第1章コンピューターサイエンス入門

By the end of this section, you will be able to:

• Discuss how computer scientists develop foundational technologies

• Discuss how computer scientists evaluate the negative consequences of technologies

• Discuss how computer scientists design technologies for social good As noted earlier, computer science is a powerful tool, and computer scientists have vast technological knowledge that continues transforming society. Computer scientists have an obligation to be ethical and good stewards of technology with an emphasis on responsible computing. Written code influences daily life, from what we see on social media to the news stories that pop up in a Google search and even who may or may not receive a job interview. When computer scientists don’t consider the ramifications of their code, there can be unintended consequences for people around the world. The Y2K problem, also known as the “millennium bug,” is a good example of shortsighted decisions that allowed computer scientists to only store the last two digits of the year instead of four. This made sense at a time when memory was expensive on both mainframe computers and early versions of personal computers. The Y2K problem was subsequently coined by John Hamre, the United States Deputy Secretary of Defense, as the “electronic equivalent of the El Niño.”¹⁵ The future of computer science will highly affect the future of the world. Although we often think of computer technologies as changing the way the world works, it is actually people and their vision for the future that are amplified by computing. The relationship between computer science and people is about how computer technologies can bias society and how the choices made through computer systems can both promote and discourage social inequities. Computer technologies can encode either value or both values in their designs. In this section, we’ll introduce three ways that computer science can shape the future of society: developing foundational technologies, evaluating negative consequences of technologies, and designing technologies for social good.

We’ve seen how foundational technologies like artificial intelligence, algorithms, and mathematical models enable important applications in data science, computational science, and information science.

As noted previously, artificial intelligence (AI) is the development of computer functions to perform tasks, such as visual perception and decision-making processes, that usually are performed by human intelligence. AI refers to a subfield of CS that is interested in solving problems that require the application of machine learning to human cognitive processes to achieve goals. AI research seeks to develop algorithm architectures that can make progress toward solving problems. One such example is image recognition, or the problem of identifying objects in an image. This problem is quite difficult for programmers to solve using traditional programming methods. Imagine having to define very precise rules or instructions that could identify an object in an image regardless of its position, size, lighting conditions, or perspective. As humans, we have an intuitive sense of the qualities of an object. However, representing this human intelligence in a machine requiring strict rules or instructions is a much harder task. AI methods for image recognition involve designing algorithm architectures that can generalize across all the possible ways that an object can appear in an image.

Agricultural robots help large-scale industrial farmers produce crops more efficiently and support sustainability efforts. One agricultural robot is now being used to improve fertilizer and pesticide treatments by taking pictures of plants as a farmer drives a tractor over the field. Artificial intelligence techniques are used to recognize and identify the lettuce plants and weed plants in the image. For each identified lettuce or weed plant, the robot makes a personalized decision about the best chemical treatment for the plant in real time as the tractor moves to the next row of crops. This ability to personalize chemical treatments improves yields and plant quality for large-scale industrial agriculture by producing more crops with fewer chemicals.

15 “Looking at the Y2K bug,” portal on CNN.com. Archived 7 February 2006 at the Wayback Machine. https://web.archive.org/web/ 20060207191845/http://www.cnn.com/TECH/specials/y2k/

Recent approaches to AI for image recognition draw on a family of methods called neural networks instead of having programmers craft rules or instructions by hand to form an algorithm. In humans, the neural network is a complex network in the human brain that consists of neurons, or nerve cells, connected by synapses that send messages and electrical signals to all parts of the body, enabling us to think, move, feel, and function.

In computer science, a neural network (Figure 1.9) is an AI algorithm architecture that emphasizes connections between artificial nerve cells whose behavior and values change in response to stimulus or input. These neural networks are not defined by individual neurons but by the combination of all the neurons in the network. Typically, artificial neurons are arranged in a hierarchy that aims to capture the structure of an image. Although the first level of neurons might respond to individual pixels, later levels of artificial neurons might respond in aggregate to the arrangement of several artificial neurons in the preceding layer. This is similar to how the human visual system responds to edges at the lower levels, then responds in aggregate to the specific arrangement of several edges in later levels, and ultimately identifies these aggregated arrangements as objects.

The idea of neural networks, however, is not as new as it might seem. Artificial neural networks were first imagined in the mid-1900s alongside contemporary research efforts in the cognitive sciences. The ideas of multilayered, hierarchical networks of neurons and the mathematical optimization methods for learning were all there, but these early efforts were limited by the computational processing power available at the time. In addition, the large datasets that drive neural network learning were not nearly as available as they are today with the Internet. Developments in foundational technologies such as computer architecture and computer networks paved the way for the more recent developments in neural network technologies. The broad area of computer systems investigates these architectures and networks that enable new algorithms and software. Without these technologies, neural networks would not be nearly as popular and revolutionary as they are today. Yet the relationship between computer systems and AI development is not one-directional. Today, computer scientists are using neural networks to help design new, more efficient computer systems. The development of foundational computer technologies not only creates opportunities for direct and indirect applications, but also supports the development of other computer technologies.

Just as we saw how technological fixes embodied a powerful belief about the relationship between computer solutions and social good, a similar cultural belief exists about the relationship between foundational technologies and their social values. The belief that technologies are inherently neutral and that it is the people using technology who ultimately make it “good” or “bad” is considered social determination of technology.