Modern AI technologies started in the 1990s after big data was introduced to train and marry the AI models. Lately, the AI term has been used for any instance where a computer acts like a human. The underlying technology is machine learning (ML), which allows computers to learn directly from data to discover patterns and learn tasks on its own. The question is how should we teach ML to our children? ML requires logic and mathematical reasoning that can be applied to many endeavors. Learning code and ML modeling is a way to let them see how these things work. Doing so empowers them to become creators with technology instead of mere consumers.
In recent years, there has been a tremendous push to encourage children to start coding early. Learning to code introduces them to a type of thinking that will help them later in life, even if they do not become programmers, because it requires logic and mathematical reasoning that can be applied to many endeavors. Learning to code is a way to let them see how these things work. Doing so empowers them to become creators with technology instead of mere consumers.
Data is the electricity of the twenty-first century. Helping children understand how to collect, examine, and analyze data sets them up for success in the world of big data. We are moving toward a society where data-driven methods are increasingly shaping our future. Consider, for example, how data is transforming fields like education, the criminal court system, and health care. This trend will not be limited to IT jobs. As the sensors become more advanced, data collection will start happening in multiple ways. Thus, it is not far-fetched to assume that our children will eventually work jobs or build businesses that live on data. They may not all become data scientists or analysts, but they will likely need to be familiar with data processes.
Learning code and ML modeling will help develop their critical thinking ability and help them connect the “how” with the “why.” It is not enough to learn how to build AI applications; we should teach them why they should do it. What does it mean to outsource reasoning and decision-making to machines? Children, more than us, need to think about these issues because they will inherit this world.
Four different approaches have been integrated with Machine Learning models:
• Machine Learning using Scratch Coding. Small sample data for each label. No mathematics involved with hidden ML algorithms (Grades 3-6)
• Machine Learning using Python Coding. Big industrial data sets and real applications. No mathematics involved with hidden ML algorithms (Grades 5-12)
• Machine Learning algorithms using Python Coding. Big industrial data sets and real applications. (Grades 10-adults)
• Machine Learning smart devices (Grades 10-adults)
To summarize, through our AI ML projects the children will:
1. Gain programming skills and logical reasoning ability
2. Become familiar with the world of big data and understand the differences between rule-based models and ML models
3. Create smart systems with AI ML models, like chatbots
4. Build up their computational thinking and design thinking capabilities