The next revolution

The first age of computing was concerned with using computers for simulation. As we have seen, the first computers were built to do complex calculations. The initial motivation for building the ENIAC was to calculate artillery tables showing the angles at which guns should be fired based on the distance to the target and other conditions. After World War II, scientists used the ENIAC to explore possible designs for a hydrogen bomb. More generally, computers were used to simulate complex systems defined in terms of a mathematical model that captured the essential characteristics of the system under study. During the first thirty years of computing, from about 1950 until the early 1980s, researchers increasingly used computers for simulations of all sorts of complex systems. Computer simulations have transformed our lives, from designing cars and planes to making weather forecasts and financial models. At the same time, businesses used computers for performing the many, relatively simple calculations needed to manage inventories, payroll systems, and bank transactions. Even these very early computers could perform numerical calculations much, much faster than humans.

WARNING: This chapter is more mathematical in character than the rest of the book. It may therefore be hard going for some readers, and they are strongly advised either to skip or skim through this chapter and proceed to the next chapter. This chapter describes the theoretical basis for much of formal computer science.

Hilbert’s challenge

Are there limits to what we can, in principle, compute? If we build a big enough computer, surely it can compute anything we want it to? Or are there some questions that computers can never answer, no matter how big and powerful a computer we build. These fundamental questions for computer science were being addressed long before computers were built!

In the early part of the twentieth century, mathematicians were struggling to come to terms with many new concepts including the theory of infinite numbers and the puzzling paradoxes of set theory. The great German mathematician David Hilbert (B.6.1) had put forward this challenge to the mathematics community: put mathematics on a consistent logical foundation. It is now diffi cult to imagine, but in the early twentieth century, mathematics was in as great a turmoil as physics was at that time. In physics, the new theories of relativity and quantum mechanics were overturning all our classical assumptions about nature. What was happening in mathematics that could be comparable to these revolutions in physics?

The hypertext visionaries

Vannevar Bush (B.11.1), creator of the “Differential Analyzer” machine, wrote the very influential paper “As We May Think” in 1945, reflecting on the wartime explosion of scientific information and the increasing specialization of science into subdisciplines:

Bush concluded that methods for scholarly communication had become “totally inadequate for their purpose.” He argued for the need to extend the powers of the mind, rather than just the powers of the body, and to provide some automated support to navigate the expanding world of information and to manage this information overload.

Software and hardware

Butler Lampson, recipient of the Turing Award for his contributions to computer science, relates the followi ng anecdote about software:

It is amazing how these holes (Fig. 3.1) have become a multibillion-dollar business and arguably one of the main driving forces of modern civilization.

The software crisis

The term software engineering originated in the early 1960s, and the North Atlantic Treaty Organization sponsored the first conference on the “software crisis” in 1968 in Garmisch-Partenkirchen, West Germany. It was at this conference that the term software engineering first appeared. The conference reflected on the sad fact that many large software projects ran over budget or came in late, if at all. Tony Hoare, a recipient of the Turing Award for his contributions to computing, ruefully remembers his experience of a failed software project:

In his classic book The Mythical Man-Month, Fred Brooks (B.4.1) of IBM draws on his experience developing the operating system for IBM’s massive System/360 project. Brooks makes some sobering reflections on software engineering, saying, “It is a very humbling experience to make a multimillion-dollar mistake, but it is also very memorable.”

Three views of the future of computing:

What is computer science?

It is commonplace to say that we are in the midst of a computing revolution. Computers are impacting almost every aspect of our lives. And this is just the beginning. The Internet and the Web have revolutionized our access to information and to other people. We see computers not only providing intelligence to the safety and performance of such things as cars and airplanes, but also leading the way in mobile communications, with present-day smart phones having more computing power than leading-edge computers only a decade ago. This book tells the story how this all came about, from the early days of computers in the mid-1900s, to the Internet and the Web as we know it today, and where we will likely be in the future.

The academic field of study that encompasses these topics draws from multiple disciplines such as mathematics and electronics and is usually known as computer science. As Nobel Prize recipient, physicist Richard Feynman says in the quotation that introduces this chapter, computer science is not a science in the sense of physics, which is all about the study of natural systems; rather, it is more akin to engineering, since it concerns the study of man-made systems and ultimately is about getting computers to do useful things. Three early computing pioneers, Allen Newell, Alan Perlis, and Herbert Simon, were happy to use science to describe what they did, but put forward a similar definition to Feynman: computer science is the study of computers. As we shall see, computer science has much to do with the management of complexity, because modern-day computers contain many billions of active components. How can such complex systems be designed and built? By relying on the principles of hierarchical abstraction and universality, the two main themes that underlie our discussion of computers.

Black hats and white hats

As we have seen in Chapter 10, the Internet was invented by the academic research community and originally connected only a relatively small number of university computers. What is remarkable is that this research project has turned into a global infrastructure that has scaled from thousands of researchers to billions of people with no technical background. However, some of the problems that plague today’s Internet originate from decisions taken by the original Internet Engineering Task Force (IETF). This was a small group of researchers who debated and decided Internet standards in a truly collegial and academic fashion. For a network connecting a community of like-minded friends and with a culture of trust between the universities, this was an acceptable process. However, as the Internet has grown to include many different types of communities and cultures it is now clear that such a trusting approach was misplaced.

One example is the IETF’s definition of the Simple Mail Transfer Protocol (SMTP) for sending and receiving email over the Internet. Unfortunately, the original SMTP protocol did not check that the sender’s actual Internet address was what the email packet header claimed it to be. This allows the possibility of spoofing, the creation of Internet Protocol (IP) packets with either a forged source address or using an unauthorized IP address. Such spoofing is now widely used to mask the source of cyberattacks over the Internet, both by criminal gangs as well as by governments.

The beginnings of interactive computing

In the early days of computing, computers were expensive and scarce. They were built for solving serious computational problems – and certainly not for frivolous activities like playing games! The microprocessor and Moore’s law have changed this perspective – computing hardware is now incredibly cheap and it is the software production by humans and management of computers that is expensive. Some of the ideas of interactive and personal computing can be traced back to an MIT professor called J. C. R. Licklider. Lick – as he was universally known – was a psychologist and one of the first researchers to take an interest in the problem of human-computer interactions. During the Cold War in the 1950s, he had worked at MIT’s Lincoln Labs on the Semi-Automated Ground Environment (SAGE) system designed to give early warning of an airborne attack on the United States. This system used computers to continuously keep track of aircraft using radar data. It was this experience of interactive computing that convinced Lick of the need to use computers to analyze data as the data arrived – for “real time” computing.

Another type of interactive computing was being developed at around the same time. Engineers at MIT’s Lincoln Labs had developed the TX-0 in 1956 – one of the first transistorized computers. Wesley Clark and Ken Olsen had specifically designed and built the TX-0 to be interactive and exciting, the exact opposite of sedate batch processing on a big mainframe computer. Olsen recalled:

Nanotechnology

In 1959, at a meeting of the American Physical Society in Pasadena, California, physicist Richard Feynman set out a vision of the future in a remarkable after-dinner speech titled “There’s Plenty of Room at the Bottom.” The talk had the subtitle “An Invitation to Enter a New Field of Physics,” and it marked the beginning of the field of research that is now known as nanotechnology. Nanotechnology is concerned with the manipulation of matter at the scale of nanometers. Atoms are typically a few tenths of a nanometer in size. Feynman emphasizes that such an endeavor does not need new physics:

During his talk, Feynman challenged his audience by offering two $1,000 prizes: one “to the first guy who makes an operating electric motor which is only 1/64 inch cube,” and the second prize “to the first guy who can take the information on the page of a book and put it on an area 1/25000 smaller.” He had to pay out on both prizes – the first less than a year later, to Bill McLellan, an electrical engineer and Caltech alumnus (Fig. 15.1). Feynman knew that McLellan was serious when he brought a microscope with him to show Feynman his miniature motor capable of generating a millionth of a horsepower. Although Feynman paid McLellan the prize money, the motor was a disappointment to him because it did not require any technical advances (Fig. 15.2). He had not made the challenge hard enough. In an updated version of his talk given twenty years later, Feynman speculated that, with modern technology, it should be possible to mass-produce motors that are 1/40 a side smaller than McLellan’s original motor. To produce such micromachines, Feynman envisaged the creation of a chain of “slave” machines, each producing tools and machines at one-fourth of their own scale.

Cybernetics and the Turing Test

One of the major figures at MIT before World War II was the mathematician Norbert Wiener (B.13.1). In 1918, Wiener had worked at the U.S. Army’s Aberdeen Proving Ground, where the army tested weapons. Wiener calculated artillery trajectories by hand, the same problem that led to the construction of the ENIAC nearly thirty years later. After World War II, Wiener used to hold a series of “supper seminars” at MIT, where scientists and engineers from a variety of fields would gather to eat dinner and discuss scientific questions. J. C. R. Licklider usually attended. At some of these seminars, Wiener put forward his vision of the future, arguing that the technologies of the twentieth century could respond to their environment and modify their actions:

Beginnings

What is an algorithm? The word is derived from the name of the Persian scholar Mohammad Al-Khowarizmi (see B.5.1 and Fig. 5.1). In the introduction to his classic book Algorithmics: The Spirit of Computing, computer scientist David Harel gives the following definition:

Thus an algorithm can be regarded as a “recipe” detailing the mathematical steps to follow to do a particular task. This could be a numerical algorithm for solving a differential equation or an algorithm for completing a more abstract task, such as sorting a list of items according to some specified property. The word algorithmics was introduced by J. F. Traub in a textbook in 1964 and popularized as a key field of study in computer science by Donald Knuth (B.5.2) and David Harel (B.5.3). When the steps to define an algorithm to carry out a particular task have been identified, the programmer chooses a programming language to express the algorithm in a form that the computer can understand.

In 2012 the U.S. National Research Council published the report “Continuing Innovation in Information Technology.” The report contained an updated version of the Tire Tracks figure, first published in 1995. Figure A.2 gives examples of how computer science research, in universities and in industry, has directly led to the introduction of entirely new categories of products that have ultimately provided the basis for new billion-dollar industries. Most of the university-based research has been federally funded.

The bottom row of the figure shows specific computer science research areas where major investments have resulted in the different information technology industries and companies shown at the top of the figure. The vertical red tracks represent university-based research and the blue tracks represent industry research and development. The dashed black lines indicate periods following the introduction of significant commercial products resulting from this research, and the green lines represent the establishment of billion-dollar industries with the thick green lines showing the achievement of multibillion-dollar markets by some of these industries.

Early visions

The British science fiction writer, Brian Aldiss, traces the origin of science fiction to Mary Shelley’s Frankenstein in 1818. In her book, the unwise scientist, Victor Frankenstein, deliberately makes use of his knowledge of anatomy, chemistry, electricity, and physiology to create a living creature. An alternative starting point dates back to the second half of the nineteenth century with the writing of Jules Verne (B.17.1) and Herbert George (H. G.) Wells (B.17.2). This was a very exciting time for science – in 1859 Charles Darwin had published the Origin of Species; in 1864 James Clerk Maxwell had unified the theories of electricity and magnetism; and in 1869 Mendeleev had brought some order to chemistry with his Periodic Table of the Elements, and Joule and Kelvin were laying the foundations of thermodynamics. Verne had the idea of combining modern science with an adventure story to create a new type of fiction. After publishing his first such story “Five Weeks in a Balloon” in 1863, he wrote:

Inspirations

There are many “popular” books on science that provide accessible accounts of the recent developments of modern science for the general reader. However, there are very few popular books about computer science – arguably the “science” that has changed the world the most in the last half century. This book is an attempt to address this imbalance and to present an accessible account of the origins and foundations of computer science. In brief, the goal of this book is to explain how computers work, how we arrived at where we are now, and where we are likely to be going in the future.

The key inspiration for writing this book came from Physics Nobel Prize recipient Richard Feynman. In his lifetime, Feynman was one of the few physicists well known to a more general public. There were three main reasons for this recognition. First, there were some wonderful British television programs of Feynman talking about his love for physics. Second, there was his best-selling book “Surely You’re Joking, Mr. Feynman!”: Adventures of a Curious Character, an entertaining collection of stories about his life in physics – from his experiences at Los Alamos and the Manhattan atomic bomb project, to his days as a professor at Cornell and Caltech. And third, when he was battling the cancer that eventually took his life, was his participation in the enquiry following the Challenger space shuttle disaster. His live demonstration, at a televised press conference, of the effects of freezing water on the rubber O-rings of the space shuttle booster rockets was a wonderfully understandable explanation of the origin of the disaster.