Learn about Artificial Intelligence (AI) | Code.org

AI and Machine Learning impact our entire world, changing how we live and how we work. That's why its critical for all of us to understand this increasingly important technology, including not just how its designed and applied, but also its societal and ethical implications.

Join us to explore AI in a new video series, train AI for Oceans in 25+ languages, discuss ethics, and more!

Learn about how AI works and why it matters with this series of short videos. Featuring Microsoft CEO Satya Nadella and a diverse cast of experts.

Students reflect on the ethical implications of AI, then work together to create an AI Code of Ethics resource for AI creators and legislators everywhere.

We thank Microsoft for supporting our vision and mission to ensure every child has the opportunity to learn computer science and the skills to succeed in the 21st century.

With an introduction by Microsoft CEO Satya Nadella, this series of short videos will introduce you to how artificial intelligence works and why it matters. Learn about neural networks, or how AI learns, and delve into issues like algorithmic bias and the ethics of AI decision-making.

Go deeper with some of our favorite AI experts! This panel discussion touches on important issues like algorithmic bias and the future of work. Pair it with our AI & Ethics lesson plan for a great introduction to the ethics of artificial intelligence!

Resources to inspire students to think deeply about the role computer science can play in creating a more equitable and sustainable world.

This global AI for Good challenge introduces students to Microsofts AI for Good initiatives, empowering them to solve a problem in the world with the power of AI.

Levels 2-4 use a pretrained model provided by the TensorFlow MobileNet project. A MobileNet model is a convolutional neural network that has been trained on ImageNet, a dataset of over 14 million images hand-annotated with words such as "balloon" or "strawberry". In order to customize this model with the labeled training data the student generates in this activity, we use a technique called Transfer Learning. Each image in the training dataset is fed to MobileNet, as pixels, to obtain a list of annotations that are most likely to apply to it. Then, for a new image, we feed it to MobileNet and compare its resulting list of annotations to those from the training dataset. We classify the new image with the same label (such as "fish" or "not fish") as the images from the training set with the most similar results.

Levels 6-8 use a Support-Vector Machine (SVM). We look at each component of the fish (such as eyes, mouth, body) and assemble all of the metadata for the components (such as number of teeth, body shape) into a vector of numbers for each fish. We use these vectors to train the SVM. Based on the training data, the SVM separates the "space" of all possible fish into two parts, which correspond to the classes we are trying to learn (such as "blue" or "not blue").

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Learn about Artificial Intelligence (AI) | Code.org