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How Might We Make Understanding Algorithms Easier for Everybody?

How Might We Make Understanding Algorithms Easier for Everybody? AI-kitchen ran from 2015-2017 and focused on how to make it easy to discuss and critique how algorithms work. We worked with collaborators to translate algorithmic ‘writing’ into plain language to help people understanding how AI works and how it could be improved. We believe that the basic inputs, assumptions, steps, and outputs of algorithms should be as accessible as sharing and discussing recipes. To do this, our team reverse-engineered popular algorithms like Tinder’s matching algorithm and AirBnB’s location algorithm, and hosted regular discussion events at Harvard where we presented on how they work in plain language. Some of our core themes included: Human Values and How They Relate to AI; Algorithmic Writing and Critique; The Design of AI; Human-Robot Interaction; Human-Computer Interaction, and How to make AI more accessible, representative, and desirable. Ai-kitchen began as a mix of professionals and

How Might We Train the Next Generation of Tech Leaders?

How Might We Train the Next Generation of Tech Leaders? In 2017-2018, Altringer has been building upon this research to co-design curriculum for a new dual graduate degree program at Harvard: the engineering MS + MBA. She and her team are also working to eventually make this body of work interactive and available to the public.

Evidence-based Design Education

How Might We Make Design and Innovation Education More Evidence-Based? This multi-year study involved in-depth qualitative and quantitative analysis of over 300 projects in top design firms like IDEO. It examined the real-world complexity of innovation projects, which often involve multi-disciplinary, multi-cultural and multi-organizational collaboration, searching for patterns associated with more (and less) successful outcomes. This research has been supported by the Harvard Initiative for Learning and Teaching. Previous support came from MIT International Design Center and the University of Cambridge. The work was later continued as a six-year longitudinal study on the effectiveness of project-based experiential design and innovation courses.

How Might We Make a Fun Game to Learn to Think Like a Chef?

Can a Fun Game Teach You to Think Like a Chef? *NEW* Later in 2018, we will release a consumer mobile game for learning flavor fluency. Sign up to be a beta ‘taster’ at We generally update the current status of related flavor projects on the Flavor Genome Project website.

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This section is for students enrolled in the Fall 2020 ES285 course at Harvard University