DeLong: The Future of Work: Automation and Labor: Inclusive AI: Technology and Policy for a Diverse Human Future
Thank you very much.
Let me follow the example of our Lord and Master Alpha-Go as it takes the high ground first.
Let me, therefore, take the hyper-Olympian and very long run historical point of view.
The human brain is a massively parallel supercomputer that fits inside half a shoebox. It draws 50 watts of power. It is an amazing innovation, analysis, assessment and creation machine. 600 million years of proto-mammalian and mammalian evolution coupled with the genetic algorithm means that almost every single human can solve AI problems far beyond our current engineering reach—so much so that much of what our machines find impossible our brains find so trivially easy that we call such capabilities “unskilled”.
When combined with our brains, human fingers are amazingly fine manipulation devices.
Human back and leg muscles—especially when testosterone soaked—are quite good at moving heavy objects.
Thus back in the environment of evolutionary adaptation, we used our brains, our big muscles, and our fingers to lead cognitively interesting if stressful and short lives.
But history has rolled forward since the hunter-gatherer age. And as history has rolled forward, we have figured out other things to do to add economic and sociological value than their uses in the hunger-gathers paradigm. Over the long historical sweep, the ability to add value using our backs to move heavy objects and our fingers to perform fine manipulations in cognitively-interesting ways has, relatively, declined. We have, so far:
- turned many of us into robots ourselves, performing simple routinized repetitive and vastly boring tasks to fill in the gaps in value chains between the robots that we know how to build.
- found jobs as microcontrollers for domesticated animals and machines—the horse does not know what plowing the furrow is.
- found jobs as relatively simple accounting and software bots, keeping track of stuff, what it is useful for, and how its use is to be decided.
- become personal servitors.
- become social engineers—trying to keep all those things and all those people—especially, perhaps, trying to keep those brains soaked in testosterone—somehow working in harmony, somehow pulling together, although admittedly with limited success.
- remained innovators, analyzers, assessors, and creators as well.
Backs started to go out with the domestication of the horse. Fingers began to go out with the invention of the spinning jenny. But humans-as-microcontrollers, humans-as-accounting-‘bots—paper shufflers—and humans-as-the-robots we cannot yet build—took up all the job slack. Every horse needs a microcontroller. And a human
brain was the only possible option. Even today, to a large amount every textile machine needs a human watching it at least part of the time. It doesn’t know when it’s gone wrong. It has no clue how to fix itself. It no more understands the idea of “fixing” any more than Alpha-Go understands that it is playing Go, and not just solving a problem of outputting a two-element vector in response to a 19 x 19 matrix of inputs with the additional structure that the output changes the matrix and that the possible matrices have a value-function structure.
Now, however, we can finally peer into a future in which the microcontrollers and the accounting bots are on their way out in a manner analogous to the backs and the fingers. Fortunately, this brings with it the forthcoming extinction of the the jobs that treat humans as simple robots: simple cogs in the machine that is Henry Ford’s River Rouge assembly line. Many occupations that vastly underutilize the massively parallel supercomputer that fits in half a shoebox are on the way out—and good: for those are not properly “human” jobs at all.
That leaves us with a future of work—not next year, and not next decade, but further out by some unknown time—in which humans’ jobs will be as:
- personal servitors,
- social engineers, and
- innovators, analyzers, assessors’ nd creators.
And here we might well, someday, have a huge problem.
The market economy will amply fund AI research that replaces workers in capital intensive production processes by machines. Such industries have mammoth returns to scale. They thus tend to be characterized by large oligopolies. And so the firm that funds such labor-replacing research will capture with its own scale and in its own value chain a substantial part of the benefits of such R&D. But the market economy will to amply fund AI research that assists and amplifies workers in labor intensive production processes. Such tend to be small scale. The inventors and the innovators cannot capture even a small part of the benefit in their own production processes and value chains. And intellectual property is a very weak reed indeed to rely on to fix the problem—in fact, intellectual property is more likely to be the problem than the solution, cf. Nathan Myhrvold, and Intellectual Ventures.
That means that the combination of coming AI with a market economy will be absolute poison for equity and equitable growth. It will race ahead with the first: shedding workers in capital intensive production processes. Yet AI could be gold for equity: amplifying the capabilities of workers in labor intensive production processes would, as John Maynard Keynes once said, bring us vastly closer to economic El Dorado.
Utopia or dystopia? Heaven or hell? I turn that over to you. And by “you”, I definitely include our engineering dean Shankar Sastry. Because firms will not invest on a large scale in AI that amplifies the capabilities of labor in labor intensive industries, it will not happen unless some NGO does. How about an engineering school? How about an engineering school like an engineering school at a public university?
And let me stop there.
As prepared for delivery:
Inclusive AI: Technology and Policy for a Diverse Urban Future https://www.eventbrite.com/e/inclusive-ai-technology-and-policy-for-a-diverse-urban-future-tickets-31896895473: Wed, May 10, 2017 10:30 AM – 5:30 PM
Panel 3: The Future of Work: Automation and Labor
- Ken Goldberg
- Brad DeLong,
- James Manyika
- Costas Spanos
- Laura Tyson
- John Zysman
Since I get to go first, I will preemptively take the hyper-Olympian and very long-run historical point of view…
The human brain is a massively parallel supercomputer that fits in half a shoebox. It draws 50 W of power. 600 million years of proto- and mammalian evolution mean that almost every single human can solve AI problems that our machines cannot—what our machines find very hard or impossible, our brains find so trivially easy that we call such capabilities “unskilled”.
Human fingers are amazingly fine manipulation devices. Human back and leg muscles—especially when testosterone soaked—are quite good at moving heavy objects. And so, back in the environment of evolutionary adaptation, we used our brains, big muscles, and fingers to lead interesting, if stressful and short, lives.
But as history has enrolled we have done other things to add economic and sociological value than use our backs, our fingers, and our brains to innovate and create. Over the long historical sweep, backs and fingers have declined and we have turned many of us into, instead:
- robots performing repetitive tasks,
- microcontrollers for domesticated animals and machines,
- relatively simple accounting and recording software bots,
- personal servitors,
- social engineers trying to keep all those things controlled by brains—especially by the testosterone soaked ones—working together harmoniously. With limited success.
while remaining innovators and creators.
Backs started to go out with the domestication of the horse. Fingers with the invention of the spinning jenny. Microcontrollers and accounting ‘bots, we can see, are now on the way out too. So, fortunately, are the jobs that treat humans as simple robots.
That leaves us with a future of work made up of:
- personal servitors,
- social engineers,
- innovators and creators.
The market economy will fund AI that replaces workers in capital-intensive production processes. Such are large scale and oligopolistic: firms profit from R&D because they capture a significant portion of efficiencies in their value chains. There is no equivalent market force funding AI that assists and amplifies workers in labor-intensive production processes.
The first is poison for equity and inclusion. The second is gold.
That second is one thing this NGO institution that surrounds us would be good at doing, and needs to do.
Utopia or dystopia? Heaven or hell?
Over to you, James. And, in a broader sense, over to all of you—in the audience, and out there in Internet land.