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3.95 AVERAGE
3.95 AVERAGE
i can't believe i made myself suffer through 46 entire pages written by a venture capitalist who honestly believes the problem with silicon valley is that the companies are "mission driven" and not profit driven before i finally stopped trying to give it a chance.
Warning for the US. But so far...it’s just Tik Tok. The personal story weaved in was jarring.
This book is a great introduction to AI for anyone new to the subject, as well as a great way to expand your previous knowledge if you are already somewhat comfortable with the topic.
Kai-Fu Lee writes about artificial intelligence in a natural and flowing manner, and gives some great insights into the impact AI will have on our world and the way we live our lives. He also manages to introduce the topic of his own life experiences and decisive moments in his life (primarily of his cancer diagnosis) in a very elegant way, which can really take you from the seriousness of the topic to a heartfelt moment in a heartbeat.
Overall I think this is a great read and would definitely recommend it to anyone interested in the topic.
Kai-Fu Lee writes about artificial intelligence in a natural and flowing manner, and gives some great insights into the impact AI will have on our world and the way we live our lives. He also manages to introduce the topic of his own life experiences and decisive moments in his life (primarily of his cancer diagnosis) in a very elegant way, which can really take you from the seriousness of the topic to a heartfelt moment in a heartbeat.
Overall I think this is a great read and would definitely recommend it to anyone interested in the topic.
This was fascinating, frightening, and touching. Part tech reporting, part memoir, part prophetic. I learned a lot while enjoying this book. Recommended.
Provides a good overview of what AI is, how it's evolving and how it's changing our world, now and in the near future. Always focusing on the differences between the 2 major contenders in the field, the US and China.
The author has a clear bias toward China and the book has a bit of a taste of propaganda.
In the final chapters the author describes how a life-changing situation changed his views regarding the future of society and the role of AI. But it didn't quite fix his bias...
The author has a clear bias toward China and the book has a bit of a taste of propaganda.
In the final chapters the author describes how a life-changing situation changed his views regarding the future of society and the role of AI. But it didn't quite fix his bias...
informative
medium-paced
A thoughtful reflection on the current state of AI in the US and China by someone close to it.
Highlights
- the insight into China's startup culture and how it contrasts with the United States'. Chinese companies tend to "go heavy", investing in and controlling all parts of the user experience. American companies tend to go light. Good stories and examples - Groupon, Danping, Yelp, Meituan, etc.
- the stages of AI - discovery vs exploration and implementation - and why China has an edge over the USA in the implementation
- Analysis on what the growth of AI could mean outside of US and China
- The idea of a social stipend - in addition to retraining, reducing work hours or re-educating workers
Lowlights
- Kai Fu Lee appears convinced of the benefits of China's startups' approach. It's not clear to me from the examples he provides. The "light" or "heavy" models are market-specific. For example, it is likely more expensive and less valuable to run a heavy model in the US vs, say China.
- There's a romantic view of AI towards the end - a vision of humans co-existing with AI. Agreeable in theory, but market forces are likely to take charge here. It will be the battle of the billionaires all over again, as it was for the railroads.
Highlights
- the insight into China's startup culture and how it contrasts with the United States'. Chinese companies tend to "go heavy", investing in and controlling all parts of the user experience. American companies tend to go light. Good stories and examples - Groupon, Danping, Yelp, Meituan, etc.
- the stages of AI - discovery vs exploration and implementation - and why China has an edge over the USA in the implementation
- Analysis on what the growth of AI could mean outside of US and China
- The idea of a social stipend - in addition to retraining, reducing work hours or re-educating workers
Lowlights
- Kai Fu Lee appears convinced of the benefits of China's startups' approach. It's not clear to me from the examples he provides. The "light" or "heavy" models are market-specific. For example, it is likely more expensive and less valuable to run a heavy model in the US vs, say China.
- There's a romantic view of AI towards the end - a vision of humans co-existing with AI. Agreeable in theory, but market forces are likely to take charge here. It will be the battle of the billionaires all over again, as it was for the railroads.
This book is terrible. It's a propaganda piece for the Chinese government and also a puff piece for the author. If you enjoy reading all about how wonderful he is at being a venture capitalist, and how China's gathering of data about people's personal habits and shopping preferences is the next best thing to happen to AI, then you'll love this book. It's a plodding, celebratory hack job that never once questions the way that the Chinese government treats its citizens or asks if it is right that their entrepreneurial environment includes getting free rent from the government while using their police to get your competitors thrown in jail.
Me ha parecido interesantísimo este ensayo sobre el futuro de la IA (Inteligencia Artificial), tratada como industria y no como maravilla tecnológica.
El autor comienza aludiendo al Sputnik moment para los chinos, cundo se dieron cuenta como país (como gobierno) de que estaban quedándose atrás en el desarrollo de la IA y que tenían que ponerse las pilas.
Según el autor (que fue investigador jefe de Apple para reconocimiento de voz y luego jefe de Google China, nada menos), el gran avance científico de la IA ya está hecho y ahora falta que miles de empresas de ingeniería se pongan manos a la obra para implementar técnicamente ese avance. Y que mientras EE.UU tiene aún un liderazgo en la investigación pura, China es mejor en meter en mercado estas novedades, y acabará adelantando a los EE.UU. en el campo de la IA.
Pero esto es solo el primer capítulo. El autor habla luego de las cuatro principales arenas donde la inteligencia artificial se hará con el contro l absoluto de la productividad:
Y predice que el impacto en el mercado de trabajo, por la amplitud y sobre todo por el ataque multisectorial a tantos puestos de trabajo distintos, será como ninguno antes presenciado:
Hay un capítulo intermedio contando cómo el autor sobrevivió a un cáncer, lo que le hizo tomarse la vida de otra manera, y termina proponiendo una solución para el masivo desempleo que se viene. actualmente hay tres soluciones propuestas: formarse en otra cosa, trabajar menos horas y la Renta Básica Universal:
El autor propone "el contrato social", mediante el que el Estado pagará a todas a quellas personas que se dediquen a actividades que solo los humanos pueden hacer (cuidado de personas, enseñanza...)
En conjunto, muy interesante y muy bien estructurado. Muy recomendable. No pongo más citas porque he llegado al límite de los 10.000 caracteres de Goodreads.
El autor comienza aludiendo al Sputnik moment para los chinos, cundo se dieron cuenta como país (como gobierno) de que estaban quedándose atrás en el desarrollo de la IA y que tenían que ponerse las pilas.
Deep learning’s big technical break finally arrived in the mid-2000s, when leading researcher Geoffrey Hinton discovered a way to efficiently train those new layers in neural networks.
[...] The turning point came in 2012, when a neural network built by Hinton’s team demolished the competition in an international computer vision contest.
[...] 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.
[...]
Según el autor (que fue investigador jefe de Apple para reconocimiento de voz y luego jefe de Google China, nada menos), el gran avance científico de la IA ya está hecho y ahora falta que miles de empresas de ingeniería se pongan manos a la obra para implementar técnicamente ese avance. Y que mientras EE.UU tiene aún un liderazgo en la investigación pura, China es mejor en meter en mercado estas novedades, y acabará adelantando a los EE.UU. en el campo de la IA.
[like] 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.
[...]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.
[...]Both of the transitions described on the previous pages—from discovery to implementation, and from expertise to data—now tilt the playing field toward China.
[...] Silicon Valley’s entrepreneurs have earned a reputation as some of the hardest working in America, passionate young founders who pull all-nighters in a mad dash to get a product out, and then obsessively iterate that product while seeking out the next big thing. Entrepreneurs there do indeed work hard. But I’ve spent decades deeply embedded in both Silicon Valley and China’s tech scene, working at Apple, Microsoft, and Google before incubating and investing in dozens of Chinese startups. I can tell you that Silicon Valley looks downright sluggish compared to its competitor across the Pacific.
[...] Putting all these pieces together—the dual transitions into the age of implementation and the age of data, China’s world-class entrepreneurs and proactive government—I believe that China will soon match or even overtake the United States in developing and deploying artificial intelligence.
[...] I predict that within fifteen years, artificial intelligence will technically be able to replace around 40 to 50 percent of jobs in the United States.
[...] Following Thomas Edison’s harnessing of electricity, the field rapidly shifted from invention to implementation. Thousands of engineers began tinkering with electricity, using it to power new devices and reorganize industrial processes. Those tinkerers didn’t have to break new ground like Edison. They just had to know enough about how electricity worked to turn its power into useful and profitable machines. Our present phase of AI implementation fits this latter model. A constant stream of headlines about the latest task tackled by AI gives us the mistaken sense that we are living through an age of discovery, a time when the Enrico Fermis of the world determine the balance of power. In reality, we are witnessing the application of one fundamental breakthrough—deep learning and related techniques—to many different problems. That’s a process that requires well-trained AI scientists, the 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.
Pero esto es solo el primer capítulo. El autor habla luego de las cuatro principales arenas donde la inteligencia artificial se hará con el contro l absoluto de la productividad:
The complete AI revolution will take a little time and will ultimately wash over us in a series of four waves: internet AI, business AI, perception AI, and autonomous AI. Each of these waves harnesses AI’s power in a different way, disrupting different sectors and weaving artificial intelligence deeper into the fabric of our daily lives. The first two waves—internet AI and business AI—are already all around us, reshaping our digital and financial worlds in ways we can barely register. They are tightening internet companies’ grip on our attention, replacing paralegals with algorithms, trading stocks, and diagnosing illnesses. 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. These four waves all feed off different kinds of data, and each one presents a unique opportunity for the United States or China to seize the lead. We’ll see that China is in a strong position to lead or co-lead in internet AI and perception AI, and will likely soon catch up with the United States in autonomous AI. Currently, business AI remains the only arena in which the United States maintains clear leadership.
outiao’s AI engines trawl the internet for content, using natural-language processing and computer vision to digest articles and videos from a vast network of partner sites and commissioned contributors. It then uses the past behavior of its users—their clicks, reads, views, comments, and so on—to curate a highly personalized newsfeed tailored to each person’s interests. The app’s algorithms even rewrite headlines to optimize for user clicks. And the more those users click, the better Toutiao becomes at recommending precisely the content they want to see.
Business AI mines these databases for hidden correlations that often escape the naked eye and human brain. It draws on all the historic decisions and outcomes within an organization and uses labeled data to train an algorithm that can outperform even the most experienced human practitioners. 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. For example, in predicting the likelihood of someone contracting diabetes, a person’s weight and body mass index are strong features. AI algorithms do indeed factor in these strong features, but they also look at thousands of other weak features: peripheral data points that might appear unrelated to the outcome but contain some predictive power when combined across tens of millions of examples. These subtle correlations are often impossible for any human to explain in terms of cause and effect: why do borrowers who take out loans on a Wednesday repay those loans faster? But algorithms that can combine thousands of those weak and strong features—often using complex mathematical relationships indecipherable to a human brain—will outperform even top-notch humans at many analytical business tasks.
As a result, perception AI is beginning to blur the lines separating the online and offline worlds. It does that by dramatically expanding the nodes through which we interact with the internet. Before perception AI, our interactions with the online world had to squeeze through two very narrow chokepoints: the keyboards on our computers or the screen on our smartphones. Those devices act as portals to the vast knowledge stored on the world wide web, but they are a very clunky way to input or retrieve information, especially when you’re out shopping or driving in the real world. As perception AI gets better at recognizing our faces, understanding our voices, and seeing the world around us, it will add millions of seamless points of contact between the online and offline worlds. Those nodes will be so pervasive that it no longer makes sense to think of oneself as “going online.” When you order a full meal just by speaking a sentence from your couch, are you online or offline? When your refrigerator at home tells your shopping cart at the store that you’re out of milk, are you moving through a physical world or a digital one? I call these new blended environments OMO: online-merge-offline.
These kinds of immersive OMO scenarios go far beyond shopping. These same techniques—visual identification, speech recognition, creation of a detailed profile based on one’s past behavior—can be used to create a highly tailored experience in education. 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. But AI can help us lift those limitations. 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.
But Chinese officials aren’t just adapting existing roads to autonomous vehicles. They’re building entirely new cities around the technology. Sixty miles south of Beijing sits the Xiong’an New Area, a collection of sleepy villages where the central government has ordered the construction of a showcase city for technological progress and environmental sustainability. The city is projected to take in $583 billion worth of infrastructure spending and reach a population of 2.5 million, nearly as many people as Chicago. The idea of building a new Chicago from the ground up is fairly unthinkable in the United States, but in China it’s just one piece of the government’s urban planning toolkit.
Y predice que el impacto en el mercado de trabajo, por la amplitud y sobre todo por el ataque multisectorial a tantos puestos de trabajo distintos, será como ninguno antes presenciado:
Although timelines for these capabilities vary widely, Bostrom’s book presents surveys of AI researchers, giving a median prediction of 2040 for the creation of AGI [Artificial General Intelligence], with superintelligence likely to follow within three decades of that.
Beyond direct job losses, artificial intelligence will exacerbate global economic inequality. By giving robots the power of sight and the ability to move autonomously, AI will revolutionize manufacturing, putting third-world sweatshops stocked with armies of low-wage workers out of business. In doing so, it will cut away the bottom rungs on the ladder of economic development. It will deprive poor countries of the opportunity to kick-start economic growth through low-cost exports, the one proven route that has lifted countries like South Korea, China, and Singapore out of poverty.
Despite the best efforts and protests of the Luddites, industrialization plowed full steam ahead,
“The answer is surely not to try to stop technical change,” Summers told the New York Times in 2014, “but the answer is not to just suppose that everything’s going to be O.K. because the magic of the market will assure that’s true.”
Unlike the GPTs of the first and second Industrial Revolutions, AI will not facilitate the deskilling of economic production. It won’t take advanced tasks done by a small number of people and break them down further for a larger number of low-skill workers to do. Instead, it will simply take over the execution of tasks that meet two criteria: they can be optimized using data, and they do not require social interaction.
Displaced workers can theoretically transition into other industries that are more difficult to automate, but this is itself a highly disruptive process that will take a long time.
s a technology and an industry, AI naturally gravitates toward monopolies. Its reliance on data for improvement creates a self-perpetuating cycle: better products lead to more users, those users lead to more data, and that data leads to even better products, and thus more users and data. Once a company has jumped out to an early lead, this kind of ongoing repeating cycle can turn that lead into an insurmountable barrier to entry for other firms.
With manufacturing and services increasingly done by intelligent machines located in the AI superpowers, developing countries will lose the one competitive edge that their predecessors used to kick-start development: low-wage factory labor. Large populations of young people used to be these countries’ greatest strengths. But in the age of AI, that group will be made up of displaced workers unable to find economically productive work. This sea change will transform them from an engine of growth to a liability on the public ledger—and a potentially explosive one if their governments prove unable to meet their demands for a better life.
When we scan the economic horizon, we see that artificial intelligence promises to produce wealth on a scale never before seen in human history—something that should be a cause for celebration. But if left to its own devices, AI will also produce a global distribution of wealth that is not just more unequal but hopelessly so. AI-poor countries will find themselves unable to get a grip on the ladder of economic development, relegated to permanent subservient status. AI-rich countries will amass great wealth but also witness the widespread monopolization of the economy and a labor market divided into economic castes. Make no mistake: this is not just the normal churn of capitalism’s creative destruction, a process that has previously helped lead to a new equilibrium of more jobs, higher wages, and a better quality of life for all. The free market is supposed to be self-correcting, but these self-correcting mechanisms break down in an economy driven by artificial intelligence. Low-cost labor provides no edge over machines, and data-driven monopolies are forever self-reinforcing. These forces are combining to create a unique historical phenomenon, one that will shake the foundations of our labor markets, economies, and societies. Even if the most dire predictions of job losses don’t fully materialize, the social impact of wrenching inequality could be just as traumatic.
Hay un capítulo intermedio contando cómo el autor sobrevivió a un cáncer, lo que le hizo tomarse la vida de otra manera, y termina proponiendo una solución para el masivo desempleo que se viene. actualmente hay tres soluciones propuestas: formarse en otra cosa, trabajar menos horas y la Renta Básica Universal:
THE THREE R’S: REDUCE, RETRAIN, AND 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. Each of these approaches aims to augment a different variable within the labor markets (skills, time, compensation) and also embodies different assumption about the speed and severity of job losses. 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. Instead, we will have to adopt more radical redistribution schemes to support unemployed workers and spread the wealth created by AI. Next, I will take a closer look at the value and pitfalls of each of these approaches.
El autor propone "el contrato social", mediante el que el Estado pagará a todas a quellas personas que se dediquen a actividades que solo los humanos pueden hacer (cuidado de personas, enseñanza...)
LOOKING FORWARD AND LOOKING AROUND The ideas laid out in this chapter are an early attempt to grapple with the massive disruptions on the horizon of our AI future. We looked at technical fixes that seek to smooth the transition to an AI economy: retraining workers, reducing work hours, and redistributing income through a UBI. While all of these technical fixes have a role to play, I believe something more is needed. I envision the private sector creatively fostering human-machine symbiosis, a new wave of impact investing funding human-centric service jobs, and the government filling the gaps with a social investment stipend that rewards care, service, and education. Taken together, these would constitute a realignment of our economy and a rewriting of our social contract to reward socially productive activities.
In this sense, our current AI boom shares far more with the dawn of the Industrial Revolution or the invention of electricity than with the Cold War arms race. Yes, Chinese and American companies will compete with each other to better leverage this technology for productivity gains. But they are not seeking the conquest of the other nation. [...]
En conjunto, muy interesante y muy bien estructurado. Muy recomendable. No pongo más citas porque he llegado al límite de los 10.000 caracteres de Goodreads.