{"id":7002,"date":"2017-03-03T13:18:46","date_gmt":"2017-03-03T13:18:46","guid":{"rendered":"https:\/\/marvel7077.wpengine.com\/?p=7002"},"modified":"2022-03-25T07:40:59","modified_gmt":"2022-03-25T07:40:59","slug":"testing-ai-concepts-user-research","status":"publish","type":"post","link":"https:\/\/marvelapp.com\/blog\/testing-ai-concepts-user-research\/","title":{"rendered":"Testing AI Concepts in User\u00a0Research"},"content":{"rendered":"

It is clear that intelligent algorithms such as deep learning, machine learning, artificial intelligence, etc. will have a major impact on the world. However, what isn\u2019t clear is how these technologies can be used to solve real problems for people.<\/p>\n

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Patrick, your friendly robot helper, as designed by my amazing teammates at Philosophie, Jamie<\/a> and Leah<\/a>.<\/p><\/div>\n

At Philosophie<\/a> we work on experiences that help humans in their day-to-day tasks. We hope to join the benefits of intelligent algorithms with the things that humans do best: deal with complex and creative endeavors that solve their problems towards some purpose.<\/p>\n

In this post we will take you through the methods we have used at Philosophie to validate how intelligent algorithms could be used to solve real world problems.<\/p>\n

Centaurs or\u00a0cyborgs?<\/h2>\n
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Scene from COMPUTER CHESS<\/a><\/p><\/div>\n

Our inspiration comes from intelligent algorithms being used in the domain of chess, one of the first \u2018hard\u2019 games to be conquered by deep learning. After Garry Kasparov was beaten by Deep Blue he was inspired to start a new form of chess that combines humans and computers together called Advanced Chess. It is also referred to as centaur, advanced, and cyborg chess.<\/p>\n

Advanced Chess - Wikipedia<\/span>Advanced Chess is a relatively new form of chess, wherein each human player uses a computer chess program to help him\u2026<\/span><\/span><\/span><\/a><\/section>\n

As in the centaur model, we look to the intelligent algorithms as assistants, helpers, and to help the humans they work with achieve the human\u2019s purposes. Not the other way around. It is much like a partnership since the algorithms need to learn from the human counterpart as well.<\/p>\n

In this article I try to avoid talking about particular technologies or techniques because people don\u2019t care what you use as long as it helps solve a problem. Generally the systems we are considering use supervised or semi-supervised learning<\/a> techniques, but it could really be anything (GANs are pretty cool right now<\/a>).<\/p>\n

Always get started with a\u00a0problem<\/h2>\n
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\"First, as with any endeavor, you need to have a real problem that you are trying to solve.\"<\/p><\/span><\/blockquote>\n

At this stage of intelligent algorithms, you should be targeting the parts of a problem that are busywork. By busywork, I mean those aspects that feel like routine, are easily documented (say in a manual), or are a heuristic commonly used.<\/p>\n

Ideation without an AI\u00a0PhD<\/h2>\n

Once you have identified an important problem to solve, you need to ideate solutions with intelligent algorithms in mind. We love to do Crazy Eights when ideating<\/a> and this is a perfect time to do so.<\/p>\n

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A Crazy Eights exercise sketch by yours\u00a0truly<\/p><\/div>\n

To be clear, we don\u2019t assume that in this process you are building anything yet. We are just looking for ideas that an intelligent algorithm acting like another human helping the person. When sketching (and later prototyping) we will just assume some intelligent algorithm \u2018magic\u2019 happens behind the scenes.<\/p>\n

There are a few questions you should ask yourself when ideating for intelligent algorithms:<\/p>\n