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until recently, the typical computer processor could ping only one thing at a time.
That began to change more than a decade ago, when a new kind of chip, called a graphics processing unit, or GPU, was devised for the intensely visual—and parallel—demands of video games,
Traditional processors required several weeks to calculate all the cascading possibilities in a neural net with 100 million parameters. Ng found that a cluster of GPUs could accomplish the same thing in a day.
2. Big Data
Every intelligence has to be taught.
Even the best-programmed computer has to play at least a thousand games of chess before it gets good. Part of the AI breakthrough lies in the incredible avalanche of collected data about our world, which provides the schooling that AIs need.
3. Better Algorithms
This perfect storm of cheap parallel computation, bigger data, and deeper algorithms generated the 60-years-in-the-making overnight success of AI.
Cloud computing empowers the law of increasing returns, sometimes called the network effect, which holds that the value of a network increases much faster as it grows bigger. The bigger the network, the more attractive it is to new users, which makes it even bigger and thus more attractive, and so on.
But here’s the even more surprising part: The advent of AI didn’t diminish the performance of purely human chess players. Quite the opposite.
If AI can help humans become better chess players, it stands to reason that it can help us become better pilots, better doctors, better judges, better teachers.
robust intelligence may be a liability—especially if by “intelligence” we mean our peculiar self-awareness,
Our most premium AI services will likely be advertised as consciousness-free. Nonhuman intelligence is not a bug; it’s a feature. The most important thing to know about thinking machines is that they will think different.
as we build more and more synthetic minds we’ll come to realize that human thinking is not general at all. It is only one species of thinking.
It is not necessary that this type of thinking be faster than humans’, greater, or deeper. In some cases it will be simpler.
The variety of potential minds in the universe is vast.
A few really smart people, like astronomer Stephen Hawking and genius inventor Elon Musk, worry that making supersmart AIs could be our last invention before they replace us (though I don’t believe this), so exploring possible types is prudent.
One mind cannot do all mindful things perfectly well. A particular species of mind will be better in certain dimensions, but at a cost of lesser abilities in other dimensions.
The point of this speculative list is to emphasize that all cognition is specialized. The types of artificial minds we are making now and will make in the coming century will be designed to perform specialized tasks, and usually tasks that are beyond what we can do.
Today, many scientific discoveries require hundreds of human minds to solve, but in the near future there may be classes of problems so deep that they require hundreds of different species of minds to solve.
The scientific method is a way of knowing, but it has been based on how humans know. Once we add a new kind of intelligence into this method, science will have to know, and progress, according to the criteria of new minds. At that point everything changes.
Humans are for inventing new kinds of intelligences that biology could not evolve. Our job is to make machines that think different—to create alien intelligences. We should really call AIs “AAs,” for “artificial aliens.”
Artificial intelligence will help us better understand what we mean by intelligence in the first place.
The greatest benefit of the arrival of artificial intelligence is that AIs will help define humanity. We need AIs to tell us who we are.
It may be hard to believe, but before the end of this century, 70 percent of today’s occupations will likewise be replaced by automation—including the job you hold.
To demand that artificial intelligence be humanlike is the same flawed logic as demanding that artificial flying be birdlike, with flapping wings. Robots, too, will think different.
But as manufacturing costs sink because of robots, the costs of transportation become a far greater factor than the cost of production.
once we can cowork with robots right next to us, it’s inevitable that our tasks will bleed together, and soon our old work will become theirs—and our new work will become something we can hardly imagine.
We’ve accepted utter reliability in robot manufacturing; soon we’ll accept the fact that robots can do it better in services and knowledge work too.
Every time you click on the search button you are employing a robot to do something we as a species are unable to do alone.
We aren’t giving “good jobs” to robots. Most of the time we are giving them jobs we could never do. Without them, these jobs would remain undone.
In a very real way our inventions assign us our jobs. Each successful bit of automation generates new occupations—occupations we would not have fantasized about without the prompting of the automation.
Robots create jobs that we did not even know we wanted done.
The one thing humans can do that robots can’t (at least for a long while) is to decide what it is that humans want to do.
Everyone will have access to a personal robot, but simply owning one will not guarantee success. Rather, success will go to those who best optimize the process of working with bots and machines.
Our human assignment will be to keep making jobs for robots—and that is a task that will never be finished. So we will always have at least that one “job.”
You’ll be paid in the future based on how well you work with robots. Ninety percent of your coworkers will be unseen machines.
It is inevitable. Let the robots take our jobs, and let them help us dream up new work that matters.
The internet is the world’s largest copy machine. At its most fundamental level this machine copies every action, every character, every thought we make while we ride upon it.
If something can be copied—a song, a movie, a book—and it touches the internet, it will be copied.
The digital economy runs on this river of freely flowing copies. In fact, our digital communication network has been engineered so that copies flow with as little friction as possible.
This superdistribution system has become the foundation of our economy and wealth. The instant reduplication of data, ideas, and media underpins the major sectors of a 21st-century economy.
This total sequence of perpetual upgrades is continuous. It’s a dream come true for our insatiable human appetite: rivers of uninterrupted betterment.
The initial age of computing borrowed from the industrial age. As Marshall McLuhan observed, the first version of a new medium imitates the medium it replaces. The first commercial computers employed the metaphor of the office. Our screens had a “desktop” and “folders” and “files.”
The second digital age overturned the office metaphor and brought us the organizing principle of the web. The basic unit was no longer files but “pages.”
Now we are transitioning into the third age of computation. Pages and browsers are far less important. Today the prime units are flows and streams.
Flowing time has shifted as well. In the first era, tasks were accomplished in batch mode.
Then, in the second age, along came the web, and very quickly we expected everything the same day.
Our cycle time jumped from batch mode to daily mode.
Now in the third age, we’ve moved from daily mode to real time. If we message someone, we expect them to reply instantly. If we spend money, we expect the balance in our account to adjust in real time.
