How the Industrial Revolution invented modern computing

I'm David Alan Greer. I am currently a writer and author on issues of technology and industry and

things of that sort. In the past, I have been a computer programmer, a professor, a software

engineer, president of the IEEE Computer Society. I am the author of the book When Computers Were

Human and also the book Crowdsourcing for Dummies, among others. 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, you know, 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

Okay, 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? 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 18th 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 data that 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 objects

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

and 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, miscalculations, and even misapprehensions, 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 know where they were on the Earth 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 explorations 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 this 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, what are its bounds, what are its 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 Descartes

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, it turns the next wheel, that was well worked out early

However, it really wasn't part of a process, of 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 interest 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 who will give me a lecture on this

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 started 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 the Nautical 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

in their diets, in the work that they do, in 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's 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 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

being 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 Boole. George Boole 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 yses

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 unless you 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

Clocks 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

were a 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 bringing people together to work together to collaborate together and if in particular you 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. And it gave them a method to control them

But more importantly, it gave them a technology to build off of, 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

Chapter 2. The Power of Standardization. Standards are a key part of the industrial world

Building things to standard models, standard parts, goes almost all the way back to 1776

The one that we usually point to is the Remington factory in Connecticut

and the tools that they used to build standard firearms. But the real standardization process, the one that has created the modern age

is much newer than that and has a much broader scope that involves the mathematics of calculation

that involves data much more profoundly, and in turn influenced how we calculate and how we

work with data. The fundamental goal of all of this is reducing cost, reducing effort

and also transferring ideas to the least well-trained individuals. If you're going to make large-scale data processing work

you need to have people involved who are not as educated as yourself

who can deal with a part of it, who can follow instructions. If you look at the original calculations done for nautical almanacs

in the late 18th century, the 1780s and 1790s. They are done by individuals who would lay them out on a piece of a paper

each use their own method, each had little tricks for identifying things

for remembering the exact calculations they were doing. There would be a collection of additions and subtractions, a few multiplications

and if you could at all avoid it, no long divisions. there is a collection of them that belonged to one of the human computers as they were called

that worked for the british nautical almanac who lived in the united states and he would do his

work here and send them across via a boat to london where they would be incorporated in the

nautical almanac he had his own way and he also had a rather cramped hand he could not write on a

straight line. And if you go over his materials, you have to be very careful to make sure you're

on the right line of the calculation and you haven't jumped to the wrong one. He could make

that work and he was quite successful at it. Not everyone could decode that writing. By the start

of the 20th century, you see people doing it on graph paper with little squares so that all the

rows line up. You start seeing people identifying the operations line by line by line, particularly

in the mid-19th century in England when they were doing title recordings. We did those here as well

but they were ahead of us in systematizing them. We did that a little bit later in the 1870s

for weather. Weather requires lots of data because if you look at the weather channel

if you look at your phone and watch the clouds move across

there are clouds that will hit one part of our metropolitan area that will miss other parts

and they won't be that different, that far apart. So there's a lot of data you have to have

to be able to get accurate weather forecasts beyond just the broad statement that it might get this hot

for this metropolitan area on this day. When we set up a network to start doing that

again, we had people collecting data, There was more data to collect

It was a little more subtle about how you get rainfall and precipitation and humidity

and required a certain amount of arithmetic. And it required that you be able to work with people who had not gone to college

because you weren't going to get enough of them if you restricted yourself to just college graduates

And that forced the weather people and scientists as a whole to ask about standardization

how do we follow the industrial patterns and move things into ways that are always done the same way

that always use the same methods, that always produce the same results, and that anyone can interpret

In terms of the modern sense of standardization, I really pin it on World War I more than anything

And if you wanted one individual who was more responsible for it than any other, who's a major leader, it's Herbert Hoover

Hoover was an engineer. He was a mining engineer, which means the big thing he's building is tunnels and walls and things to hold up tunnels and concentration plants, which are basically pools, and other big things

but he also grasped that engineering methods could be applied to other products if there was

a standardization. And he worked in an era when things that we take for granted, like bolts, you

You have a nut, you have a bolt, you put them together, and you can screw them. And they work because they're the same

Weren't standardized. You buy them from one manufacturer, they're not going to fit from another one

And he argued that there were a large number of things that if they were produced to standard forms

would increase the scale of our industrial processes, would allow us to make more products for more people at lower costs

and expand our ability to do things. And that led to a variety of institutions

the National Institute of Standards, then called the National Bureau of Standards, being one of them

and private standard organizations. My former organization, the IEEE, the Institute for Electrical and Electronic Engineers

has a big standards group that deals with electrical standards. There is the American National Standards Institute

Institute, which deals with a lot of other standards, including a lot of standards involving

computers. If you have everyone going off and doing their own thing, that can be very good

because it means you're exploring and figuring out what works and what doesn't work

But at some point, you've got to start focusing on production, at least for certain problems

And in those cases, you need standards. And as the computing age built, they more and more started

looking at standard ways of doing calculation, standard ways of expressing algorithms, standard

languages for expressing programs. And all of these became a key part of computing that would

have been a lot more expensive and a lot slower if it hadn't had the standards there. In our modern

age, one of the things that companies do, that individuals do, is ask, how can I use these to

solve my problem? How can I build and extend and expand them? So at this point, it's so deeply

connected with computing that there's very little you can do without understanding them

Yet at the start of the 19th century, they were at best vaguely understood and vaguely used

We live in a standardized world and we live in an industrialized world and we industrialize

lots of things. And one of these things that we do, we produce at large scale, low cost

is education. Prior to the early part of the 20th century, really in the late 19th century

there were some efforts, but education was entirely a local affair in the United States

It was something that a lot of people weighed in and provided books and sample curriculum

There were publishers primarily in New York that did textbooks that were commonly used, readers

for example, that helped kids grow up. But if you think about it, that outside places where there

was large and easy transportation of children in an age before their school buses and paved roads

often, classroom was one room teaching. It was a group of kids together with one teacher who was

there for a short time and did the best they could and moved on. 20th century starts to move that

forward. It does go back into the 19th, but Carnegie's work really comes in the 20th, and it's

largely dealing with higher ed, with colleges. And I suppose technically he's Carnegie. This is the

founder of what becomes U.S. Steel. In colleges, we have a standard amount of education that'll get

you a bachelor's degree. 120 for Bachelor of Arts, 146 hours of instruction, that'll get you a

Bachelor of Science. You take a certain number of classes. The classes have to have a certain

distribution. They have to meet for so long. These standards were developed out of work done by the

Carnegie Institution. He funded studies of a wide variety of subjects that ask what's the best way

to teach them. The most famous is the 1910 study of medical education. That's the Flexner Report

The Flexner Report went and looked at how medicine was being taught to doctors, what kind of things

were being taught, what were the different practices, what were the best practices, and how

can we produce the greatest number of doctors with uniform education at the least cost? And in the

middle of that report is a line that refers to how we must devote our education in medicine

to people who can devote their entire careers to medicine. And a lot of deans flagged that at

universities and said, oh good, we don't have to worry about women anymore because women take time

off, women have families, women have other duties. And prior to that, there were roles for women in

medicine. They dealt with what you might expect, babies, pregnant women, and dying old people

But they were part of the medical community, and the Flexton Report shoved them out the door

And if you go to the early age of the suffragette movement in the United States

the women who picketed the White House, the women who wore the yellow sash, the women who lobbied state governments for the vote, they were overwhelmingly the daughters of

doctors because they had time, they had money, and they had a grudge because they weren't going to get

a position in the medical community, perhaps other than being a nurse. And what happened after the

Flexner report, this is wandering away from standards for a moment and back to computing

the sciences looked at women who had been studying medicine, who had been part of medicine

and largely said, you're nice people, please go away. Chemistry said, we can't have women study chemistry

It will hurt their ability to have children, and it probably hurt the men's ability to have children too

Biology was not the field that we know it today, nor was physics

Mathematics, being somewhat desperate for enrollments then as now, said, women, we'll take them

They can always be high school teachers. And in an instant, the teaching of high school mathematics becomes feminized, that women see an opportunity and they grab it

And they also grabbed jobs in calculation and other things because medical education was being standardized

This applied to other forms of education as well. But it produced a standard form of a university that we in the United States understand and know how it works

But the roots of those standards, there are a number of other organizations that have contributed to it

were in a series of reports that the Carnegie Institution put together in the early part of the 20th century

In fact, there was a time that the college course credit was called a Carnegie credit

That has largely passed from memory. But there were institutions that said, we will become richer

We will be more productive. we can give more to more people if we do it in standard ways and of that

Carnegie was important Hoover was important but so were a lot of other organizations and computing benefited in it because without it it scribbles on a page With a standardized sense you can start getting people to do it

who are not trained in it, who have not the education that the directors of the project have

and that greatly increased the power and the use of computational methods

Chapter 3. computing the human experience. Right now where we're sitting in Washington is the old patent office

And back in the 1850s, that was the place the first telegraph was connected in the United States

It was from here up to Baltimore. And the first message that went across it was the famous, what hath God wrought

And it was just people who were madly in love with technology going, my God, we can talk to Baltimore

and it's like it's right there, not whatever it is, 25 or 30 miles up the road

We don't remember the second question. The second message was, what time is it there

Because at that point, every city had a local time that was measured by when the sun was directly overhead at noon

And Baltimore is about seven and a half minutes ahead of Washington

and one of the things that it told the scientists who were in the room and it was

it was a political group but there were also a number of senior u.s scientists at the time

was that you could collect a lot of data about where things were by measuring time differences

and they immediately set up a process and this again points i think to the industrial connection

a process for determining differences in time between cities that could be done by people who basically were just following instructions

and really didn't know what they were doing beyond counting clicks on a clock

They could sit there and they could ping messages back and forth. They did not use the word ping, I should say, at that point

but send clicks back and forth, count clock motions, and at the end of the day, they would write that down

and that piece of data would tell you the difference in time

That was used very quickly to collect lots of data about time differences all over the East Coast

and very quickly over the United States. And again, it had to be systematized and organized

to figure out how it worked and what were the differences and how could that be used for commercial reasons

And the big one that started to come is, of course, railroad trains

because most places in the United States in the 19th century, you had one track between here and Baltimore

or one track from Baltimore to Philly. And you had trains running both directions

And if they came together, that was bad. They had places to pull out

But how do you know someone's past you? How do you know if you're reading the time schedule right

And getting a unified time, which was in place by the late 19th century

helped eliminate a large number of train wrecks and delayed trains and other things that disrupted production

that disrupted the transportation of goods and services. So all of these things are deeply, deeply imbued in computing

And computing is so tied with the issues of production, of getting things done quickly, easily, systematically

and of capturing human experience. In the early days, the human experience was largely capturing natural experience

What's the time difference? Where's the locale? When does the sun rise here

When does it sit there? But by the end of the 19th century, they're starting to look and capture human data, human

activity, the kind of things that people did and worked and behaved so that they could use that

for making policy decisions, for making marketing decisions, for deciding how much to produce

That became the interesting thing. And again, as just you went from doing one comet to doing

nautical almanacs, to doing mapping the sky and to mapping the earth, this was another big jump

in the amount of data collected. The census was done every 10 years

It's in the Constitution. And for the first 100 years or so

it was largely an office that appeared every 10 years. They hired a bunch of people

and told them to run around and count. And then they got all the stuff back in Washington

tabulated it up into how many people were in each city and each state and published it

And it was straightforward and fairly easy. By the 1840s, they're starting to ask questions

How many people live in your household? How many men? How many women? How many children

They began looking at issues of public health, of disease, of general healthiness

Landowners, what kind of work people did, their ethnic background, certainly a slavery

built, the problem over slavery built in the 1850s, that became an issue

And that meant we were collecting more and more data. And the censuses took longer and longer to do

And by the 1870 census, that takes most of a decade to complete

The 1880 census is never really finished. It still isn't really finished to this day

There are publications, but there's data out there that we haven't officially worked with

And as 1890 began to approach, the head of the census said, we've got to do something

We've got to systematize it. We have to industrialize it. We have to reduce the cost

And so he put out a request for someone who could build machines that could count people

And the process that came out that won the contract formed a company that was known then

as the Hollerith Tabulating Company, become something we all know a little better, IBM

And they proposed one that would send people out to the field, not with a pad of paper and something to write things down, but with a punched card and a little machine that you would punch holes in it with

Those cards could be taken back and they could be put on a machine and the machine would count up how many women, how many men, how many people over 45, how many people living in this city

And it sped the process so greatly that by 1893, the preliminary census was completed, and it was being widely advertised and widely used

1893, there was a big World's Fair in Chicago, and the results and the machinery that produced those results were on display and a central exhibit at that fair

People were talking about the results. In particular, at that fair, there was a historian who got up and said

by reading the recent census report, I can conclude that we no longer have a frontier

America is a settled country. And that produced a change in perception of ourselves

And that we only reached it because we were able to handle that scale of data They were as is so commonly the case in love with their machines

They were very proud of them. They were very proud of what they could do, of the results that they could obtain

the speed with which they were able to tabulate the population of the United States

to the point where they started falling in love with the punched cards

There is an assistant director of the census who wrote this stunning document

about how he could hold up a card and he could see the person in the card

Now, I have no doubt that people got used to them enough and said, yep, that's the manhole, that's the womanhole

that's the kidhole. You know, I can quickly scan and guess what kind of person this is

but it went beyond that. It was an enthusiasm, as I said, that's common with new technology

that he would hold it up, and he felt that when he was putting it through the tabulator

he was working out the future of that person. And the bell, which indicated that the card had been processed

was the bell calling that soul to judgment of heaven or hell

It's sweet and it's endearing, and it also shows how as we start approaching our life through data

through the large processing of data, we start seeing ourselves in new ways

and we start seeing something that we couldn't see before. We get a hope, an intuition

and that that intuition provokes an imagination and a love that we didn't have before

Now, that often comes crashing down, But nonetheless, there was a time of falling in love with large-scale processing that came from seeing ourselves in new ways that were driven by data

Chapter 4, How Computers Change Us. How long have we been going

I'm a professor. I can talk for hours. In the 1940s, there was again a lot of computation that was needed for the war

In particular, bombing, hitting things, and specifically shooting down aircraft. Shooting down aircraft is basically like duck hunting

The hunter must forge his lead and aim ahead of the duck, if he is to hit it

You want to get up there, you want something to explode near the aircraft, and if it explodes near the aircraft, you get a hit

And unlike duck hunting, you don't set your dog after it, but the effect is quite similar

But because of the great altitude and speed of a bomber, the anti-aircraft gunner cannot rely on dead reckoning

His leading must be a careful mathematical calculation. That's a hard mathematical problem because you've got a moving aircraft, you've got a gun that is probably moving on a ship

or if it's on fixed land, it's swinging, it's moving back and forth. You're computing part of an arc, which makes it harder

They had methods from the First World War where they could estimate what the whole curve looked like, but they were hitting something on the ground or a ship that was fixed

And that was easier. Shooting things down out of the air is tough, and getting where you want your shell to explode is tough

What was then the Army Air Corps invested a lot of money in figuring out how to do that, and in particular at the University of Pennsylvania

they employed a couple machines that had been built there to simplify it

and just produced reams and reams and reams of data that were taken to another site

and reduced into instructions that you could give to pilots, to gunners and other soldiers

The group in Philadelphia, in working with their machine, which was what we now call an og machine

It used amplifiers and tubes to draw an arc that could have been drawn on a screen

It was usually drawn on a piece of paper. One of their number realized that you could do it more generally by doing the actual calculations

You could draw the curve by stepping along the line, and from that you could do more than just do the curve for a shell

There are other problems that you could solve with it. They pitched this to the Army and set up a process to build this machine, and this is a key moment in American computing history

This machine, which would get the name ENIAC, electronic numerical integrator and calculator, was a very direct precursor of the modern computer

and it operated in a substantially different way, but nonetheless, a very large number

the first generation of computer engineers got their training on that machine

And by first generation, I mean really the next eight to 10 years. In doing that, one of the leaders was a mathematician

from the University of Michigan, Herman Goldstein. And he, on one of the train stations around Philly

noticed a scientist that he knew from Princeton named John von Neumann

Von Neumann was at the Institute for Advanced Study, which is an institution of higher ed that has no students, takes no tuition

gets famous people to come and just think. Einstein was one of the people there

He went up and introduced himself, said he was working on calculations, and von Neumann said he was very interested

Could he come and look? Von Neumann was a mathematician, very skilled, could very quickly grasp ideas

He had been interested in calculation for a long, long time, and had studied some of

the computing groups, particularly the one in London that was doing the Nautical Almanac

And with the leader of that group, the two of them had sat down and worked out things

that were really the precursor of what a program would look like

what did it mean to program a device? How did you systematize calculation to a set of instructions

that would always be performed the same way? Von Neumann goes to visit the ENIAC

which is at the University of Pennsylvania, and gets very excited about it

and starts thinking about how to expand it and develop it and wrote a report that has since remained

the controversial beginning of the computing age because it defined what a modern computer was

and it left off the names of all the people who had been working on the ENIAC

Von Neumann abstracted what had been done there, and it dawned on him that the machine

that they were really trying to build, not the one that they were building

but the one that had the most flexibility and the most ability to do things

had three elements. It had a place where you could store numbers

a scratch pad, if you will, memory that we now know it it would have a processing unit didn't describe what that

processing unit did at the time it was generally assumed that it did addition and but it could do

other things it could do the other arithmetic operations or it could do other operations still and that there was a third element in it and that was a program decoder That is to say something that would read signals from the memory figure out what those

signals meant, and send them either back to the memory or send them to the processing

device to do some activity. These three elements in multiple forms are part of every single computer we have today

Some do arithmetic, but not all of them. Some of them do just various forms of symbol processing

Some do a variety of other things. But having an instruction decoder, a memory, and a processor is part of everything

The key piece of research that laid the foundation for the Internet was the work on ARPANET

There's a lot of work out there that's done, but the first systematic network that was built using the principles of sharing lines, of splitting them up, was indeed the ARPANET

And the people who worked on that then turn around and eventually work on the Internet

and the ideas that they work are clearly the internet builds on the ideas that they

put together for the ARPANET. It connected a group of computer research sites that were at

primarily major universities. It was funded by the Department of Defense. One of the things that that department wanted to do was to build computer science

into a recognizable discipline. At the time, it was not. There were very few places you could study it

If the few you could, you were considered to be studying a form of electrical engineering

My early study of it, which was from that period a little bit later

I studied mathematics because I couldn't study it. I had a father who was in computing

which gave me access to lots of stuff to learn it, but I still couldn't study it

Their goal was to build a community. And in that goal, a whole lot of other things came out that were unspoken or sometimes very actively spoken about what communication should do

The first was that there was always going to be a human-to-human element

Email was not the first application. It was probably the third. and people figured out all sorts of ways to do it on the fly before they got a good system that

worked. And in fact, that was a very important early standard, that they built a system that

putting a file with a message in it, it would get to the right place without you having to intervene

That took a couple of years, but it got there and it was working. The second part that they

grasped and articulated was that it was not only person to person, but that there would be

repositories, that there would be collections of information that anyone could go and get

They would be called libraries, they would be called servers, they weren't quite sure. They would be organized in various ways, but they would involve searching and looking for things

And that concept of searching is crucial, and we have entirely forgotten that it is

It's one thing to have access to everything and everybody. It's another thing to find

the person you want and get that little bit of information that you're hoping to use

Searching was an early computer problem. If you look at the early algorithms from the late 40s

you see lots of written on efficient ways to do multiplication and clever ways of doing long

division. And every now and then, a little thing will bubble up and say, oh, if you wanted to

search through a bunch of records, this is how you would do it. There's papers, I think you can find

one from 49, but you really don't start seeing that kind of question until the early 50s. And

it very soon becomes a major issue of how you do it and how you do it well

If you're searching for names, you know that that name is a part of a record. That's easy

If you're trying to search on qualities, that's harder. A group of Stanford graduate students in the early 60s had this great idea, which we all have borne the burden of

of wouldn't it be great to put people's qualities into a database and have it search through them and match the couples that are most alike

It was an early form of computerized dating. They had a party

They ran the program. They distributed the outcome to everyone who was there

including couples who had been dating. I believe there were some there who were even married

Needless to say, the walk home that night was not as much fun as they hoped it had been

They were excited by the process. The thing energized them incredibly. But there are people that said

what do you mean I don't match with this person? I match with that one. What do you mean that I'm closest to these qualities rather than those qualities

I mentioned about the Herman Hollerith machines, where people got very, very excited about the cards and saying they could see human beings in them, see histories in them, see their future in them

that comes up in the literature again and again each time a new technology comes that allows us

to see ourselves our activities our data in new ways the pc was that very much in the 70s

people saw this as a personal device something that i would use in a way that we have forgotten

was novel the idea that you had a computer that was solely you your solely yours that you could

use it and have complete control of it was quite new in the 70s. People had been able to work one

on one with computers before. That's not anything special, but it's special that it was yours

that you could make it. And you see it in, you know, customizing computers and putting stickers

on them. We still go on with wallpaper and other things that make it ours, that make it the

reflection of us that show our ideas are important, that our work and that our activities are positive

and that they can be reflected back to us by our machine. But in working with systems

the fundamental rule is we adjust ourselves. We adjust our thoughts. We adjust the way we work

And that process involves us adapting our thought and our habits and the way we look at the world

to the way those systems are designed. We talk about how we go shopping

about how we interact with various things. These are our actions to systems

that are built on algorithms and other processes that have helped discipline us

And you see that in everything you do, the way we use our phones. We didn't carry phones with us 25 years ago

We went from phone to phone, from office to home. And in the process, we learned to pay attention to it

to use it in different ways, to get certain information and put information into it in different ways

We now look at it for advice on how we get home

If we're commuting by car or by bicycle, where's the busy traffic

What do we do? What do we avoid? That was an art algorithm. was once considered one of the great advances of artificial intelligence

There's an important strain of AI that is building large databases and searching through them

And the search that does it most effectively for that kind of work is called A-star search

and that's what we use on our phones to find the fastest way home. Where is the traffic? What can we avoid

And in doing that, we look at it and we know how we give credence to that

whether we go on the roads marked red because that's the straight way and we don't want to be bothered

or whether we avoid it because we just loathe sitting in traffic and we want to keep the car moving

We adjust how we think and we adjust how we develop that strategy as we begin to see how effective it is

Does it just make sense to go through the big traffic? Are there better things to do

That's a simple example. but in everything we do, everything we deal with a computer, we're fundamentally asking

three questions. Are we getting what we want out of it? And if we're getting what we want

we're likely to repeat it. If we're not, we're likely to modify what we do and try something

different. Is it taking too much effort? And if it's taking too much effort, we're looking for a

way to do it more cheaply, more quickly, more easily? And the last one is, is it irritating me

That's part of the cost, perhaps. But it seems to be a slightly different thing of how does it make

me feel about what I'm doing? Will I change my tactics? Because the way it's presenting information

or the advice that it's giving, or the response that it's making, something that I'd rather not

face and I will go on and do something else. Those are the key parts of our working with the machine

And our goals are always, do we get something that makes it better for us? Do we improve our position

where we are amongst our friends and family? Do we see that expand and able to engage more people

or to engage with them more fully or do things that we couldn't do before? That's the second part

our function, what we do. And again, we tend to do computers to either get something more done

or do what we ordinarily do, but cheaper with less effort on our part. And the big one that

has really been the combination of AI and all the social networking has been status

which is how do people view us? How do they think about us? How do they listen to us

And some people may think about computers and say, does this computer honor my status

Does it give me more status? But in fact, it's how we appear to our neighbors

How do we appear to our family? And so much of our response to computers at base

is connected to how we respond to our family, to our neighbors, to our friends, to our community

to our office, to the people who drive next to us. And we change our response to it

in effect so that we are doing things better, doing them cheaper

and our goals tend to be in that social grouping. Does it increase our status

Does it improve our function, what we do? Does it give us a new position in this world

I'm not particularly frightened or concerned about it other than I know that it is part of adjusting our thought to the machine

And at some level, it is our adjustment that is saying, well, I will accept this, what the machine is saying, what the machine is doing, because I can view it as a reflection of myself, as a reflection of a being

Chapter 5, When Machines Replace Humans The connection between human computers and the machine computers is complex because it's involving the division of labor

The modern computer divides three actions out, gives them each a device

Storing numbers. Well, storing numbers is something people are not particularly good at

We forget them very easily. There was a time that my hand would automatically type out my parents' phone number or my sister's

I have no idea what any of my friends' phone numbers are anymore

because we don't do them and we forget them and that's that

The second part is remembering instructions. We are better at that, but again, it involves a memory, which we're not good at

But the interpretation, we tend to spin things that make life easy for us

or simpler for us or more harmonious for us. the the last piece is the doing the task and in working with the human computers as the process

moved forward increasingly that's the piece that it was that humans were reduced to doing the task

there is a photograph that's in my first book that is um i'm convinced that it's going to be

what I am known for in the rest of my days, that shows a room in New York with 450 people in it

lined up in thin little desks back and forth, all having a piece of paper in front of them

all of them having a pen, all of them doing addition after addition after addition

This room in New York City was the main office of a group known as the Math Tables Project

It was the largest collection of human computers, to my knowledge, that has ever been assembled

on the face of the earth, and for its time, the most powerful computing organization in

the world. It was a work progress administration project In 1937 the Depression which at that point had been going on for not quite a decade tightened up again It was often called the Roosevelt Recession

And the federal government was looking for ways to put urban workers back to work, particularly

New York City, which had an unemployment rate higher than most everywhere else in the country

and particularly they were looking for jobs that were like construction jobs but inside

The Institute for Advanced Study proposed that they build a computing lab

and that computing lab would be a group of people doing calculations for projects

that could benefit by having highly accurate mathematical calculations done for them

They would take jobs from scientists, they would take jobs from government offices

They would do some work on their own doing. And this would be of value

But it's a work progress administration task. Means to say that they are recruiting from the unemployed

And by 1937, they were the long-term unemployed. Largely, they were store clerks, some of them office clerks

They had some knowledge of addition, but they were not particularly well skilled and the wpa was the last hope these were people who

could not feed their family any other way and so in taking these jobs they did so reluctantly

they knew it was repetitive but they knew it would feed their families they were from

the bronx they were from harlem they were from the poorer parts of town

but at the same time all of them had some at least high school education for the bulk of them

they felt the sting two ways the wpa gave them work but also required them to look for work

and so they're hi we're here we're glad to have you there you have you have to be going you have

to be looking for something else and second that problem of babbage that people doing the same task

the same way will always get the same results, will always make the same errors too, hung

heavily on them. And they wanted to be known, because of the reputation of the WPA, as being a group that

produced error-free calculations. So they put together a process that ultimately, in terms of equivalent labor, meant that each

number was calculated six or seven times, never the same way, never identically

and most of the effort was involved in creating a group of numbers

and then taking them apart and trying to figure out how they worked. They stretched to find any place that their work was used and accepted

particularly as the war came. Whenever they figured out a connection between their work

and a military operation, they would promote it heavily. Coming up to D-Day, roughly eight months before in September of 43, they were given a series of calculations to do that were equivalent to putting down a map, throwing coins on the map, pennies, and counting where the coins lay

they did the experiment sometimes and then they developed a mathematical calculation from it

one of the people looked at the map that they had put on the floor to throw it and recognized it as

part of normandy france and speculated that they were preparing something for the invasion of

europe which indeed they were they were trying to figure out how to clear the beaches of mines

by dropping bombs. And the idea was to drop enough bombs so they'd explode the mines

without blowing too much of the sand away. They worked through the problem

It's tough computationally. It was well beyond some of the mathematical ysis

of the time. They were immensely proud of it, even though in terms of looking at

how the D-Day planners used that information, they ultimately concluded that rather than trying to bomb the mines off the beach

they would try to avoid the Meeches with mines. And how much they used it directly for planning is not clear

but they loved it, and they loved that connection. They did things for radar

and radar calculations for the work against U-boats in the North Sea

The big one, which they did not grasp, and it's part of their files

was calculations that involving the collapse of a sphere, of having an explosion crush a sphere into a smaller sphere

The request is in their files. It has a big statement. You cannot share this

We will not explain what it is. We will not explain what it's for. We will not tell you what the units are or what's going on

Crushing a sphere. That's what you do to get an atomic plutonium bomb to go off

And it was part of the work to check the calculations at Los Alamos

for the Manhattan Project. When they found it out again, this was something of great pride to them

And yet at the same time, doing it made them feel like a part of a machine

They had trouble with labor unions, with strikes, with the typical things you have

with large labor activity and the feeling that they were barely above a machine When the place was demobilized some of them were brought in

and asked to take computing organizations for other groups. The bulk of them chose to be high school mathematics teachers

because they had learned enough mathematics that they could handle that, and there they were dealing with kids and people

and that was a much more satisfying activity. It was work for the poor, and that really is the ultimate form of that human computing

Prior to that, it had always been surplus labor. People who, you know, had the skills and had some trouble finding work, but they are part

of the process. When you get to that last group in New York City, they're laborers, and it's very clear

they're laborers. the substitution of machinery for labor is a huge part of the story of computation

and there are you know two principal motivations for it one reducing activities that we thought

required human intelligence to mechanization to something that can be repeated again and again and

again very quickly that's only part of it the second part is systemization that you can put it

in part of a larger system that is processing information, that's doing things, that's making

decisions, and working on a bigger set of problems. In all of these, you're in effect trying to replace

expensive labor with cheaper labor, replace labor that is less predictable, whether by labor unions

or just wanting to add their own ideas, or not understanding what's going on with something that

we'll do the same thing again and again and again. That transition is always hard because there is a

cohort that feels quite angry and quite misused at what's going on. My favorite example is in the

1950s, as the American economy is growing to, in many ways, provide the same level of production for

consumer goods as we did for military goods in World War II. One of the new devices that's coming

into manufacturing are automated machine tools, controlled machine tools. And the ones that we see

in the 50s are things that are mimicking, that are following the motions of skilled machinists

After a part has been produced using a combination of manual and electric control

the tape is played back for automatically producing successive parts. The tape programs the machine's operations

The use of record playback control in this operation is expected to double production

You would take one of these automated tools and you'd have someone machine a part or create something

And the machine tool would record that and then duplicate it, replicate the process of doing it

So it would be behaving like the skilled machinist. And skilled machinist jobs were a higher level of factory work than, for example, assembly-like work in the auto industry

In assembly line work, you are part of the machine. Henry Ford made that very, very clear in his writings

The machine is the line, and you are part of the machine

And you don't, because you're a part, interfere with the operation of the whole

Machinists were different. And during the 50s, as they start seeing these tools come up, they protest

And it leads to one of the key fights that we are having today about who owns data

The argument that the large manufacturers had is we build the factories

We created the factories. You would not have your skills if you didn't work in our factories

You have no place to learn them, no place to use them. Therefore, your skills are something that we can copy and we can transfer to an automated machine tool

The workers obviously saw it from a very different point of view. We learned these skills. These are ours

We have added our own identity to those skills, and you can't have them

You know, in the American labor movement, if the early 50s represents a high watermark of its control over activities

This is part of the first chink in the armor. This is part of the first attack on it

Who owns the skills of factory workers? That fight in many ways was resolved by the second or third

or maybe even fourth generation of machine tools, because by the 70s, they're not mimicking skilled machinists

They are putting together motions and activities and machining steps from computer-aided design files that are produced by computer

So, for the most part, mimicking skilled machinists is no longer an issue in the labor movement

not the way it certainly was then. But that process, that same controversy

continues with the collection of data for artificial intelligence. Artificial intelligent algorithms, as we view them right now, rely on data

They rely on trying to capture something of human behavior, human thoughts, human activities

and put those activities, forms, and thoughts in some form that it can store and recover

and furthermore reason from or process from. It's not sticking with what it captured immediately

It's asking how can we extrapolate out? How can we apply it to scenes we have not seen

How can we use it for situations that are novel And the question becomes who owns the activities that are being captured by data If it were a factory you have that same fight that we had before Some large

capitalized firm built the factory. You work in the factory. You could not do it without that

And so you're stuck. But it's no longer a factory. It's no longer a place we go to

It's the social media. It's where we buy stuff. It's the systems we do our work with

And often the things being captured are not necessarily the primal activity going on

It's some other action that we are doing with the system. And so that fight now probes much deeper into our lives

It's asking who owns our basic activities? Who owns our right? Who owns the way that we use computers, that we shop, that we communicate with our friends

Who can take that and use it in a way that replicates it? It's not clear that this fight has been resolved yet

You know, we have seen some early efforts at legislation. California was trying to address it and pulled back from it

But that still remains. What do we have that we own of ourselves, own of our actions, own of our thoughts

and that aspect of factory work and factory labor, that contention over who owns the skills of the factory worker

was the starting point for that discussion and remains the starting point

even though our concept of factory and data gathering has grown much, much bigger

When we talk about artificial intelligence, we're lumping a bunch of different technologies into one

and we are looking from a very modern perspective at a process that's been going on for close to 75 years

The programs of artificial intelligence have been bubbling around for years and you have seen them come and go

but it has taken a certain amount of time to get the computing power to make them work at scale

to be able to build them with enough sophistication that they work across the board with many different people

That that problem of providing a system that you don't need to be an expert at it to be able to use it

has been part of it a long time. And part of the expansion that we see of artificial intelligence

is just that. We're building systems that allow more and more people to work with them

The technologist in me sits... There's kind of a U-shaped narrative. something comes and it's very exciting and you're writing at the high end of the U

And then there's a dip. And then there's a recovery. The recovery is usually less than the starting point, but there is still a recovery

New ideas are always exciting and I always enjoy engaging them. But in the course of my career, I've seen time and again

new ideas that work in a brief demo in a controlled environment

And when you try to build them out to engage a large community of users

you get problems and you get problems that are seriously wrong. You know, the early translation programs from one language to another

were at times very clever and very laughable. We don't play this game anymore of translating something into another language

and translating it back into English. The canonical one, which I was able to demonstrate during my graduate career

was taking the phrases, do you accept credit cards, translating it into Polish, which I do not speak

and then translating that back to English and having the response be, do you play poker for money

And, you know, it got some of those words right, and it was quite fun and humorous

but it illustrated the issue of making a system that's not the basis of reliable communication

By contrast, I now use in my work, translate programs all the time because I'm dealing with people in China, with people in

France. And for my group in Ireland, I even translate things into Irish just to be snarky

about it. And I know that that's a regular system. I know to keep my sentences short

I know to keep direct verbs. I know do not put large amounts of dependent clauses and modifying

words because the modifying words will end up modifying different things. But I know that

and it's a system I work with. It's not particularly exciting. It's not as exciting

as those first ones were, but it's a tool I use and that I work with. My feeling is with artificial

intelligence now, we have seen some of the first bloom of this generation. And this generation

really goes back to the 90s. The machine learning, the large-scale neural networks

the use of these things in generative systems where you are getting a sense of the whole

landscape of human behavior. All those are very interesting and very exciting in what they can do

They fail enough that they are not reliable tools for me. And I don't make much use of them because I don't want to trust what I am doing to potential

mistakes that aren't my fault. If I'm going to pay for a mistake, it's going to be mine

and thank you very much. Okay. And so now you take all this

and you edit the sucker. You're the editor. Want to support the channel

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