Literacy is defined as the ability to read, write, speak and listen, and use numeracy and technology, at a level that enables people to express and understand ideas and opinions, to make decisions and solve problems, to achieve their goals, and to participate fully in their community and in wider society. What does this mean within the realm of Artificial Intelligence (AI) and why is it important for not only our future, but also our present?
AI Literacy begins with a basic understanding of what AI is, the language surrounding the technological and social aspects of AI, how AI works and how it is currently playing a role in our daily lives, in addition to the potentiality of future implementations in all professions and industries. The objective of AI Literacy is to eradicate the misconceptions around AI and to create an all-inclusive ecosystem where all members of the community are armed with the basic skills needed to pursue further learning to better adapt to a changing world where AI will be prevalent.
In recent years, jobs across all levels require understanding and usage of technology. As a result, computer and digital literacy is the #1 entry-level skill needed in the job market.
Computer literacy allows us to engage with society — finding a job, ordering takeout, searching an answer to a question — in ways previously unimaginable. Similarly, AI literacy is becoming increasingly necessary as well, as artificial intelligence systems become more integrated into our daily lives.
Data literacy is required for data mining, in which data are exploratorily analyzed to examine a phenomenon. In machine learning, statistical literacy is required to analyze big data. In view of the fact that data mining and machine learning represent main AI technologies, data literacy and statistical literacy continue to be key elements. What about AI literacy? In daily life, none of us would create a deep learning algorithm or is expected to have such ability. AI literacy refers to being conscious about whether there are any standardized/formatted tasks that have not been applied for AI due to large data volume, and whether excessive labor, money or time is spent for “categorization, repetition, exploration, organization and optimization.” We should make it a habit to think about what can and cannot be applied to AI. AI can learn much more than just quantitative data. Recording a range of information including text, sound, and images is valuable. For example, record your mother’s recipe as digitized text. When a cooking robot becomes reality in the future, having the data for one recipe could lead to a new business of reproducing home cooked meals. We should constantly examine whether the information we possess has the potential of generating high value added, be emotionally prepared to let go of tasks that AI can perform in our place, and start learning and investing in our strengths that they cannot perform.