第1章コンピューターサイエンス入門

Not only is computation interdisciplinary, but other disciplines are also becoming more and more computational. In The Invisible Future, Nobel Laureate biologist David Baltimore defines DNA in computational terms. He states that biology is an information science because DNA encodes for the outputs of biological systems. The interdisciplinary field studying information technologies and systems as they relate to people, organizations, and societies is called information science. The role of information in natural sciences can also be found in the physics of quantum waves that carry information about physical effects, in the chemical equations that specify information about chemical reactions, in the information flows that drive the evolution of economies and political organizations, and in the information processes underlying social, management, and communication sciences.¹³

Research into DNA sequencing and indexing is opening new ways of helping medical providers offer personalized treatments for patients. Large-scale genome sequencing of not only the human genome, but also the DNA signatures for viruses has made it possible for medical providers to take human fluid samples and analyze them for the presence of infectious diseases. This research requires computer science concepts, including specialized medical computer devices to sequence the billions of nucleotides that form a DNA sequence, data structures and algorithms to efficiently process and identify DNA signatures, and the miniaturization of computer hardware so that this technology is accessible (both in terms of price and physical size) in more and more care centers.

Although information science has its roots in information classification, categorization, and management in the context of library systems, information science today is a broad field that encompasses the many diverse ways information shapes society. For example, today’s social media networks provide more personable and instantaneous information communication compared to traditional news outlets—billions of people around the world are using social media to engage with information about the world. For many people, social media may be the primary way that they learn about and make sense of the world (Figure 1.8). Yet, we’ve already seen risks associated with information technologies such as the Internet. In today’s “information age,” information has more power than ever before to reshape society. Information scientists, data scientists, computational scientists—and, therefore, computer scientists—have a social responsibility: “One cannot reap the reward when things go right but downplay the responsibility when things go wrong.”¹⁴

13 P. J. Denning, “Computing is a natural science,” Commun. ACM, vol. 50, no. 7, pp. 13–18, July 2007. https://doi.org/10.1145/ 1272516.1272529. 14 R. Benjamin, “Race after technology: Abolitionist tools for the new Jim code,” 2019, Polity.

Despite the centrality of information to decision-making and social change, dominant approaches to computer science tend to focus on computational structures, systems, and processes (such as algorithms) that describe one kind of information by focusing on the what or how of solving problems with computers, but less often the why or who questions. Information science broadly centers people, organizations, and society in the study of information technologies.

In presenting data science, computational science, and information science, we’ve introduced the idea that computer science can shape other disciplines. But we’ve also raised questions about what computer science is today. If computer science is the study of all “phenomena surrounding computers,” it could also involve data science, computational science, bioinformatics, cheminformatics, computational social science, medical informatics, and information science. As you will learn in Chapter 13 Hybrid Multicloud Digital Solutions Development, another aspect of computer science is responsible computing, which includes the appropriate management of cyber resources as well as robust cybersecurity. It is difficult to define computer science today because it is so widely used by people across the world in diverse capacities. Definitions are about defining boundaries and excluding practices, which may be helpful for understanding the practices of a certain culture or group that is “doing” computer science, but it can never truly represent everyone and all the things that people are doing with computer science. However, computer science’s historical roots in mathematics shape the way it categorizes subfields:

• Theoretical computer science

◦ Theory of computation

◦ Information representation

◦ Data structures and algorithms

◦ Programming language and formal methods

• Computer systems

◦ Architecture

◦ Artificial intelligence

◦ Networks

◦ Security

◦ Databases

◦ Distributed computing

◦ Graphics

• Applied computer science

◦ Scientific computing

◦ Human-computer interaction

◦ Software engineering

In this hierarchy, theoretical computer science and computer systems are treated separately from applied computer science and human-computer interaction, suggesting that the mathematics of computing are pure and separate from social questions. Yet we’ve seen several examples that question this paradigm and instead point to a structure where human-computer interaction is infused throughout the study of computer science and all its subfields.

Today, computer science is a field that is just as much about people as it is about computer technology because each of these subfields is motivated by the real-world problems that people ultimately want to solve. The subfields of artificial intelligence and machine learning have applications that directly influence human decision-making, ranging from advertisement targeting to language translation to self-driving cars. Effective computational solutions to research or business problems require combining specific knowledge with computer science concepts from a combination of areas. For example, the computational science application of weather prediction combines knowledge about various subfields of computer science (algorithms, distributed computing, computer systems) with knowledge about climate systems. Theoretical computer scientists are increasingly interested in asking questions such as, “How do we design, analyze, and evaluate algorithms or information systems for fairness? How do we even define fairness in a computer system that strips away the complexities of the real world? What ideas or information are encoded in the data? And what are the limits of our approaches?” Computer science is a complex field, and its synergistic nature means that when computer science is used in an interdisciplinary manner that shapes other disciplines, its impact on society is much greater than when each discipline functions on its own.