Top 15 Kai-Fu Lee Quotes

Part of why predicting the ending to our AI [artificial intelligence] story is so difficult is because this isn’t just a story about machines. It’s also a story about human beings, people with free wills that allows them to make their own choices and to shape their own destinies. Our AI future will be created by us, and it will reflect the choices we make and the actions we take.

In stark contrast, China’s startup culture is the yin to Silicon Valley’s yang: instead of being mission-driven, Chinese companies are first and foremost market-driven. Their ultimate goal is to make money, and they’re willing to create any product, adopt any model, or go into any business that will accomplish that objective. That mentality leads to incredible flexibility in business models and execution, a perfect distillation of the “lean startup” model often praised in Silicon Valley. It doesn’t matter where an idea came from or who came up with it. All that matters is whether you can execute it to make a financial profit. The core motivation for China’s market-driven entrepreneurs is not fame, glory, or changing the world. Those things are all nice side benefits, but the grand prize is getting rich, and it doesn’t matter how you get there.

AI ever allows us to truly understand ourselves, it will not be because these algorithms captured the mechanical essence of the human mind. It will be because they liberated us to forget about optimizations and to instead focus on what truly makes us human: loving and being loved.

In deep learning, there’s no data like more data. The more examples of a given phenomenon a network is exposed to, the more accurately it can pick out patterns and identify things in the real world.

the invention of deep learning means that we are moving from the age of expertise to the age of data. Training successful deep-learning algorithms requires computing power, technical talent, and lots of data. But of those three, it is the volume of data that will be the most important going forward. That’s because once technical talent reaches a certain threshold, it begins to show diminishing returns. Beyond that point, data makes all the difference. Algorithms tuned by an average engineer can outperform those built by the world’s leading experts if the average engineer has access to far more data.

Algorithms tuned by an average engineer can outperform those built by the world’s leading experts

birthplace and heritage are not the sole determinants of behavior.

Algorithms tuned by an average engineer can outperform those built by the world’s leading experts if the average engineer has access to far more data.

Of the hundreds of companies pouring resources into AI research, let’s return to the seven that have emerged as the new giants of corporate AI research — Google, Facebook, Amazon, Microsoft, Baidu, Alibaba, and Tencent.

Throw in the valley’s rich history of computer science breakthroughs, and you’ve set the stage for the geeky-hippie hybrid ideology that has long defined Silicon Valley. Central to that ideology is a wide-eyed techno-optimism, a belief that every person and company can truly change the world through innovative thinking. Copying ideas or product features is frowned upon as a betrayal of the zeitgeist and an act that is beneath the moral code of a true entrepreneur. It’s all about “pure” innovation, creating a totally original product that generates what Steve Jobs called a “dent in the universe.” Startups that grow up in this kind of environment tend to be mission-driven. They start with a novel idea or idealistic goal, and they build a company around that. Company mission statements are clean and lofty, detached from earthly concerns or financial motivations.

Cash has disappeared so quickly from Chinese cities that it even “disrupted” crime. In March 2017, a pair of Chinese cousins made headlines with a hapless string of robberies. The pair had traveled to Hangzhou, a wealthy city and home to Alibaba, with the goal of making a couple of lucrative scores and then skipping town. Armed with two knives, the cousins robbed three consecutive convenience stores only to find that the owners had almost no cash to hand over — virtually all their customers were now paying directly with their phones. Their crime spree netted them around $125 each — not even enough to cover their travel to and from Hangzhou — when police picked them up. Local media reported rumors that upon arrest one of the brothers cried out, “How is there no cash left in Hangzhou?

Can you imagine the elation that comes from beating a world champion at the game you’ve devoted your whole life to mastering? AlphaGo did just that, but it took no pleasure in its success, felt no happiness from winning, and had no desire to hug a loved one after its victory.

Realizing the newfound promise of electrification a century ago required four key inputs: fossil fuels to generate it, entrepreneurs to build new businesses around it, electrical engineers to manipulate it, and a supportive government to develop the underlying public infrastructure. 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.

IT IS BETTER TO LIVE YOUR OWN DESTINY IMPERFECTLY THAN TO IMITATE SOMEBODY ELSE’S PERFECTLY.

RENEWABLE ENERGY REVOLUTION: SOLAR + WIND + BATTERIES In addition to AI, we are on the cusp of another important technological revolution — renewable energy. Together, solar photovoltaic, wind power, and lithium-ion battery storage technologies will create the capability of replacing most if not all of our energy infrastructure with renewable clean energy. By 2041, much of the developed world and some developing countries will be primarily powered by solar and wind. The cost of solar energy dropped 82 percent from 2010 to 2020, while the cost of wind energy dropped 46 percent. Solar and onshore wind are now the cheapest sources of electricity. In addition, lithium-ion battery storage cost has dropped 87 percent from 2010 to 2020. It will drop further thanks to the massive production of batteries for electrical vehicles. This rapid drop in the price of battery storage will make it possible to store the solar/wind energy from sunny and windy days for future use. Think tank RethinkX estimates that with a $2 trillion investment through 2030, the cost of energy in the United States will drop to 3 cents per kilowatt-hour, less than one-quarter of today’s cost. By 2041, it should be even lower, as the prices of these three components continue to descend. What happens on days when a given area’s battery energy storage is full — will any generated energy left unused be wasted? RethinkX predicts that these circumstances will create a new class of energy called “super power” at essentially zero cost, usually during the sunniest or most windy days. With intelligent scheduling, this “super power” can be used for non-time-sensitive applications such as charging batteries of idle cars, water desalination and treatment, waste recycling, metal refining, carbon removal, blockchain consensus algorithms, AI drug discovery, and manufacturing activities whose costs are energy-driven. Such a system would not only dramatically decrease energy cost, but also power new applications and inven