software

Students learn how pharmacies work and how Amazon Pharmacy uses technology to help deliver safe and affordable medication.

Students learn how prescriptions are received and how AI and machine learning help enter prescriptions quickly and accurately. Pharmacists review them for dosage, accuracy, and medication interactions.

Students see how robots and automation help fill prescriptions. Robots use computer systems, algorithms, and sensors to dispense, cap, and label medication.

Students learn how computer science and machine learning check filled prescriptions for accuracy and safety. Then, medications are prepared for delivery, including same-day delivery in some locations.

Students learn that every AI system is built on three essential building blocks: data (large, organized collections of information), models (which learn patterns from data), and computing power (special hardware that trains and runs AI). They explore how the size and quality of a dataset directly affects AI performance.

Students learn how AI improves through reinforcement learning — a process of trying things, getting feedback, and gradually getting better — and how simulations provide safe, virtual environments where AI can practice tasks and make mistakes without causing real-world harm.

Students learn how AI agents can independently take actions — like browsing websites, filling out forms, and completing multi-step tasks — and how automation handles repetitive work so humans can focus on creative thinking, complex problem-solving, and decisions that require human judgment.

Students learn how User Experience (UX) and design shape the way people interact with AI products. They discover that good design makes technology easy to understand, enjoyable to use, and inclusive for as many people as possible — and that testing with real users and gathering feedback is essential.

Students learn why ethics and guardrails are essential for building safe, fair, and beneficial AI systems. They discover that humans must stay in the loop — reviewing AI recommendations and making final decisions — and that careers in AI policy, law, and social science are just as important as technical roles.

Students learn that AI needs many different talents — engineers, designers, researchers, data scientists, and many others all work together to build AI. They reflect on the overarching question: “What has this tour taught you about AI, and how might the careers you’ve seen today connect to your own interests and future?” The key takeaway: there is no single path into AI. Curiosity, creativity, problem-solving, and teamwork can be just as important as technical skills — and the AI of the future will be built by their generation.