Life 3.0: Being Human in the Age of Artificial Intelligence

by Max Tegmark

As AI gets smarter, this will involve not merely building good user interfaces for information sharing, but also figuring out how to optimally allocate tasks within human-computer teams—for example, identifying situations where control should be transferred, and for applying human judgment efficiently to the highest-value decisions rather than distracting human controllers with a flood of unimportant information.

Second, they claim that since the year 2000, an ever-larger share of corporate income has gone to those who own the companies as opposed to those who work there—and that as long as automation continues, we should expect those who own the machines to take a growing fraction of the pie. This edge of capital over labor may be particularly important for the growing digital economy,

Third, Erik and collaborators argue that the digital economy often benefits superstars over everyone else.

Recent forecasts for when various jobs will get taken over by machines identify several useful questions to ask about a career before deciding to educate oneself for it.48 For example: Does it require interacting with people and using social intelligence? Does it involve creativity and coming up with clever solutions? Does it require working in an unpredictable environment? The more of these questions you can answer with a yes, the better your career choice is likely to be.

In contrast, jobs that involve highly repetitive or structured actions in a predictable setting aren’t likely to last long before getting automated away.

Since the market salary for a job is the hourly cost of whoever or whatever will perform it most cheaply, salaries have historically dropped whenever it became possible to outsource a particular occupation to a lower-income country or to a cheap machine.

Now we’re gradually figuring out how to replace our minds by machines.

When we allow real-world systems to be controlled by AI, it’s crucial that we learn to make AI more robust, doing what we want it to do. This boils down to solving tough technical problems related to verification, validation, security and control.

Our laws need rapid updating to keep up with AI, which poses tough legal questions involving privacy, liability and regulation.

Career advice for today’s kids: Go into professions that machines are bad at—those involving people, unpredictability and creativity.

In most cases, this technology-driven trend has made large entities parts of an even grander structure while retaining much of their autonomy and individuality, although commentators have argued that adaptation of entities to hierarchical life has in some cases reduced their diversity and made them more like indistinguishable replaceable parts.

Although our present world remains stuck in a multipolar Nash equilibrium, with competing nations and multinational corporations at the top level, technology is now advanced enough that a unipolar world would probably also be a stable Nash equilibrium.

At the same time, the most fundamental driver of decentralization will remain: it’s wasteful to coordinate unnecessarily over large distances.

For superintelligent AI, the laws of physics will place firm upper limits on transportation and communication technology, making it unlikely that the highest levels of the hierarchy would be able to micromanage everything that happens on planetary and local scales.

If we don’t know what we want, we’re unlikely to get it.

What do you personally prefer, and why? Do you want there to be superintelligence? Do you want humans to still exist, be replaced, cyborgized and/or uploaded/simulated? Do you want humans or machines in control? Do you want AIs to be conscious or not? Do you want to maximize positive experiences, minimize suffering or leave this to sort itself out? Do you want life spreading into the cosmos? Do you want a civilization striving toward a greater purpose that you sympathize with, or are you OK with future life forms that appear content even if you view their goals as pointlessly banal? To help fuel such contemplation and conversation, let’s explore the broad range of scenarios summarized in table 5.1. This obviously isn’t an exhaustive list, but I’ve chosen it to span the spectrum of possibilities. We clearly don’t want to end up in the wrong endgame because of poor planning. I recommend jotting down your tentative answers to questions 1–7 and then revisiting them after reading this chapter to see if you’ve changed your mind! You can do this at http://AgeOfAi.org, where you can also compare notes and discuss with other readers.