In working with systems, the fundamental rule is we adjust ourselves, adapting our thought and our habits to the machine. 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 "Crowdsourcing for Dummies," among others. 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 stretch 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.
Getting 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 of 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 analog 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 E-NIAC, 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 of the first generation of computer engineers got their training on that machine.
And by first generation, I mean really the next eight to ten years. In doing that, one of the leaders was a mathematician from the University of Michigan, Herman Goldstein, and he, 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, those 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, was 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 of.
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, 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 turned around and eventually worked 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 a 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 years, but it got there and it was working.
The second part that they grasped and articulated was that there 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 in 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 are 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.
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? " 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 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 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. You know, 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 algorithm that was once called, that 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* search and that's what we use on our phones to find the fastest way home.
Where's 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, we 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 the being. [music] Want to support the channel? Join the Big Think members community where you get access to videos early, ad free.
