In 1922 the mathematician Lewis Fry Richardson had imagined a large hall full of "computers", people who, one hand calculation at a time, would advance numerical weather prediction. Less than a hundred years later, machines have improved the productivity of that particular task by up to fifteen orders of magnitude, with the ability to process almost a million billion similar calculations per second.
Consider the growth in heavy labor productivity by comparison. In 2014 the world used about 500 Exajoules—a billion, billion joules—of primary energy, to produce electricity, fuel manufacturing, transport and heat. Even if we assumed all of that energy went into carrying out physical tasks in aid of the roughly 3 billion members of the global labor force (and it did not), assuming an average adult diet of 2,000 Calories per capita per day, would imply roughly 50 "energy laborers" for every human. More stringent assumptions would still lead to at most an increase of a few orders of magnitude in effective productivity of manual labor.
We have been wildly successful at accelerating our ability to think and process information, more so than any other human activity. The promise of artificial intelligence is to deliver another leap in increasing the productivity of specific cognitive functions: ones where the sophistication of the task is also orders of magnitude higher than previously possible.
Keynes would have probably argued that such an increase should ultimately lead to a fully employed society with greater free time and a higher quality of life for all. The skeptic might be forgiven for considering this a case of hope of experience. While there is no question that specific individuals will benefit enormously from delegating tasks to machines, the promise of greater idleness from automation has yet to be realized, as any modern employee—virtually handcuffed to a portable device—can attest.
So, if we are going to work more, deeper, and with greater effectiveness thanks to thinking machines, choosing wisely what they are going to be "thinking" about is particularly important. Indeed, it would be a shame to develop all this intelligence to then spend it on thinking really hard about things that do not matter. And, as ever in science, selecting problems worth solving is a harder task than figuring out how to solve them.
One area where the convergence of need, urgency, and opportunity is great is in the monitoring and management of our planetary resources. Despite the dramatic increase in cognitive and labor productivity, we have not fundamentally changed our relationship to Earth: we are still stripping it of its resources to manufacture goods that turn to waste relatively quickly, with essentially zero end-of-life value to us. A linear economy on a finite planet, with seven billion people aspiring to become consumers—our relationship to the planet is arguably more productive, but not much more intelligent than it was a hundred years ago.
Understanding what the planet is doing in response, and managing our behavior accordingly, is a complicated problem, hindered by colossal amounts of imperfect information. From climate change, to water availability, to the management of ocean resources, to the interactions between ecosystems and working landscapes, our computational approaches are often inadequate to conduct the exploratory analyses required to understand what is happening, to process the exponentially growing amount of data about the world we inhabit, and to generate and test theories of how we might do things differently.
We have almost 7 billion thinking machines on this planet already, but for the most part they don't seem to be terribly concerned with how sustainable their life on this planet actually is. Very few of those people have the ability to see the whole picture in ways that make sense to them, and those that do are often limited in their ability to respond. Adding cognitive capacity to figure out how we fundamentally alter our relationship with the planet is a problem worth thinking about.