Andrew Mcafee Quotes

Don’t anthropomorphize computers—they hate it.

Because we humans are so fond of our judgment, and so overconfident in it, many of us, if not most, will be too quick to override the computers, even when their answer is better.

[T]he key to winning the race is not to compete against machines but to compete with machines.

Good decisions are critical to well-functioning societies:

Getting rid of human judgments altogether—even those from highly experienced and credentialed people—and relying solely on numbers plugged into formulas, often yields better results.

As more and more work is done by machines, people can spend more time on other activities. Not just leisure and amusements, but also on the deeper satisfactions that come from invention and exploration, from creativity and building, and from love, friendship, and community. ... If the first machine age helped unlock the forces of energy trapped in chemical bonds to reshape the physical world, the real promise of the second machine age is to help unleash the power of human ingenuity.

Algorithms are simplifications; they can't and don't take everything into account (like a billionaire uncle who has included the applicant in his will and likes to rock-climb without ropes).

Our generation will likely have the good fortune to experience two of the most amazing events in history: the creation of true machine intelligence and the connection of all humans via a common a common digital network, transforming the planet's economics. Innovators, entrepreneurs, scientists, tinkerers, and many other types of geeks will take advantage of this cornucopia to build technologies that astonish us, delight us, and work for us. Over and over again they will show how right Arthur C Clarke was when he observed that a sufficiently advanced technology can be indistinguishable from magic

Americans believe that they still live in the land of opportunity - the country that offers the greatest chance of economic advancement. But this is no longer the case.

We humans build machines o do things that we see being done in the world by animals and people, but we typically don't build them the same way that nature built us. As AI trailblazer Frederick Jelineck put it beautifully, "Airplanes don't flap their wings.

We humans build machines to do things that we see being done in the world by animals and people, but we typically don't build them the same way that nature built us. As AI trailblazer Frederick Jelineck put it beautifully, "Airplanes don't flap their wings.

It's a fundamental principle of management: what gets measured gets done.

No matter how bright the light is, you won't find your keys by searching under a lamppost if that's not where you lost them.

Sometimes ones man's creativity is another machine's brute force analysis

growth is on the way, argue technology optimists such as Erik Brynjolfsson and Andrew McAfee: thanks to the exponential growth in digital processing power, we are entering the ‘second machine age’ in which the fast-rising productivity of robots will drive a new wave of GDP growth.

company’s existing high-speed Internet connections. Might these resources be valuable to people who wanted to build a database, application, website, or other digital resource but didn’t want to go through the trouble of maintaining all required hardware and software themselves? Amazon decided to find out and launched Amazon Web Services in 2006. It originally offered storage (Amazon S3) and computing (Amazon EC2) services on the platform.

People will continue to be critically important in the improved health care delivery systems of the future, but not always in the same roles as today. Emotionally and socially astute care coordinators, rather than brilliant diagnosticians and other HiPPOs, might move to center stage. Earlier, we told the old joke about the two employees—person and dog—in the factory of the future. We suggest a slight tweak for health care: the medical office of the future might employ an artificial intelligence, a person, and a dog. The AI’s job will be to diagnose the patient, the person’s job will be to understand and communicate the diagnosis, and to coach the patient through treatment, and the dog’s job will be to bite the person if the person tries to second-guess the artificial intelligence.

As Erik Brynjolfsson and Andrew McAfee point out in their book, the four key measures of an economy’s health (per capita GDP, labor productivity, the number of jobs, and median household income) all rose together for most of the Cold War years. “For more than three decades after World War II, all four went up steadily and in almost perfect lockstep,” Brynjolfsson noted in a June 2015 interview with the Harvard Business Review. “Job growth and wage growth, in other words, kept pace with gains in output and productivity. American workers not only created more wealth but also captured a proportional share of the gains.” In

Meanwhile, Singapore has implemented an Electronic Road Pricing System that has virtually eliminated congestion. Americans collectively spend over one hundred billion hours stuck in traffic jams, a testament to the fact that road pricing is not yet widely adopted.

The best solutions- probably, in fact, the only real solutions-to the labor force chal- lenges that will arise in the future will come from markets and capitalism, and from the technology-enabled creations of innovators and entrepreneurs.

Another huge advantage that humans have is good old common sense.

Our world is increasingly complex, often chaotic, and always fast-flowing. This makes forecasting something between tremendously difficult and actually impossible, with a strong shift toward the latter as timescales get longer.

Owners of premium brands can charge more for their offerings, but the owners of two-sided networks want to pay to sellers as little as possible of the money they take in from buyers. The result is an obvious tension. Many platforms, especially when they’re new and trying to build volume and network effects, want to have on board at least one prestigious brand. But as platforms grow, they want to keep more of the consumer’s share of both mind and wallet.

The platform can also use the extensive tool kit of revenue management techniques to shape which suppliers each buyer sees, and how prominently. It’s not too cynical to expect that a platform might use this power to feature lesser-known suppliers over more famous ones, all else being equal.

Pricing strategies for two-sided networks can be aggressive and seemingly nonsensical if you don’t understand their peculiar economics. In particular, changes in the quantity demanded on one side of the network can affect demand on the other side of the network...Lowering the price on one side of the network increases demand on both sides of the network, creating an extra benefit for each price cut.

He’s been responsible for multiple advances in neural networks, one of which essentially renamed the field. His 2006 paper “A Fast Learning Algorithm for Deep Belief Nets,” coauthored with Simon Osindero and Yee-Whye Teh, demonstrated that sufficiently powerful and properly configured neural networks could essentially learn on their own, with no human training or supervision.

I’ve found that the book’s fundamental concept—that capitalism and tech progress are now allowing us to tread more lightly on the earth instead of stripping it bare—is hard for many people to accept.

As I said in the previous chapter, all of the rich countries that meet my definition of capitalist have welfare systems that include support for the poor and unemployed, subsidized health care for at least some groups, child and elder care, and so on. Advanced capitalist countries have tremendous variations in their social safety nets—Norway’s, for example, is very different from America’s—but all such countries have one.

A study published in 2017 by researchers Christian Schmidt, Tobias Krauth, and Stephan Wagner found that 88–95 percent of all plastic garbage that flowed into the world’s oceans from rivers came from just ten of them, of which eight were in Asia and two in Africa. The developed economies of North America and Europe were as a group contributing little to the problem of river-sourced plastic trash in our oceans.

My story is basic economic theory, phrased in the language of the four horsemen of the optimist: capitalism, tech progress, responsive government, and public awareness. The story is that in recent years capitalism and tech progress have combined not only to increase human prosperity but also to bring us post-peak in resource consumption in America and other rich countries and finally allow us to get more from less. This happened because resources cost money, profit-seeking competitors don’t want to spend that money if they don’t have to, and tech progress now offers them many ways to slim, swap, evaporate, and optimize their way out of using resources. As a result we continue to consume more, but our consumption is now dematerializing. We are entering a Second Enlightenment. But

As recently as the time of Christ all of us humans together probably weighed only about two-thirds as much as all the bison in North America, and less than one-eighth as much as all the elephants in Africa.

The geek way leans into arguments and loathes bureaucracy. It favors iteration over planning, shuns coordination, and tolerates some chaos. Its practitioners are vocal and egalitarian, and they’re not afraid to fail, challenge the boss, or be proven wrong. Instead of respecting hierarchy and credentials, they respect helpfulness and chops.

The successful companies of the second machine age will be those that bring together minds and machines, products and platforms, and the core and crowd very differently than most do today.

There’s an old joke that the factory of the future will have two employees: a human and a dog. The human’s job will be to feed the dog, and the dog’s job will be to keep the human from touching any of the machines. Is that actually what the company of tomorrow will look like?

Don’t anthropomorphize computers—they hate it.” ¶

Electrification was one of the most disruptive technologies ever; in the first decades of the twentieth century, it caused something close to a mass extinction in US manufacturing industries.

As more data become available and as the economy continues to change, the ability to ask the right questions will become even more vital. No matter how bright the light is, you won’t find your keys by searching under a lamppost if that’s not where you lost them.

What should employees do once technologies like enterprise software and the World Wide Web free them from the “paperwork mine”? Hammer and Champy offered a clear answer in Reengineering the Corporation: with the computers handling the routine, people should be empowered to exercise their judgment. “Most of the checking, reconciling, waiting, monitoring, tracking—the unproductive work . . .—is eliminated by reengineering. . . . People working in a reengineered process are, of necessity, empowered. As process team workers they are both permitted and required to think, interact, use judgment, and make decisions.

In 2006, Avinash Kaushik and Ronny Kohavi, two data analysis professionals who were then working at Intuit and Microsoft, respectively, came up with the acronym HiPPO to summarize the dominant decision-making style at most companies. It stands for “highest-paid person’s opinion.” We love this shorthand and use it a lot, because it vividly illustrates the standard partnership. Even when the decisions are not made by the highest-paid people, they’re often—too often—based on opinions, judgments, intuition, gut, and System 1. The evidence is clear that this approach frequently doesn’t work well, and that HiPPOs too often destroy value.

radically simplify[ing] the environment in which machines work to enable autonomous operation, as in the familiar example of a factory assembly line.