The computing revolution that secretly began in 1776

Computing didn’t begin with electronics or genius breakthroughs. It began as a practical response to chart immense amounts of stars, land, and trade activity. 

Although computers feel like a very recent breakthrough, the computing revolution actually began in 1776. Let David Alan Grier explain.

DAVID ALAN GRIER: How can you mechanize mathematics? What can you do with it? Those lessons were first approached in largely the 18th century and by people doing work by hand. Very, very quickly, people started asking, what can you do with machines? Hi, I'm David Alan Grier. I am currently a writer and author on issues of technology and industry and things of that sort. I am the author of the book "When Computers Were Human" and also the book "Crowd Sourcing for Dummies," among others. [MUSIC PLAYING] Why is computing part of the Industrial Revolution? The Industrial Revolution is about systematizing production. And it's about producing goods of uniform quality, if not uniform design, at the lowest possible cost for the largest possible market. If you want a date that's easy to remember and just nails things down, you go with 1776. And that's useful for my purpose as a writer, because that's also the year that Adam Smith's "The Wealth of Nations" is published. And the start of that book is about the description of industrial processes, how we came to them, and how we used them to start building uniform products that would have large markets that would increase the wealth of nations. And those first chapters deal with the division of labor, the specializations of tasks, and the systematization of work. That book was highly influential not only in the industrial group, particularly in London, in the cotton producing and in the pottery producing fields in northern London. But also amongst the scientific crowd, because they also had things that were large problems that needed systematic approaches. Astronomy was the first. We had used astronomy for navigation. But there was a question of what was out there, and how did things behave that was being addressed by people purchasing telescopes or financing telescopes, and then setting up a staff to collect observations. And they would night after night go to the observatory and map the heavens. And in the process of mapping the heavens, it doesn't take long to realize the data problem they generated. Suppose it takes a minute or two a night to get one star located. OK, that means you can get a couple hundred, maybe even low thousands, of stars a night. But the figures that you record depend upon the hour of the day which direction the Earth is facing, or your telescope is facing. And the time of year, where the Earth is in its orbit, you'll get different measurements of the stars at different times of year. That means you create a massive pile of data that needs to be reduced to absolute coordinates, to a location somewhere fixed in space. That requires a lot of work and a lot of arithmetic. And it required these observatories which could do the recording with a staff of two or three astronomers to have a large group of people to help them reduce these data points to something that was absolute so they could start fitting them into their map to the heavens. That was a repetitive job. There was a lot of it. And the issue that they all faced was how can you do it for the least amount of money? But part of what we think of as high tech and computing and programming is also the task of systemization, of regularization, of taking a complex thing that could be done many different ways and putting it in a form that can be marched through in a fixed series of steps. At base, no one really needs to know the return date of Halley's comet. There are certain few scientists for whom it helps explain the universe. But it's mostly just a reminder that this object has been seen every 75 or so years throughout history and has been recorded as such. But at the end of the 17th century, a group of French astronomers knowing that it was coming asked, could we figure it out? It was a test of the science that had been developing before them, of the theories of people like Copernicus and Galileo. And the question was, could they get a date? Could they get the date it was closest to the sun? And could they do it mathematically? And that's actually a tough problem at some level even now, although we have all the programs to do it, because it involves the location of several big objets and needing to move them through space while you're tracking the comet around the solar system. And what they did was divide the labor. And that becomes a key theme in computing, a theme that was worked out by human beings and relatively modest mechanical devices before we started putting electronics to it and rushing off into programs and artificial intelligence and all the rest. We worked out the problems of computing because it was divided labor. And for that first return of Halley's comet, they had two people working on the location first of the Earth and second of Jupiter, because Jupiter is a major influence on the motion in the solar system. And then another astronomer tracked the comet, which is pretty small and has little impact in terms of moving other things around in its orbit. And they figured out how to do that and do that repeatedly. Repeatedly and in a way that they could double-check their work. And that's sort of another one of the themes, that it's not just the brilliant insight, it's not just the algorithms, it's figuring out how to find mistakes, how to find mis-additions, mis-calculations, and even mis-apprehensions, misunderstandings of what's going on. That process, which took about a month, really set the stage for the things that were to come in particular, it set the stage for the nautical almanacs. As I said, you and I have lived all of our lives without knowing the next return date for Halley's comet. And our lives will go on. And when it comes, it'll be a big party, and I hope it's better seen than the last time when it came in the '80s. But it's just a party, it's just watching an odometer flip. The bigger problems, the things that involve production, in terms of our living our lives, of feeding ourselves, clothing ourselves, providing shelter. We need to produce goods and services. And the key thing that was involved there was trade and ocean-going vessels and knowing where they are. Once you're out of sight of land, how do you know where you are? The ancient astronomers, pre-17th century, had a lot of various methods that were somewhat ad hoc that sort of involved, "This is spring, we know this star is over our destination. "We go out, we point the ship at the star, "and we pray that we get there." They didn't know where they were on the Earth's globe. And that required an understanding of how to compute longitude and latitude. And latitude is fairly easy to get, at least in the Northern Hemisphere. Longitude is a lot harder, and it requires knowing where stars are relative to a fixed point on Earth. And that knowledge, you have to codify in a book, and you have to do it multiple years in advance, because you give these books to captains and turn them loose across the oceans, far away from wherever that fixed point might be, which, in the early days of exploration, were one of three points, London, Amsterdam, or Paris. To produce those books accurately, you needed a system, you needed a system that allowed you both to do the calculations, and then undo them in a way that was different. Because one of the very early pioneers of calculation, Charles Babbage, discovered what he called Babbage's Rule, which is two calculations done the same way by different people will tend to make the same errors. There seems to be, in the process of hand calculation, mistakes that trip up everybody. Not all the time, but there's a tendency that if one person makes the mistake, the next person will make it. So you need to approach the problem in a different way, and in particular, in a different way that exposes errors. And that was thrashed out in the late 18th century, in the nautical almanac in London, and in a comparable publication in Paris. They figured out not only how to divide, because there were multiple objects they wanted to get the locations of. So they divided that amongst a variety of people. And then ways of undoing those calculations in a form that exposed the errors. And that was a key innovation on their part. And that led to these publications, and that leads to the age of exploration and the start of global trade, global oceanic trade. Trade that requires boats to go out of sight. And that, of course, leads very directly to industrialization in the modern world. Same was true with surveying. In the United States, there was, by the early 19th century, there's the problem of figuring out where the United States actually is, whether it's bounds, whether it's limits. And that involves surveying first the coasts and then moving in. And that, again, required... they used nautical almanacs, again, to figure out the locations of places. But a great deal of calculation, because you're basically laying down across the earth a bunch of triangles. And you can figure out where two of the corners of the triangle are, and from those two corners, you can get to the third. And then from that third, that gives you a new triangle, and you can keep marching off across the land. That, particularly when they got to California, proved to be a hard thing to do, that it was very difficult to get all the calculations you needed to get that vast space well-surveyed. And in particular, that was needed because you had a bunch of people who thought they owned land there somewhere. And by thinking that they owned land, they had to know where it was. They couldn't say from this rock to that mountain to that tree. They had to have more exact points. And the treaties that shaped the West and shaped the United States basically required the new states to figure out land ownership and the location of land. And so they all faced this same problem of how do you take large amounts of data and process it, and that required them to think industrially, to think how the two pieces went together. And so there evolves other systems to try to do that quickly, to get first approximations, to be able to get numbers close enough so that the errors on borders are relatively minor and can be worked out in local negotiations. But it's that process of systematizing, correcting errors, finding approximations, and making them work as civil systems that was what really drove me to start looking at human calculation and what was the foundation that it laid for the modern computer age. Those lessons were first approached in largely the 18th century and by people doing work by hand and doing it largely but not exclusively for astronomy or surveying. One of the aspects of building systematic processes around numbers, around calculation was that very, very quickly people started asking, "What can you do with machines?" The idea of an adding machine, a machine that could add two numbers, goes back to discard. It has a long, long history. And the idea that you can represent numbers by the turning of a wheel, and then if the wheel turns all around, the next wheel, that was well worked out early. However, it really wasn't part of a process, so something that you could rely on. And when you talk about industrializations, you're basically building processes that can be done by people who at base don't know what they're doing or more accurately. They are doing something that they gain a skill at, that they understand their steps and they learn how to do them efficiently. But they don't understand necessarily the science and the ideas behind them. At the start of the 19th century, there's a great explosion in the interests of what machines, levers, gears can do. And there is a study of linkages, for example, of mechanical arms that connect together. And what they could do that, I don't think it's quite lost. I'm sure there are mechanical engineers but it's something that has certainly vanished from our daily contemplation and contemplation even of non-specialized students. And one of the people who got fascinated with this was an Englishman named Charles Babbage. He's a start of the 19th century. His father was a banker. Because his father was a banker, his father was also involved in the Caribbean trade. And that meant he was familiar with boats going off to the Caribbean to collect largely sugar and bring it back to the United Kingdom. And it's at a point where systematized shipping is starting to really take hold. It really doesn't kick in until the start of the industrial age where boats run on a schedule. And you know the schedule and you know more or less the date they're coming. Babbage attends Cambridge. He learns astronomy, gets fascinated with it, fascinated with mathematics, and then starts pondering how can you mechanize mathematics? What can you do with it? He had written a couple of things while he was in college that in many ways was filling up spaces and filling up shapes with smaller versions of itself to see how you could approximate them and what systems would work and what wouldn't. And sometime during that early period he got involved with an article almanac. He ends up being on an advisory board for it. And this is when he discovers his rule that two people doing the same calculation the same way tend to make the same mistakes. The second job that's starting to come up and he gets involved and interested is the understanding of insurance, of probability behind it. In particular, insurance is at some level a savings account, nothing more than that. And it's savings that you get to cash in the savings account when you die or if it's a health insurance, which is later in the century, when you get sick. That means the people running the bank need to know how much money they are likely to pay out. And the understanding of that, the mathematics of that, the mathematics of probability, is just really starting at... Again, it's an industrial revolution start, but it's really starting to take hold in industrial life at the start of the 19th century. And it involves the creation of another kind of table called mortality tables, which tell you how long someone is likely to live given that they've lived this long. And they are highly dependent on data and on what people. You don't do them for a huge population because there are differences in the way people live and their diets in the work that they do and the kinds of families that they have. So you tend to do it for smaller groups, and that means you have to do a lot of these tables, and you have to process a lot of data. Babbage pondered it, and he began to realize that for both astronomy and for these insurance tables, there was a common kind of calculation that could be very useful, but was by the standards of the time, very time-consuming. And it's basically fitting a curve to data. You have these points, and you want a curve to go nice and smoothly through them so that you can make estimates between the points, and you can go project out beyond the end of your curve to figure out where it might be going. And it dawned on him, on Babbage, that calculation was, in effect, could be reduced to a lot of additions, a very large number of additions and subtractions. And that meant that he could chain together a bunch of these adding machines and produce a machine that could indeed do that by just grinding away at a crank, or as he thought of it, be produced by steam. He was working in the very early age of railroads and steam engineering, and he saw his machine as very much in the heritage of locomotive design. And so he produced a machine with very large gears and numbers, and the dials on it were made out of well-machined brass. And the process was probably beyond the engineering ability of his time. His lead engineer pushed British engineering substantially and advanced at both working for Babbage and later. And it was also probably the wrong concept to build from. Machines are very often done as metaphors. We build a machine to do something like this, and Babbage was building a machine to do calculations like a railroad engine. And railroad engines were not completely safe and secure then, and they were still learning a great deal about how to build them. Babbage never got his working. He built a number of models. He built several that demonstrated the proof of concept. He could fit not the curve he wanted, but a much simpler curve to data, and in a machine that got through the calculations, we know at least once. And then he built a lot of rough parts for the machine he wanted to build. And because of that, Babbage and his ideas left a legacy about what they were trying to do more than what they actually did. What he was trying to do was systematize calculations so he could handle large-scale, in both cases, calculations, fitting curves to data and things of that sort. He had contemporaries that were intellectually, in terms of direct connection, far more important. George Bool. George Bool wrote a book called "On the Laws of Thought." We now know those laws of thought as Boolean algebra, which is the fundamental tool underlying all of the analyses that design electronic circuits and computers. That connection has been well known and understood forever. Babbage vanished for a bit, and also the exact intellectual connection is not as clear as you'd look at the systemization, the building of an industrial process, the work to lessen the cost of computing and to move it into a bigger environment. In that sense, he had a tremendous impact and remains an important figure to this day. Almost 30, 40 years after Babbage did it, a Swedish father and son built one of these machines using Babbage's idea and built it, and it was fully functional using clock technology. And clocks are much smaller, they're much easier to work with at that scale, they require less energy, and there were lots of standardized parts they could borrow from. In particular, escapement clocks, which were the dominant technology until not that long ago, have an important role in science writ large and in computing specifically. Clock were one of the first sophisticated technologies that had wide distribution in the 18th century. The escapement clock, clocks with a pendulum, with a little gear and a little thing that goes click, clock, click, clock, tick, tock, tick, tock, tick, tock. Technology that were novel, they were often packaged in ways that made them, in effect, a luxury item, something that the rich would have, but very quickly they became common and they allowed industrialization, for example, by setting times when a factory would be open. You don't need a clock if you're running a farm necessarily. There might be a few things where knowing an hour or knowing a minute is useful, but for the most part, you're following natural rhythms of the day, and those natural rhythms have a very wide margin of error. You can miss them by 15 minutes, 20 minutes, and you will be just fine. If you're setting up a factory, if you're bringing people together to work together, to collaborate together, and if in particular you're dividing a task up into a linear process, which was one of the very first things that happened. Adam Smith writes about making pins and needles, and in particular it's a process of cutting the wire, sharpening the wire, putting a head on it for a pin, putting it in the paper that you're going to sell it with, and moving it on. It's a linear thing. You have to do one step before the other, and if you're dividing it up across different people, it means you have to have people for all the steps, and that means they all have to be there at the same time, and you really needed clocks to be able to do that. Scientifically, it made a lot of measurements more interesting, in particularly the measurements of location. You needed that to determine longitude. You needed that to determine location on the earth. Because of that, it became an important technology for disseminating ideas, for bringing them to people who became fixated with clocks with how they worked, of how they could improve them, of what they could do with them, of clockwork mechanisms that became part of other technologies. In computing the clock, the tick-tock of the Escapement Ratchet, became part of computing because it became a drumbeat that stepped through the basic mechanisms of calculating devices. As you would go, you just wouldn't let your machine run wild. You would have a device that was going tick-tock, tick-tock, tick-tock, that was controlling the other elements, the other parts. And that allowed them to think about machines that could do calculations in a systematic orderly way. It gave them a method to control them. But more importantly, it gave them a technology to build off, to think about, to say, "Okay, we know this works for doing time. How does it work for collecting data? How does it work for timing additions? What can we do with it?" And so these clocks, clockworks and clock mechanisms are deeply imbued in industrialization, deeply part of calculation, and part of the tools that people used to think about how to build the next generation of devices and how to use them. [music] Want to support the channel? Join the Big Think members community where you get access to videos early, ad-free.