As the progenitor and leader of SAIL ON, the Stanford AI Lab’s year-round effort to attract and keep underrepresented minorities in the field of Artificial Intelligence, I engage high schoolers about artificial intelligence, machine learning, and the positive social impacts of our field.  SAIL ON meets once a month in the Computer Science building at Stanford.  Its trifold focus allows past participants in the SAILORS two-week summer camp to continue to learn about AI, to nurture strong relationships with each other, and to lead outreach projects that bring the technical, humanistic, and diversity missions of the AI outreach program to the wider community.

As the educational component of the October meeting of SAIL ON, we discussed and applied Naive Bayes modeling.  Like other machine learning methods, Naive Bayes is a generic approach to learning from specific data such that we can make predictions in the future.  Whether the application is predicting cancer/whether you’ll care about an email/who will win an election, we can use the mathematics of Naive Bayes to connect a set of input variables to a target output variable.  (Of course, some problems are harder than others!)

We focused on the derivation of Naive Bayes with a chalk-and-talk discussion (slides), identifying why Naive Bayes is mathematically justified and posing some deeper thought questions.  We checked understanding with a hand-calculation of a Naive Bayes problem (handout): does a shy student received an A in a class, given some observations about her and some observations about more forthcoming students?  We then turned to a Jupyter Notebook that applies the same methods on a larger scale, working on the Titanic challenge from Kaggle with an applied introduction to pandas and sklearn: given passenger manifest records, can we predict who survived?

By providing this basis, I hope to increase appreciation for applications of what students are seeing in their math classes, and to facilitate students moving further on their own with applied machine learning before November’s meeting.

One of my favorite parts of studying linguistics was being presented with data and being asked to find the system within it. Language data, with linguistic theory’s insistence that everything must make sense, make the most excellent data and logic puzzles.

As part of preparing for a spatial grammar-heavy meeting of the Montgomery Blair High School Linguistics Club, I developed three American Sign Language morphology problems.  These problems illustrate interesting properties of American Sign Language that spoken languages do not have (non-manual markers, spatial agreement, and a rich temporal inflection system based in manual phonology).

Try your hand at doing the problems if you’re interested in any of the following:

• What it means to do theoretical linguistics (or the sort of logic skills that linguists develop)
• Unique properties of spatial languages
• Basic American Sign Language linguistics
• Similarities between American Sign Language and other world languages

Unlike most materials on ASL linguistics, the problems don’t assume that readers are fluent in American Sign Language or in linguistic theory — I developed these problems because I couldn’t find any resources aimed at an intelligent lay non-Deaf audience.  The problems deliberately walk users through the steps to answer a question, whereas most theoretical linguistics problem sets jump straight to the questions at hand and assume existing familiarity with linguistic features not observed in English.

Once the club and I meet, I’ll post the answer sheet as well.