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?
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.
…But unlike many of the optimists, I do not find that similarity fully comforting, as technological advances in recent decades have brought tremendous benefits but have also contributed to increasing inequality and falling labor force participation. However, as I will emphasize this morning, the effects of technological change on the workforce are mediated by a wide set of institutions, and as such, policy choices will have a major impact on actual outcomes. AI does not call for a completely new paradigm for economic policy—for example, as advocated by proponents of replacing the existing social safety net with a universal basic income (UBI) —but instead reinforces many of the steps we should already be taking to make sure that growth is shared more broadly. But before turning to concerns about some of the possible side effects from AI, I want to start with the biggest worry I have about it: that we do not have enough of AI. Our first, second and third reactions to just about any innovation should be to cheer it—and ask how we get more of it, the issue I will discuss first in my remarks. But I will then discuss the potential labor market downsides of AI. Finally, I will conclude with the role of public policy in addressing these issues…
…for men… 24 and 54… similar trends… elsewhere…. It is a real puzzle to observe simultaneously multi-year trends of rising non-employment of low-skilled workers and declining measured productivity growth. Either we need a new understanding, or one of these observed patterns is ill-founded or misleading…. Unless we can somehow transform that sustained lower demand for workers into the widespread leisure of the sort imagined by Keynes and some science fiction writers, with the income redistribution to support it, I would think this is very bad news for social stability and technological progress….
Unmeasured quality improvement… [the] fraction of the economy… [susceptible] has been rising, so the amount of mismeasurement (and therefore productivity understatement) would be rising…. [Thus] inflation is lower than even its currently low level–and that has the consequence that real interest rates are higher, so monetary policy at present is tighter… [and] farther away from its mandated inflation target…
Recessions in the OECD… in most cases the level of GDP is lower five to ten years afterward than any prerecession forecast or trend…. “The classic model of cyclical fluctuations… around the given trend is not the right model…. The preoccupation of macroeconomics should be on lower frequency fluctuations that have consequences over long periods of time….
Discussing… Abenomics’ results… I asked whether a message we should take from the Japanese experience is to avoid bad states of the economy at almost any cost…. [And] the very language we use to speak of business cycles, of trend growth rates, of recoveries of to those perhaps non-stationary trends, and so on–which reflects the underlying mental framework of most macroeconomists–would have to be rethought.
Must-Read: Just what is it that makes some personal service jobs–personal-care attendant, housekeeper–low-status and Low-pay, while keeping others–investment manager who fails to beat the indexes–high status and high pay?
Charles Arthur: Artificial Intelligence: “‘Homo sapiens will be split into a handful of gods…
…and the rest of us’. A new report suggests that the marriage of AI and robotics could replace so many jobs that the era of mass employment could come to an end… a new 300-page report from Bank of America Merrill Lynch… promises robot carers for an ageing population… [and] huge numbers of jobs being wiped out: up to 35% of all workers in the UK and 47% of those in the US, including white-collar jobs, seeing their livelihoods taken away by machines…. “The poster child for automation is agriculture,” says Calum Chace, author of Surviving AI and the novel Pandora’s Brain. “In 1900, 40% of the US labour force worked in agriculture. By 1960, the figure was a few per cent. And yet people had jobs; the nature of the jobs had changed. But then again, there were 21 million horses in the US in 1900. By 1960, there were just three million. The difference was that humans have cognitive skills–we could learn to do new things. But that might not always be the case as machines get smarter and smarter”….
Lawyers who used to slog through giant files for the “discovery” phase of a trial can turn it over to a computer…. Foxconn… aims to replace much of its workforce with automated systems…. Carl Benedikt Frey… points out that even while some jobs are replaced, new ones spring up that focus more on services and interaction with and between people. “The fastest-growing occupations in the past five years are all related to services,” he tells the Observer. “The two biggest are Zumba instructor and personal trainer.” Frey observes that technology is leading to a rarification of leading-edge employment, where fewer and fewer people have the necessary skills to work in the frontline of its advances… technology doesn’t create that many new jobs now compared to the past….. There will be people who own the AI, and therefore own everything else,” he says. “Which means homo sapiens will be split into a handful of ‘gods’, and then the rest of us…
Peter Leyden: For those of you who do not know Brad DeLong, he is a professor of economics here at U.C. Berkeley and has been so for a while. He also did a stint at the U.S. Treasury Department in the 1990s in the Clinton administration, working under Larry Summers—which has gotten some stories behind that one…
Brad DeLong: As Gene Sperling once said: “Being Larry’s friend is never dull!”
Peter Leyden: “Is never dull.” Exactly. But I think most people outside of those circles know of him through his weblog, in which he delves deeply into economics and politics. He is quite prolific. He is quite into social media, which he is probably still banging at right now, as he sits on stage. So, Brad, one of the themes that has emerged here—particularly in some of the conversations I have had here, but also through the whole day—is a sense of technology, artificial intelligence, automation, robotics. We had drones here. There is a lot of sense of the how the technology is pushing us in different directions, and kind of disrupting life as we know it. It is continuing to push and disrupt it, and will potentially displace a lot of folks in the economy to come.
Peter Leyden: And so I think, just to open it up, given your economist’s perspective, why don’t you give us just a sense of how you think of the disruption that comes from a lot of these new technologies.
Brad DeLong: First, the industrial disruption has been ongoing for 225 years, at least. The pace at which people have been disrupted has been accelerating, yes. At the start of the eighteenth century the amount of technological progress we get in one year took twenty. By the start of the nineteenth century it was down to what we see in one year they saw in five. By the start of the twentieth century it was down to one in two.
But it has produced enormous dislocations for all 225 years.
Andrew Carnegie’s father was sitting at home in Scotland making a pretty-good living as a skilled handloom weaver. All of a sudden technological improvements three hundred miles south in the form of the power loom destroys his livelihood. I don’t remember whether he starves to death or whether simply his children’s immune systems are so badly compromised by poor nourishment that they die like flies. But Andrew makes it to America. He promptly gets an entry-level job in the high-tech industry of that day as one of the first telegraph operators, one of the first people who makes it their business to sit in front of their telegraph and communicate via the code of Samuel Morse with others across hundreds of miles at lightspeed. And we are off and running.
This has been going on for quite a while. What has done most to illuminate in my mind, with the force of a thousand atomic bombs, was an article—an article that I was discussing this morning with other economists up at the QualComm Cafe on the Berkeley campus—a Wired article of long ago, an article by Neal Stephenson about the submarine telegraph cables of the nineteenth century, a brilliant article called “Mother Earth, Motherboard”. I just discovered in the green room that Peter edited it. And I must say that to edit Neal Stephenson so that not only is every paragraph a diamond of prose but the thing has a proper beginning, middle, and end—that demonstrates true genius.
Peter Leyden: Thank you.
Brad DeLong: If you want to take the really long sweep of history, the argument is this: Up until 6000 years ago by and large the kind of things that we invented were things that allowed us to use all of our human capabilities to do what we had done but do what we did better and more effectively. Spears allow us to hunt large animals—as opposed to throwing rocks at rabbits and hoping you get a lucky hit. Picking the pieces of grass that have really big seeds—what we turn into wheat—allows us to harvest and gather a lot more calories in our daily gathering if we have been lucky or smart enough to have scattered some of the seeds by the riverbank the year before. The invention of the loom allows us to actually weave grasses into cloth much more effectively. But we are are using all of our standard paleolithic human capacities to do so.
Then, in 4000 BC or so, something different happens, something unusual. We domesticate the horse. All of a sudden having people pull things is economically obsolete. Strong human backs and strong thighs are very useful whenever you have big things to move around. But once you have got a horse, a horse can do it better. Horses are much more useful. Horses make human backs and human thighs technologically obsolete as far as moving heavy objects is concerned.
Thus over the past six thousand years, the argument continues, first slowly and then more rapidly, we have had more and more places where things that used to be in the province of human excellence become activities that our draft animals, our domesticated animals, and our machines can and are doing better. The horse takes care of backs and thighs. We get the spinning jenny and the assembly line. They largely take care of fingers—of fine manipulation. We are no longer economically competitive moving things around with our big muscles or, for the most past, finely-manipulating things with our small muscles and nimble fingers. I find that on this iPhone here, in terms of nagging me to actually move around, the Withings App is significantly better than asking somebody to tell me to move around once an hour—not least because the Withings App does not have feelings of its own—not yet. And I cannot snap at the iPhone no matter how many frowny faces it shows me.
Peter Leyden: But do you think that the next generation—the AI brainpower robotics—will take it to the next level? Do you think there is any material difference in this?
Brad DeLong: Up until now, it has been the case that, every time we have domesticated an animal or invented a machine, it has removed the market value from some human excellences. But every such animal or machine or device is not intelligent. Every one requires a cybernetic control mechanism. The human brain is a supercomputer that fits in a breadbox and draws only 50W of power. That is a very impressive cybernetic control mechanism. And so—up until now—whenever you have a horse-guiding task or machine-running task or a machine-programming task or an accounting task, you had to have a human brain in the loop to control what the machines and what the software and what the animals were actually going to do. Now, however, for the first time, we can dimly envision the coming of an age in which machines will be smart enough to run themselves. They will no longer need human minders in the loop to control them.
We already know that a simple computer with the proper big-data regression underneath it could do a significantly better job at choosing which people to admit as graduate students in economics who are likely to succeed. And the faculty committees we currently hand this task to do not do that good a job. Faculty committees are always struck by stories that resonate with them. And such stories always lead them to place too-high a weight on replicating themselves in the next generation of professors, and giving too high a weight to the recommendations from their friends in their social network. The computer is an intelligence, vast and cool and unsympathetic, that does not suffer from such biases. We have reached the stage where it can crunch the data as well as—better than—I can.
Perhaps we are approaching “peak human”. Our last remaining really-strong comparative advantage was the ability of our brains to serve as cybernetic control mechanisms for dumb animals and dumb machines. Perhaps that is coming to an end.
Peter Leyden: But that does not seem to worry you. We were chatting about this before. A lot of that is taken away. How can this play out?
Brad DeLong: There are two roads: First is the road in which we genuinely have Turing-class machines and software assistants that can do for us everything that a human can do. They will serve as super-intelligent Jeeveses to our more-or-less inept [Bertie Woosters29. They will keep the trains running. They will keep us—with our inept bumbling lack of knowledge—from creating chaos and catastrophe. They will keep us from alienating our rich Aunt Agathas from whom we hope for large legacy inheritances, plus low-interest liquidity in the meantime. That road is very much that of the science-fiction novels of the alas!, late genius Iain M. Banks. In his “Culture” universe, every person has a robotic artificially-intelligent personal drone that follows them around and makes sure that their life doesn’t crash into chaos. And the drones—smarter than the humans—do this more-or-less as a hobby. It amuses them. It gives them something to do in the real world, while they use the rest of their brain power to do whatever else they want to do communicating with the other AIs and carrying out whatever projects the AIs have.
Brad DeLong: Second is the road that is well-marked not by science-fiction novels but rather by Regency Romance novels. Down this road, it is Regency Romances that present us with the image of our own future. In the works of Georgette Heyer—riffing off of Jane Austen in a peculiar way—wrote about a social class in a condition of material comfort that had absolutely no productive economic role to perform whatsoever. Even in Austen, neither Mr. Bingley nor Mr. Darcy have or ever will do a lick of socially-productive work in their lives in return for their £5000 or £10000 a year in income, respectively. And nobody expects either of them to a lick of socially-productive work. And everybody thinks that they are wonderful people because they have inherited £5000 or £10000 a year. They are good masters. They will bring you a basket down from the manor house if you are sick. Maybe they will forgive your rent for two months if you break a leg.
This is a society of material abundance for the upper class. Thus the entire narrative force of privation—of desperately trying to get the crops in before the hail smashes them or the grasshoppers eat them so the family of the Little House on the Prairie can survive The Long Winter—is absent. Material scarcity vanishes. So what do people then do? Well, look at what’s displayed at the supermarket checkout line. What people are interested in are: first, avoiding violent death, especially for their children; second, material subsistence, comfort, and fashion; and, third, who’s sleeping with whom. If you manage to greatly reduce the risks of the first and take worry about finding material subsistence away, what you are left with as the primary motive springs of human action and society are:
The social dance that decides who is going to sleep with whom.
The display of human excellence and the acquisition of status via the appreciation and exercise of comfort and fashion.
This is the Regency Romance world. Everyone in it—everyone in the Bon Ton of England in 1820—appears to be very happy engaging in this world. Wearing the right coat, wangling an invitation to Almacks, spending two hours a day tying their cravat so that it looks like they tied it carelessly in a hurry and yet it came out fine, choosing a gown color that compliments rather than clashes with their eyes. Combine that with the great mating-and-affection dance. The characters in Regency Romances manage to keep themselves very busy and occupied indeed. They do not feel like their lives are empty.
Peter Leyden: That assumes, of course, that society allowed for the very top to act like that. If we had this more mechanized society that would take care of material wants, the economy would have to be reorganized differently. In the near term, however, how do you deal with placing people? There has been some creative thinking about that. From the right, we have seen proposals for guaranteed incomes and other things that would essentially liberate people from the spur of material necessity and its trauma.
Brad DeLong: If not—if people have to earn their daily bread by the sweat of their brow by doing something economically-valuable—then we have an immense problem. We have needed a guaranteed income here in the North Atlantic since 1800 or so. Whenever we have not had a social-insurance system, the results of technological change in producing social terror and distress have been enormous. And we economists have more often than not been the bad guys on this.
My most unfavorite line from a nineteenth-century economist comes from Alexis de Tocqueville’s friend Nassau Senior, the first Professor of Political Economy at Oxford. I was, in fact, just an hour ago reciting this line to one of our brand-new assistant professors here at Berkeley, the brilliant young Danny Yagan, who we have been very lucky to hire. Senior was well-known for taking the position that the government of the United Kingdom should not spend any money relieving the distress of Andrew Carnegie’s father and the other handloom weavers whose livelihoods had collapsed out from underneath them with the invention of the power loom. Why not? Because the spur of material privation was necessary to induce them to shift occupations and find other jobs. And if you fed them in idleness to keep them from dire material deprivation and possible death, they wouldn’t search so hard for work. It would take them longer to find other jobs. And in the end the government would waste a great deal of money on outdoor relief without diminishing the total sum of misery created by technological displacement. Misery was the spur needed to induce people to get on their bikes and look for jobs.
The story is this: The Irish Potato Famine created by monoculture and blight stuck. The six million people of Ireland start to starve. It’s pretty clear that the comfortably-sustainable population of Ireland given mid-nineteenth century technology is more like four million or so. One million people, we think, die in the course of the Irish Potato famine. Classicist Benjamin Jowett, Master of Balliol College, distressed, asks Senior about what is going on—how disastrous will it be. Senior replies: “A million Irishmen will die—and that is not nearly enough.”
Peter Leyden: Let’s say we want…
Brad DeLong: Senior says: “We need another million to die to get Ireland down to a comfortably-sustainable population of four million.” What you should say is: We should give them an income—Britain is rich enough to pay. Or: We should move them to Britain, where there are plenty of jobs. Or: We should pay to ship them to Australia, Argentina, Canada, the United States—where there is a great deal of land that can be farmed, of trees that can be cut to build houses, a great deal of work in general to be done productively. People are useful and ingenious. You should give them the power and ability to be so—rather than concluding that they are social waste.
Peter Leyden: Now, this guaranteed income. It is not just a progressive thing. There are roots in conservative thinking too. There is some possibility that…
Brad DeLong: Well… There is, but that was an earlier generation of conservatives than we have here and now. There were roots in conservative thinking. Milton Friedman was always a very big backer of a simple negative income tax—something like the Earned Income Tax Credit we currently have, but more generous and not tied to your having a job. The one that Russell Long started and that Bill Clinton expanded his this property: you have to have a job, you have to work to receive it. Friedman thought it was profoundly undignified and unfree for people to have to justify to the welfare office or the IRS why they qualified for their benefit check. The overwhelming proportion of what we produce, he thought, was the joint collective product of everyone who has come before us and handed us our knowledge. That is our collective inheritance. That has all been given us for free by our predecessors, starting even before the people of Catal Huyuk noticed that the plant that was to become wheat had a really big and tasty seed, continuing with the guy named Ish-Baal or whatever in Phoenecia in 1200 BC who saw that a stylized picture of an ox could represent the phoneme “b” and thus invented the alphabet, on down to here and now. A good society, Friedman thought, would be a relatively unequal society, but it would not have a bottom extreme of dire poverty and people who were unfree because of the harsh spur of absolute material necessity.
But that was an earlier generation of conservatives.
Brad DeLong: That is vanishing from the right. That is, especially, vanishing from the right if the people who are kept out of poverty by social insurance are the wrong kind of people.
Consider what I saw crossing my desk last week. I was in Kansas City, MO, just across State Line Road from Samuel Brownback’s Kansas. Governor Brownback denounced the liberal churches of Kansas and the meager and powerless Democratic Party of Kansas for pushing for Kansas to expand Medicaid. Medicaid expansion is, at the state level, a true no-brainer. The people of Kansas are paying taxes to the federal government for Medicaid expansion all over the country. If they don’t expand Medicaid in Kansas, their tax money will go to pay for medical care for the poor and disabled and elderly disabled here in California and in New York and in Colorado and Arkansas and Illinois, and now Pennsylvania. If they do expand Medicaid they get value back for those federal taxes they are going to pay anyway. As long as Medicaid does not make its recipients sicker and the doctors, nurses, and hospitals who collect it worse off—which it does not—it is a true no-brainer.
Yet Brownback said that he was not going to do it. Why not? Because Medicaid expansion was Barack Obama’s Trojan Horse to keep alive “big city” hospitals that ought to close, and that were going to going to close.
Now, first, this is false. The big-city hospitals of Kansas City, KS, of Topeka, and of Wichita are in better shape than the rural hospitals.
It is small rural hospitals that are going to close. It’s small rural hospitals that white people go to that are under threat. But the only argument Brownback could think to make sotto voce was that Medicaid expansion gives free stuff to urban people who carry ghetto blasters. They are the ones who are going to benefit. And, Brownback hints to his audience of supporters: “We really don’t like that, do we?”
It is scary out on the prairie.
Peter Leyden: We do not have a lot of time here. And there are some interesting questions here. We have been talking about the long-term displacement from technology through a big picture lens. Right now, however, the pressing issue around here now is income inequality. This idea of our politics being trapped, and unable to deal with this. Any thoughts on what could be done relatively quickly, knowing what we know now or what we need to know soon, to shift gears on this and make some substantial progress?
Brad DeLong: First: higher taxes on the rich; more benefits for the poor. That is the first and most obvious plan. We have the least progressive tax-and-transfer system in the North Atlantic. There is no reason why we should. We are still one of the richest. So we should have a somewhat more progressive tax-and-transfer system than the average. We do not.
Second: Back at the start of the 1970s, I think we made a large collective mistake in deciding that we should charge for public colleges. At the time, that decision make some sense. People who are going to college colleges and graduate wind up being richer than average. Why should you tax the average taxpayer in order to subsidize the education of those who are going to, say, Berkeley who will be substantially richer than the average? That makes little sense—or so we thought back in the 1970s. The upper middle class do not need more subsidies.
Third: We have an enormous problem with figuring out how to work our technology. George Eastman was a marvelous inventor and innovator. He produced Kodak as we knew it, and brought middle-class prosperity to 50,000 engineers and to the surrounding city of Rochester New York for generations. Larry Page and Sergei Brin also had truly genius ideas. They grabbed Eric Schmidt to make their company run smoothly—who had grown up a lot since his days writing the Berkeley UNIX clone in the basement of Evans Hall. But Google has not produced broad-based middle-class prosperity for its workers anywhere. It has created a much-smaller group of very, very well-paid engineers, plus a few billionaires. Why did high-tech do one thing in the case of Kodak and another thing in the case of Google? Hell if I know. I wish I did.
Peter Leyden: It could be a very different kind of technology. Unfortunately, we have run out of time. We could probe your brain for a long time here. He gave us a lot of food for thought that we can continue to think about for the rest of the conference for the next couple of days. Thank you.
…and will still be true in ten years’ time…. (1) We have no f—ing clue how to simulate a brain: We can’t simulate the brain of C. Elegans, a very well studied roundworm (first animal to have its genome sequenced) in which every animal has exactly the same 302-neuron brain…. Pretty much whatever data you want, we can generate it. And yet we don’t know how this brain works. Simply put, data does not equal understanding…. (2) We have no f—ing clue how to wire up a brain: Ok, we do have a macroscopic clue… with a resolution of one cubic millimeter per voxel… [but] any map with cubic-millimeter voxels is a very coarse map indeed. And microscopically, we have no clue…. Imagine taking statistics on the connectivity of transistors in a Pentium chip and then trying to make your own chip based on those statistics. There’s just no way it’s gonna work.
(3) We have no f—ing clue what makes human brains work so well:… There’s a guy whose brain is mostly not there, and he was probably one of the dumber kids in class, but still he functions fine in human society (has a job, family, etc.). Is this surprising? Not surprising? How would we know…. So, the next time you see a pretty 3D picture of many neurons being simulated, think ‘cargo cult brain’. That simulation isn’t gonna think any more than the cargo cult planes are gonna fly. The reason is the same in both cases: We have no clue about what principles allow the real machine to operate. We can only create pretty things that are superficially similar…
…and the frontiers are elsewhere… into higher-level abstract reasoning and tasks that require some mixture of creativity, and intuition, and expertise, and also moving downward into jobs that require physical dexterity and some cognitive flexibility. It’s not that we’ve reached the limits of… computerisation… it’s just that that particular strand that has been so important has not completely played out but is not the frontier…. A lot of the computerisation has been most evident in the so-called ‘middle-skill activities’ and that process is to a substantial extent complete, but that means that it will move into other activities. Whether that’s worse or better depends very much on who you are…. It corresponds to rising productivity…. It has distributional consequences…. If you’re doing a job and all of a sudden a machine can do it cheaper, that’s almost never going to be a good thing for you. If you’re doing a job and a machine makes you more productive at doing that work, that’s almost always a good thing for you…
