Once upon a time there was a 20-year-old who walked into a lab and saw robots wandering around a hall and couldn’t believe what she was seeing. That was me walking into the Artificial Intelligence Lab at MIT towards the end of the AI Winter of the 1990s. I remember using what I knew about Scheme to start working on a project in LISP and being intimidated by the knowledge of the people I was working with. Fast forward 25 years, I’m now amazed to see how far the field has come, and to see those robots in homes and at school.
Building upon deep and rich experiences in computer science, when our students start 11th grade, another beautiful stage of development becomes apparent. They’re more self-assured, their discourse is elevated to a new level, and their ability to tackle complex systems expands. Now is when we couple Engineering, Mathematics, and Computer Science together in our Artificial Intelligence classes. Students have been introduced to Artificial Intelligence topics in their computer science classes in Lower School and through electives like Self-Driving Cars, which prepare them for the topic of today's post, the capstone courses in AI.
In the Artificial Intelligence Research Lab our students learn concepts from the various fields of artificial intelligence, including practical exercises using graphics processing units (GPUs), and experimenting with parallel computing. We introduce them to topics in machine learning such as learning theory, data preparation, supervised and unsupervised learning, computer vision, parallel computing, and convolutional neural networks. Using various real-world applications focused on computer vision, our kids are introduced to topics about the theory and practical algorithms for machine learning using Python and libraries such as TensorFlow, Keras, and OpenCV and parallel computing technologies, such as the CUDA platform. While they are learning these technologies and working on smaller projects, we work across the curriculum in English class where they learn to write a funding proposal for their year-end project while reading about the history of Artificial Intelligence. And, as part of their projects, students learn how to use current technologies, such as Amazon Web Services (AWS), to store and manipulate their data sets. The course involved an AI Review Committee made of CTOs at AI companies, practicing professionals in AI, and an AI professor from George Mason University. The students presented to the committee at two points: 1) at the proposal stage for approval and funding and 2) project completion; an experience that provided real-world experience.
A second course, which we are piloting this year, is the Autonomous Cognitive Assistant Lab. Autonomous Cognitive Assistant is a machine learning and data science, college-level course that leverages the course curriculum developed at MIT's Beaver Works Center to implement audio, vision, and natural language processing projects. This fascinating course, which also uses Python, also teaches just-in-time math concepts such as linear algebra and Fourier transforms. What differentiates our classes from others that I have seen that introduce students to Artificial Intelligence is that our students develop their own code to implement their projects.
In both courses, students spend the second half of the year on their capstone project, where they are able to implement a project of their own. Using the engineering design process, they define a real-world problem to solve or address, then design and implement their solution. In addition to their implementation, students learn how to document a project proposal, document their analysis, conduct testing, and write a final report and poster that they use to present to an external audience and members of the school community.
The classes are open to juniors and seniors who have taken AP Computer Science, and when I watch the students present their work, I’m amazed at how far they have come from the young children who I met many years before. These cross-curricular projects bring together years of education in engineering, computer science, math, science, and English, with real-life lessons in project planning, communications, presentation, and problem solving. I can’t wait to hear from our graduates about the work they go on to do not only in college, but also when they enter the workforce. I’m thankful for the educators, Mr. Ryan, Ms. Emily, Mr. Johnny, and Ms. Anne, who have been willing to learn, experiment, and innovate with the kids in order to teach the current technologies of the field, as well as for the experts from both academia and industry who have donated their time to evaluate our students' proposals and presentations so that our future engineers and computer scientists have an opportunity to present their work and obtain authentic feedback.
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