1.1 Computer Science

What Is a Computer?

While computer science is about much more than just computers, it helps to know a bit more about computers because they are an important component of computer science. All computers are made of physical, real-world material that we refer to as hardware. Hardware—which has four components, including processor, memory, network, and storage—is the computer component that enables computations. The processor can be regarded as the computer’s “brain,” as it follows instructions from algorithms and processes data. The memory is a means of addressing information in a computer by storing it in consistent locations, while the network refers to the various technological devices that are connected and share information. The hardware and physical components of a computer that permanently house a computer’s data are called storage.

One way to understand computers is from a hardware perspective: computers leverage digital electronics and the physics of materials used to develop transistors. For example, many of today’s computers rely on the physical properties of a brittle, crystalline metalloid called silicon, which makes it suitable for representing information. The batteries that power many of today’s smartphones and mobile devices rely on lithium, a soft, silvery metal mostly harvested from minerals in Australia, Zimbabwe, and Brazil, as well as from continental brine deposits in Chile, Argentina, and Bolivia. Computer engineers combine these substances to build circuitry and information pathways at the microscopic scale to form the physical basis for modern computers.

However, the physical basis of computers was not always silicon. The Electronic Numerical Integrator and Computer (ENIAC) was completed in 1945, making it one of the earliest digital computers. The ENIAC operated on different physical principles. Instead of silicon, the ENIAC used the technology of a vacuum tube, a physical device like a light bulb that was used as memory in early digital computers. When the “light” in the vacuum tube is off, the vacuum tube represents the number 0. When the “light” is on, the vacuum tube represents the number 1. When thousands of vacuum tubes are combined in a logical way, we suddenly have memory. The ENIAC is notable in computer history because it was the first general-purpose computer, meaning that it could run not just a single program but rather any program specified by a programmer. The ENIAC was often run and programmed by women programmers (Figure 1.4). Despite its age and differences in hardware properties, it shares a fundamental and surprising similarity with modern computers. Anything that can be computed on today’s computers can also be computed by the ENIAC given the right circumstances—just trillions of times more slowly.

Figure 1.4 This image depicts women programmers holding boards used in computers such as the ENIAC, many of which were designed expressly for ballistics and ordinance guidance research. Today, these room-size computers can be reproduced at a literally microscopic scale—basically invisible to the human eye. (credit: modification of "Women holding parts of the first four Army computers" by U.S. Army/Wikimedia Commons, Public Domain)

How is this possible? The algorithmic principles that determine how results are computed makes up software. Almost all computers, from the ENIAC to today’s computers, are considered Turing-complete (or Computationally Universal, as opposed to specialized computing devices such as scientific calculators) because they share the same fundamental model for computing results and every computer has the ability to run any algorithm. Alan Mathison Turing was an English mathematician who was highly influential in the development of theoretical computer science, which focuses on the mathematical processes behind software, and provided a formalization of the concepts of algorithm and computation with the Turing machine. A Turing-complete computer stores data in memory (either using vacuum tubes or silicon) and manipulates that data according to a computer program, which is an algorithm that can be run on a computer. These programs are represented using symbols and instructions written in a programming language consisting of symbols and instructions that can be interpreted by the computer. Programs are also stored in memory, which allows programmers to modify and improve programs by changing the instructions.

While both hardware and software are important to the practical operation of computers, computer science’s historical roots in mathematics also emphasize a third perspective. Whereas software focuses on the program details for solving problems with computers, theoretical computer science focuses on the mathematical processes behind software. The idea of Turing-completeness is a foundational concept in theoretical computer science, which considers how computers in general—not just the ENIAC or today’s computers, but even tomorrow’s computers that we haven’t yet invented—can solve problems. This theoretical perspective expands computer science knowledge by contributing ideas about (1) whether a problem can be computed by a Turing-complete computer at all, (2) how that problem might be computed using an algorithm, and (3) how quickly or efficiently a computer can run such an algorithm. The answers to these questions suggest the limits of what we can achieve with computers from a technical perspective: Using mathematical ideas, is it possible to use a computer to compute all problems? If the answer to a problem is yes, how much of a computing resource is needed to get the answer?

Clearly both humans and computers have their strengths and limitations. An example of a problem that humans can solve but computers struggle with is interpreting subtle emotions or making moral judgments in complex social situations. While computers can process data and recognize patterns, they cannot fully understand the nuances of human emotions or ethics, which often involve context, empathy, and experience. On the flip side, there are tasks that neither computers nor humans can perform, such as accurately predicting chaotic systems like long-term weather patterns. Despite advancements in artificial intelligence (AI), computer functions that perform tasks, such as visual perception and decision-making processes that usually are performed by human intelligence, these problems remain beyond our collective reach due to the inherent unpredictability and complexity of certain natural systems.

Theoretical computer science is often emphasized in undergraduate computer science programs because academic computer science emerged from mathematics, often to the detriment of perspectives that center on the social and technical values embodied by applications of computer technology. These perspectives, however, are gradually changing. Just as the design of ARPANET shaped the design of the Internet, computer scientists are also learning that the physical aspects of computer hardware determine what can be efficiently computed. For example, many of today’s artificial intelligence technologies rely on highly specialized computer hardware that is fundamentally different at the physical level compared to the general-purpose programmable silicon that has been the traditional focus of computer science. Organizations that develop human-computer interaction (HCI), a subfield of computer science that emphasizes the social aspects of computation, now host annual conferences that bring together thousands of researchers in academia and professionals in the industry. Computer science education is another subfield that emphasizes the cognitive, social, and communal aspects of learning computer science. Although these human-centered subfields are not yet in every computer science department, their increasing representation reflects computer scientists’ growing desire to serve not only more engaged students, but also a more engaged public in making sense of the values of computer technologies.

Computers and Racial Justice

Our histories so far have centered the interests of White American men in computer science. But there are also countless untold, marginalized histories of people of other backgrounds, races, ethnicities, and genders in computing. The book and movie Hidden Figures shares the stories of important Black women who were not only human computers, but also some of the first computer scientists for the early digital computers that powered human spaceflight at NASA (Figure 1.5).

Figure 1.5 Katherine Johnson, a Black computer scientist, recalculated the computations done by early digital computers for space flight planning at NASA. Her contributions were portrayed in the book and movie Hidden Figures. (credit: “Katherine Johnson at NASA, in 1966” by NASA/Wikimedia Commons, Public Domain)

In one chapter of Black Software, Charlton McIlwain shares stories from “The Vanguard” of Black men and women who made a mark on computer science in its early years from the 1950s through the 1990s through the rise of personal computing and the Internet, but whose histories have largely been erased by the dominant Silicon Valley narratives. Their accomplishments include leading computer stores and developing early Internet social media platforms, news, and blog websites. For example, Roy L. Clay Sr., a member of the Silicon Valley Engineering Hall of Fame, helped Hewlett-Packard develop its first computer lab and create the company’s first computers. Later, Clay provided information to venture capitalists that motivated them to invest in start-ups such as Intel and Compaq.⁵ In another example, Mark Dean was an engineer for IBM whose work was instrumental in helping IBM develop the Industry Standard Architecture (ISA) bus, which created a method of connecting a computer’s processor with other components and enabling them to communicate. This led to the creation of PCs, with Dean owning three of the nine patents used to create the original PC.⁶

Yet their efforts were often hampered by the way that computer science failed to center, or even accommodate, Black people. Historically, American Indians and Hispanic people did not have the same access as even Black Americans to computers and higher education. Kamal Al-Mansour, a technical contract negotiator at the NASA Jet Propulsion Lab, worked on space projects while Ronald Reagan was president. He recounts:

“It was conflicting . . . doing a gig . . . supporting missiles in the sky, (while) trying to find my own identity and culture . . . JPL was somewhat hostile . . . and I would come home each day [thinking] What did I accomplish that benefited people like me? And the answer every day would be ‘Nothing.’”⁷

Al-Mansour would go on to start a new company, AfroLink, finding purpose in creating software that centered on Black and African history and culture. This story of computer technologies in service of African American communities is reflected in the creation of the Afronet (an early social media for connecting Black technologists) and the NetNoir (a website that sought to popularize Black culture). These examples serve as early indicators of the ways that Black technologists invented computer technologies for Black people in the United States. Yet Black Software also raises challenging political implications of the historical exclusion of Black technologists. Black culture on the Internet has greatly influenced mainstream media and culture in the United States, but these Black cultural products are ultimately driving attention and money to dominant platforms such as X and TikTok rather than those that directly benefit Black people, content creators, and entrepreneurs. Computer technologies risk reproducing social inequities through the ways in which they distribute benefits and harms.

The digital divide has emerged as a significant issue, as many aspects of society -- including education, employment, and social mobility -- become tied to computing, computer science, and connectivity. The divide refers to the uneven and unequal access and distribution of technology across populations from different geographies, socioeconomic statuses, races, ethnicities, and other differentiators. While technological access generally improves over time, communities within the United States and around the world have different levels of access to high-speed Internet, cell towers, and functioning school computers. Unreliable electricity can also play a significant role in computer and Internet usage. And beyond systemic infrastructure-based differences, individual product or service access can create a divide within communities. For example, if powerful AI-based search and optimization tools are only accessible through high-priced subscriptions, specific populations can be limited in benefiting from those tools.

Computers and Global Development

Computer technology, like any other cutting-edge technology, changes the balance of power in society. But access to new technologies is rarely ever equal. Computer science has improved the quality of life for many people who have access to computer technology and the means of controlling it to serve their interests. But for everyone else in the world, particularly people living in the Global South, computer technologies need context-sensitive designs to meet their needs. In the 1990s, for instance, consumer access to the Internet was primarily based on “dial-up” systems that ran on top of public telephone network systems. Yet many parts of the world, even today, lack telephone coverage, let alone Internet connectivity. Research in computers for global development aims to improve the quality of life for people all over the world by designing computer solutions for low-income and underserved populations across the world—not just those living in the wealthiest countries.

Computer technologies for global development require designing around unique resource constraints such as a lack of reliable power, limited or nonexistent Internet connectivity, and low literacy. Computer scientists employ a variety of methods drawing from the social sciences to produce effective solutions. However, designing for diverse communities is difficult, particularly when the designers have little direct experience with the people they wish to serve. In The Charisma Machine, Morgan Ames criticizes the One Laptop Per Child (OLPC) project, a nonprofit initiative announced in 2005 by the Massachusetts Institute of Technology Media Lab. The project attempted to bring computer technology in the form of small, sturdy, and cheap laptops that were powered by a hand crank to children in the Global South. Based on her fieldwork in Paraguay, Ames argues that the project failed to achieve its goals for a variety of reasons, such as electricity infrastructure problems, hardware reliability issues, software frustrations, and a lack of curricular materials. Ames argues that “charismatic technologies are deceptive: they make both technological adoption and social change appear straightforward instead of as a difficult process fraught with choices and politics.” When the computers did work, OLPC’s vision for education never truly materialized because children often used the computers for their own entertainment rather than the learning experiences the designers intended. Though Ames’s account of the OLPC project (Figure 1.6) itself has been criticized for presenting an oversimplified narrative, it still represents a valuable argument for the risks and potential pitfalls associated with designing technologies for global development: technology does not act on its own but is embedded in a complicated social context and history.

Figure 1.6 In 2005, MIT’s Media Lab started the OLPC initiative to bring laptops to children in the Global South. An unexpected outcome they discovered was that designing technologies for global communities is not as straightforward as designers may initially believe. (credit: "One Laptop per Child" by OLE Nepal Cover/Flickr, CC BY 2.0)

Addressing these risks is not as simple as practicing humility and including communities in the design process. Many challenges in computing for global development are sociopolitical or technopolitical rather than purely technical. For example, carrying out a pilot test to evaluate the effectiveness of a design can appear as favoritism toward the pilot group participants. These issues and social tensions are especially exacerbated in the Global South, where the legacies of imperialism and racial hierarchies continue to produce or expand social inequities and injustices.

The identities of people creating computer technologies for global development are ultimately just as important as the technologies they create. In Design Justice, Sasha Costanza-Chock reiterates the call for computer scientists to “build with, not for,” the communities they wish to improve. In this way, Design Justice seeks to address the social justice tensions raised when asking the question, “Who does technology ultimately benefit?” by centering the ingenuity of the marginalized “user” rather than the dominant “designer.”

In some cases, underdeveloped countries can quickly catch up without spending the money that was invested to develop the original technologies. For example, we can set up ad hoc networks quickly today and at a portion of the cost in Middle Eastern and African countries using technology that was developed (at a high cost) in the United States and Europe over the past several decades. This means that sometimes, progress in one part of the world can be shared with another part of the world, enabling that area to quickly progress and advance technologically.