the economy would be if even a few sectors such as the following were entirely removed: soft drink industry; snack foods industry; global arms industry; all religions; makeup and cosmetic industry, including weight loss and cosmetic surgery; high end luxury brands of all kinds; most video games; most movies and popular entertainment; most of the ‘financial services’ industry – need I go on? Every one of the above sectors is based almost entirely on human stupidity. Or, at a minimum, none of them could function without a solid root in human stupidity. And that’s only the start.
Now consider the ramifications of just eliminating one item on that list, say, the soft drink and snack foods industry. That alone would probably eliminate up to 50% of current health care services required by the population. So the effects would ripple out everywhere. I could go on but you get the idea. The one essential economic resource is
not intelligence at all. It is stupidity. The human economy would grind to a dead halt without it. Whether human stupidity is exploited haphazardly by existing manual methods, or (in the near future) exploited and stimulated more efficiently via AI methods is immaterial.
That’s why I don’t see any great long term threat in AI. Or if there is any threat in AI, it isn’t the economic stuff called out by Lee, it’s more likely to be the existential stuff called out by Musk and others in his camp. But we’ve put that aside for this discussion, so within the terms of Lee’s book, we can expect clear sailing. Yes, AI will continue to advance, but in the words of one famous science fiction writer: ‘the street finds its own use for things’. Humans will adapt AI to their own unceasing pursuit of profit and pleasure through organized and unorganized crime and it all will be business as usual in the long run.
The only big effect will be that the AI mavens of today, the ‘smart ones’, will probably end up displacing themselves. We can do without intelligence (see above). The crucial resource is stupidity. The co-founder of Communism, Vladimir Ilyich Lenin, once famously predicted that “when the time comes to hang the capitalists, they will rush to sell us
the rope”. Similarly, the Chinese rush to pull ahead in AI by throwing money and brains at it is likely merely accelerating the creation of their own successor species. That engineered new life form will likely put all the smart guys out of business yet retain some need for stupid feedstock, just as in the Matrix movies the AI’s ran the world but kept sleeping (stupid) humans as batteries.
But still - shouldn’t I be a bit more tremulous about the advent of our AI overlords? After all, it has been stated by one who should know that: With superintelligent computers that understand the universe on levels that humans cannot even conceive of, these machines become not just tools for lightening the burdens of humanity; they
approach the omniscience and omnipotence of a god.
Wow, AI’s will become ‘gods’. But even so, they will never be able to beat down the human race. Because we have our great ace in the hole, the one cognitive space we humans uniquely occupy, where by definition, no AI can follow. Before you hide under your bed, dig these words of wisdom:
“Against stupidity, the very gods themselves contend in vain.”
- Friedrich Schiller
Less than two months after AlphaGo's victory, China's central government issued an ambitious plan to build artificial intelligence capabilities. The plan included clear progress benchmarks for 2020 and 2025, an ultimate goal of becoming the world center of AI innovation by 2030, specifically envisioned AI playing major roles in improving/expanding Chinese healthcare and urban management (eg. security, traffic management), augmented by additional support for quantum computing and chip R&D, and new AI education initiatives in both primary and secondary schools. At the same time, in 2017 Chinese VC investors made up 48% of global AI venture funding, and surpassed the U.S. for the first time.
AI's big technical breakthrough occurred in 1986 when British researcher Geoffrey Hinton discovered how to efficiently train neural networks modeled after the human brain. It was called 'backpropagation,' and used to calculate factor weights - the centerpiece of 'deep learning' algorithms that are far easier to program and much more accurate than alternative rule-based 'expert-system' AI. (Experts 'guided' the computer's decisions by loading it with what human experts used as decision guidelines.)
Researchers since learned how to 'train' deep-learning computers to recognize faces and images, translate voice to print in real-time, operate autonomous vehicles, translate between languages, 'read' medical images, generate image captions, trade financial instruments, factory automation, colorize black-and-white images/videos, reinforcement learning, recommendation engines, grade/correct grammar, etc. Near future/in-process uses include 'transfer learning' (using a dog/cat classifier to classify eye scans in diagnosing diabetic retinopathy, and multi-task learning (sentiment, intent, and emotion detection).
The AlphaGo victory differed from IBM's Deep Blue defeat of chess champion Garry Kasparov in 1997. Deep Blue had largely relied on customized hardware to rapidly generate and evaluate positions, aided by guiding heuristics from real-life chess champions. First, the board was only 8 X 8. Second, instead of trying to teach the computer rules mastered by human experts (the 'expert-system AI approach), they simply fed it lots and lots of data - and the computer then trained itself to recognize patterns and correlations connected to the desired outcome. Third, Big Blue's processing speed was much faster than AlphaGo's.
The age of synthesizing expertise in R&D (a U.S. strength) has now been replaced with the age of data (a Chinese strength). And now we're also transitioning from the age of discovery (a U.S. strength) to the age of implementation (a Chinese strength).
AI deep-learning algorithms need big data, computing power, and strong (but not necessarily elite) AI algorithm engineers. China lead in big data, and can produce enough algorithm engineers. Computing power is the big unknown.
Elite AI researchers provide the potential to push the field to the next level, but those advances have occurred rarely - remember, 'deep learning' was invented back in 1986. Meanwhile, the availability of data will be the driving force behind AI disruptions of countless industries around the world. 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 - thus, having a monopoly on the best and the brightest just isn't as important as it used to be.
Author Lee has spent decades in both Silicon Valley and China's tech scene. He contends that Silicon Valley is a sluggish implementer compared to the Chinese, that China is the world's most cutthroat competitive environment, and copying is an accepted practice. Cutting prices to the bone, smear campaigns, forcibly uninstalling competing software, and even reporting rivals to police were common practices. They also have a fanatical around-the-clock work ethic, and see the grand prize is getting rich - rather than filling a need.
China's central government is doing everything it can to tip the scales - pledging widespread support and funding for AI research, and encouraging local governments and educators to follow suit. Conversely, the U.S. government deliberately takes a hands-off approach to entrepreneurship, is slashing funds for basic research, and has not yet adopted a 'Sputnik' response aimed at boosting AI education. On the other hand, our current 'trade war' efforts vs. China likely are at least partly aimed at impeding China's AI initiatives.
China will soon match or overtake the U.S. in developing/deploying AI - despite America's superiority in college/university training. AI will translate into productivity gains on a scale not seen since the Industrial Revolution, adding nearly $16 trillion to global GDP by 2030 - with China taking $7 trillion of that, nearly double North America's $3.7 share. Billions of jobs up and down the economic ladder will be wiped out - an estimated 40 - 50% of jobs in the U.S.
China's advantages in this AI race include: strong government support and leadership, public-school achievement levels in major urban areas that far outpace those in America, a populace already used to and accepting of surveillance, more competitive entrepreneurs, its much larger population (more data) and more integrated personal data.
Efforts to limit U.S. immigration (over half of Silicon Valley STEM workers with a bachelor's degree or above are foreign born) and H1-B visas could easily harm our AI competitiveness. Another impediment - Google employees refusing to work on defense/privacy-related projects for the U.S. government. A third - Teamster efforts to ban/limit autonomous trucks, and fuel-saving 'drafting' on Interstate highways. Others - concerns over healthcare and education data privacy, resistance to the time consumed in statewide pupil testing programs.
Ai naturally tends to create winner-take-all economics. More data leads to better products, they attract more users, that generates more data that further improves the product. This added cash also attracts top AI talent to top companies, widening the gap between leaders and laggards. Former geographical limits will be weakened by autonomous trucks and drones that dramatically slash shipping/delivery costs - and reduce previous dispersion of profits across companies and regions.
AI-driven factory automation will also undercut the one advantage developing countries possess - cheap labor. The gap between global haves and have-nots will widen.
Widespread unemployment and gaping inequality will undermine the purpose of innumerable humans.
What type of jobs are most likely to survive? Examples include medical practitioners with less diagnostic skills who are trained to empathetically deliver adverse diagnoses such as cancer, teachers trained to support computer individualized instruction systems, positions requiring creativity and/or cross-functional thinking (eg. lawyers).
I am not a native English speaker. I can hardly judge stylistic matters. As far as I'm concerned, this is a well-written book with very high density of information. It's not Hemingway or Steinbeck, but we are talking about AI here (I am saying this because of a comment made about the book from a pickier reader). I highly recommend reading that book or listening to its audio version, which is well done.