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Giving People Income Without Jobs

So who’s right: those who say automated jobs will be replaced by better ones or those who say most humans will end up unemployable? If AI progress continues unabated, then both sides might be right: one in the short term and the other in the long term. But although people often discuss the disappearance of jobs with doom-and-gloom connotations, it doesn’t have to be a bad thing! Luddites obsessed about particular jobs, neglecting the possibility that other jobs might provide the same social value. Analogously, perhaps those who obsess about jobs today are being too narrow-minded: we want jobs because they can provide us with income and purpose, but given the opulence of resources produced by machines, it should be possible to find alternative ways of providing both the income and the purpose without jobs. Something similar ended up happening in the equine story, which didn’t end with all horses going extinct. Instead, the number of horses has more than tripled since 1960, as they were protected by an equine social-welfare system of sorts: even though they couldn’t pay their own bills, people decided to take care of horses, keeping them around for fun, sport and companionship. Can we similarly take care of our fellow humans in need?

Let’s start with the question of income: redistributing merely a small share of the growing economic pie should enable everyone to become better off. Many argue that we not only can but should do this. On the 2016 panel where Moshe Vardi spoke of a moral imperative to save lives with AI-powered technology, I argued that it’s also a moral imperative to advocate for its beneficial use, including sharing the wealth. Erik Brynjolfsson, also a panelist, said that “if with all this new wealth generation, we can’t even prevent half of all people from getting worse off, then shame on us!” There are many different proposals for wealth-sharing, each with its supporters and detractors. The simplest is basic income, where every person receives a monthly payment with no preconditions or requirements whatsoever. A number of small-scale experiments are now being tried or planned, for example in Canada, Finland and the Netherlands. Advocates argue that basic income is more efficient than alternatives such as welfare payments to the needy, because it eliminates the administrative hassle of determining who qualifies. Need-based welfare payments have also been criticized for disincentivizing work, but this of course becomes irrelevant in a jobless future where nobody works.

Governments can help their citizens not only by giving them money, but also by providing them with free or subsidized services such as roads, bridges, parks, public transportation, childcare, education, healthcare, retirement homes and internet access; indeed, many governments already provide most of these services. As opposed to basic income, such government-funded services accomplish two separate goals: they reduce people’s cost of living and also provide jobs. Even in a future where machines can outperform humans at all jobs, governments could opt to pay people to work in childcare, eldercare, etc. rather than outsource the caregiving to robots.

Interestingly, technological progress can end up providing many valuable products and services for free even without government intervention. For example, people used to pay for encyclopedias, atlases, sending letters and making phone calls, but now anyone with an internet connection gets access to all these things at no cost—together with free videoconferencing, photo sharing, social media, online courses and countless other new services. Many other things that can be highly valuable to a person, say a lifesaving course of antibiotics, have become extremely cheap. So thanks to technology, even many poor people today have access to things that the world’s richest people lacked in the past. Some take this to mean that the income needed for a decent life is dropping.

If machines can one day produce all current goods and services at minimal cost, then there’s clearly enough wealth to make everyone better off. In other words, even relatively modest taxes could then allow governments to pay for basic income and free services. But the fact that wealth-sharing can happen obviously doesn’t mean that it will happen, and today there’s strong political disagreement about whether it even should happen. As we saw above, the current trend in the United States appears to be in the opposite direction, with some groups of people getting poorer decade after decade. Policy decisions about how to share society’s growing wealth will impact everybody, so the conversation about what sort of future economy to build should include everyone, not merely AI researchers, roboticists and economists.

Many debaters argue that reducing income inequality is a good idea not merely in an AI-dominated future, but also today. Although the main argument tends to be a moral one, there’s also evidence that greater equality makes democracy work better: when there’s a large well-educated middle class, the electorate is harder to manipulate, and it’s tougher for small numbers of people or companies to buy undue influence over the government. A better democracy can in turn enable a better-managed economy that’s less corrupt, more efficient and faster growing, ultimately benefiting essentially everyone.

Giving People Purpose Without Jobs

Jobs can provide people with more than just money. Voltaire wrote in 1759 that “work keeps at bay three great evils: boredom, vice and need.” Conversely, providing people with income isn’t enough to guarantee their well-being. Roman emperors provided both bread and circuses to keep their underlings content, and Jesus emphasized non-material needs in the Bible quote “Man shall not live by bread alone.” So precisely what valuable things do jobs contribute beyond money, and in what alternative ways can a jobless society provide them?

The answers to these questions are obviously complicated, since some people hate their jobs and others love them. Moreover, many children, students and homemakers thrive without jobs, while history teems with stories of spoiled heirs and princes who succumbed to ennui and depression. A 2012 meta-analysis showed that unemployment tends to have negative long-term effects on well-being, while retirement was a mixed bag with both positive and negative aspects.56 The growing field of positive psychology has identified a number of factors that boost people’s sense of well-being and purpose, and found that some (but not all!) jobs can provide many of them, for example:57 •a social network of friends and colleagues

•a healthy and virtuous lifestyle

•respect, self-esteem, self-efficacy and a pleasurable sense of “flow” stemming from doing something one is good at

•a sense of being needed and making a difference

•a sense of meaning from being part of and serving something larger than oneself

This gives reason for optimism, since all of these things can be provided also outside of the workplace, for example through sports, hobbies and learning, and with families, friends, teams, clubs, community groups, schools, religious and humanist organizations, political movements and other institutions. To create a low-employment society that flourishes rather than degenerates into self-destructive behavior, we therefore need to understand how to help such well-being-inducing activities thrive. The quest for such an understanding needs to involve not only scientists and economists, but also psychologists, sociologists and educators. If serious efforts are put into creating well-being for all, funded by part of the wealth that future AI generates, then society should be able to flourish like never before. At a minimum, it should be possible to make everyone as happy as if they had their personal dream job, but once one breaks free of the constraint that everyone’s activities must generate income, the sky’s the limit.

Human-Level Intelligence?

We’ve explored in this chapter how AI has the potential to greatly improve our lives in the near term, as long as we plan ahead and avoid various pitfalls. But what about the longer term? Will AI progress eventually stagnate due to insurmountable obstacles, or will AI researchers ultimately succeed in their original goal of building human-level artificial general intelligence? We saw in the previous chapter how the laws of physics allow suitable clumps of matter to remember, compute and learn, and how they don’t prohibit such clumps from one day doing so with greater intelligence than the matter clumps in our heads. If/when we humans will succeed in building such superhuman AGI is much less clear. We saw in the first chapter that we simply don’t know yet, since the world’s leading AI experts are divided, most of them making estimates ranging from decades to centuries and some even guessing never. Forecasting is tough because, when you’re exploring uncharted territory, you don’t know how many mountains separate you from your destination. Typically you see only the closest one, and need to climb it before you can discover your next obstacle.

What’s the soonest it could happen? Even if we knew the best possible way to build human-level AGI using today’s computer hardware, which we don’t, we’d still need to have enough of it to provide the raw computational power needed. So what’s the computational power of a human brain measured in the bits and FLOPS from chapter 2?*4 This is a delightfully tricky question, and the answer depends dramatically on how we ask it: •Question 1: How many FLOPS are needed to simulate a brain?

•Question 2: How many FLOPS are needed for human intelligence?

•Question 3: How many FLOPS can a human brain perform?

There have been lots of papers published on question 1, and they typically give answers in the ballpark of a hundred petaFLOPS, i.e., 1017 FLOPS.58 That’s about the same computational power as the Sunway TaihuLight (figure 3.7), the world’s fastest supercomputer in 2016, which cost about $300 million. Even if we knew how to use it to simulate the brain of a highly skilled worker, we would only profit from having the simulation do this person’s job if we could rent the TaihuLight for less than her hourly salary. We may need to pay even more, because many scientists believe that to accurately replicate the intelligence of a brain, we can’t treat it as a mathematically simplified neural-network model from chapter 2. Perhaps we instead need to simulate it at the level of individual molecules or even subatomic particles, which would require dramatically more FLOPS.

The answer to question 3 is easier: I’m painfully bad at multiplying 19-digit numbers, and it would take me many minutes even if you let me borrow pencil and paper. That would clock me in below 0.01 FLOPS—a whopping 19 orders of magnitude below the answer to question 1! The reason for the huge discrepancy is that brains and supercomputers are optimized for extremely different tasks. We get a similar discrepancy between these questions: How well can a tractor do the work of a Formula One race car?

How well can a Formula One car do the work of a tractor?

So which of these two questions about FLOPS are we trying to answer to forecast the future of AI? Neither! If we wanted to simulate a human brain, we’d care about question 1, but to build human-level AGI, what matters is instead the one in the middle: question 2. Nobody knows its answer yet, but it may well be significantly cheaper than simulating a brain if we either adapt the software to be better matched to today’s computers or build more brain-like hardware (rapid progress is being made on so-called neuromorphic chips).

Hans Moravec estimated the answer by making an apples-to-apples comparison for a computation that both our brain and today’s computers can do efficiently: certain low-level image-processing tasks that a human retina performs in the back of the eyeball before sending its results to the brain via the optic nerve.59 He figured that replicating a retina’s computations on a conventional computer requires about a billion FLOPS and that the whole brain does about ten thousand times more computation than a retina (based on comparing volumes and numbers of neurons), so that the computational capacity of the brain is around 1013 FLOPS—roughly the power of an optimized $1,000 computer in 2015!

In summary, there’s absolutely no guarantee that we’ll manage to build human-level AGI in our lifetime—or ever. But there’s also no watertight argument that we won’t. There’s no longer a strong argument that we lack enough hardware firepower or that it will be too expensive. We don’t know how far we are from the finish line in terms of architectures, algorithms and software, but current progress is swift and the challenges are being tackled by a rapidly growing global community of talented AI researchers. In other words, we can’t dismiss the possibility that AGI will eventually reach human levels and beyond. Let’s therefore devote the next chapter to exploring this possibility and what it might lead to!

THE BOTTOM LINE:

•Near-term AI progress has the potential to greatly improve our lives in myriad ways, from making our personal lives, power grids and financial markets more efficient to saving lives with self-driving cars, surgical bots and AI diagnosis systems.

•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.

•This need for improved robustness is particularly pressing for AI-controlled weapon systems, where the stakes can be huge.

•Many leading AI researchers and roboticists have called for an international treaty banning certain kinds of autonomous weapons, to avoid an out-of-control arms race that could end up making convenient assassination machines available to everybody with a full wallet and an axe to grind.

•AI can make our legal systems more fair and efficient if we can figure out how to make robojudges transparent and unbiased.

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

•Long before we need to worry about intelligent machines replacing us altogether, they may increasingly replace us on the job market.

•This need not be a bad thing, as long as society redistributes a fraction of the AI-created wealth to make everyone better off.

•Otherwise, many economists argue, inequality will greatly increase.

•With advance planning, a low-employment society should be able to flourish not only financially, with people getting their sense of purpose from activities other than jobs.

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

•There’s a non-negligible possibility that AGI progress will proceed to human levels and beyond—we’ll explore that in the next chapter!