into the future, and an effective economic strategy is to concentrate resources in domains that will accelerate technological progress. Specific technological domains include software, communications, the cloud, and robotics, and the fields gaining attention for their rapid evolution spurred on by these technologies are the Internet of Things (IoT), big data, and artificial intelligence (AI).

### a. Internet of Things

Linking, via networks, things, people, services, and information on a massive scale will generate new value. In this context, Internet of Things (IoT) is the term given mainly to the interconnection of things. Gartner, in the U.S., defines IoT as “the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment.

### b. Big data

Big data, as a keyword, first gained prominence in 2011 when it was used in a McKinsey ad in the United States. Data usage is distinctive in its non-competitiveness and its near-zero marginal costs of reproduction. The near future will see the real-time accumulation of many kinds of unstructured big data lacking any format, in addition to structured data. Coupled with advancements in IoT, big data will profoundly change social systems through the analysis of data, which is benefiting from the exponential growth in computing power.

### c. Artificial intelligence

The history of artificial intelligence (AI) research can be divided into 3 major stages (Figure 1-1-2-1).

Deep learning technology is gaining attention as a tremendous leap forward in the capability of machines to analyze abstract information, using representations inspired by brain anatomy. If software reaches the point where it can understand language, then it could obtain the ability to predict and analyze humans through autonomous computer learning. This development could advance AI into the realm of imagination and creativity.

## (2) Social and economic impact of new forms of ICT

A relationship is being formed between IoT, big data, and AI. IoT is used to collect all kinds of data to visualize the current situation. Then, various types of data are accumulated on multiple levels over time (creating big data) and AI is applied to process and analyze these big data sets to forecast the future. In this chapter, we will refer to this integrated concept as IoT in the broad sense (Figure 1-1-2-2). Using the concept of IoT in the broad sense, it becomes possible to create new values.

More specifically, IoT in the broad sense enables the realization of innovation in many different configurations: process innovation for the optimization of business processes at enterprises, product innovation for the development and provision of new goods and services that tap latent demand, marketing innovation connected to the design and marketing of goods and services, organization innovation for business practices and organiza-

Figure 1-1-2-1 AI categories and comparisons

Description	Extraction of elements (features) to be detected	Discovery of connections between features
Second AI explosion (Knowledge representation) 1980s to around 1995	Information necessary for the computer to perform some inference or deduction must be notated as "knowledge" in a format the computer can recognize. Since humans couldn't possibly notate all the vast amount of information in the world for computers, AI applications were limited and the AI boom cooled off for a time.	Done by humans	Done by humans
Machine learning (in the narrow sense) Around 2000 to present	Machine learning, in a narrow sense, is technology in which humans teach the computer what elements are to be detected and then give the computer a large amount of data (numbers, text, images, voices, etc.). The computer then learns rules and knowledge on its own (i.e., the computer notates connections between elements and improves the accuracy of its deductions and decisions). Analyzing big data is 1 well-known application of machine learning.	Done by humans	Done by computers
Deep learning Around the mid-2010s to present	Deep learning is 1 technique of machine learning in the broad sense of the term. By extracting information 1 layer at a time across multiple layers, the computer is able to represent high levels of abstraction and discover on its own elements to be detected. Practical applications for deep learning are being pursued in speech recognition, image recognition, and natural language processing.	Done by computers	Done by computers

(Source) Table created based on "Study Report on ICT Evolution's Impact on Employment and Work Patterns," MIC (2016)