Calling For Volunteers To Translate Experience AI

Calling For Volunteers To Translate Experience AI

As big fans of Raspberry Pi, we are delighted and impressed with the newly launched Experience AI program by Raspberry Pi and DeepMind. Experience AI is a learning program that helps educators teach artificial intelligence (AI) and machine learning (ML) in schools.



We all know AI is a powerful computer tool, but it can appear high-tech and complicated, especially for teachers and students. Thanks to Raspberry Pi Foundation and DeepMind, who have developed this practical and easy-to-follow curriculum that comes with all the necessary teaching materials (lesson plans, slides, worksheets, videos, etc.) for the secondary school level (ages 11 to 14).

To further benefit teachers and students in our home country of Thailand, we have decided to call for volunteers to help us translate the teaching materials (lesson plans, slides, and worksheets) into Thai. We want to see more young people in Thailand possess the basic knowledge and skills in AI and machine learning. As a token of appreciation, we will be giving out a set of Raspberry Pi souvenirs (T-shirts, caps, keychains, etc.) to our volunteers, and we will feature your name on our website. You will be remembered!

Experience AI comes with six lessons, each containing a lesson plan, slides, and several worksheets. We estimate it will take 8-10 weeks (2-3 hours per week per volunteer) if we have 6-10 volunteers in a team. If you are interested in joining us, please fill out this form.

The 6 Lessons cover:

1. What is AI?: Learners explore the current context of artificial intelligence (AI) and how it is used in the world around them. Looking at the differences between rule-based and data-driven approaches to programming, they consider the benefits and challenges that AI could bring to society. 

2. How computers learn: Learners focus on the role of data-driven models in AI systems. They are introduced to machine learning and find out about three common approaches to creating ML models. Finally the learners explore classification, a specific application of ML.

3. Bias in, bias out: Learners create their own machine learning model to classify images of apples and tomatoes. They discover that a limited dataset is likely to lead to a flawed ML model. Then they explore how bias can appear in a dataset, resulting in biased predictions produced by a ML model.

4. Decision trees: Learners take their first in-depth look at a specific type of machine learning model: decision trees. They see how different training datasets result in the creation of different ML models, experiencing first-hand what the term ‘data-driven’ means. 

5. Solving problems with ML models: Learners are introduced to the AI project lifecycle and use it to create a machine learning model. They apply a human-focused approach to working on their project, train a ML model, and finally test their model to find out its accuracy.

6. Model cards and careers: Learners finish the AI project lifecycle by creating a model card to explain their machine learning model. To finish off the unit, they explore a range of AI-related careers, hear from people working in AI research at DeepMind, and explore how they might apply AI and ML to their interests.

For more details about Experience AI's lessons, please click here: