Design of Artificial Intelligence Products
Most of the time, people leverage analogy better than an algorithm. Furthermore, do we care more about accuracy or explainability? ¶ 5 Tribes of Machine Learning ¶ Symbolists — decision trees ¶ Connectionists — neural networks ¶ Evolutionaries — genetic algorithms ¶ Bayesians — probabilistic inference (prediction) ¶ Analogizers — clusters, support vectors (detect pattern)1!
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Oversimplified to think about it ¶ In order for designers to think about it, we can think about its feature as ¶ Classifier ¶ Optimizer1!
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Data Scientist vs. UX Designer ¶ Professor then talks about the tension between data scientists and designers. Data scientist tend to build models from the dataset to explain what is already happen. They claim to be the user expert by understanding users through data. On the other hand, designers are also user expert because we know how to conduct user research. As both sides claim they are user expert, there is a tension between these two disciplines.1!
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Business vs. User Experience ¶ How do we think about the intersection of business, AI and user? Thinking about the example of Netflix. The bandwidth is expensive so Netflix wants you to subscribe then not watch it at all. While a good recommendation seems to have great value for users, how does it benefit its business? What else can be done with the Netflix’s data besides recommendation?1!
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What UX value can ML offer? ¶ ML is something that can personalize for a lot of people. ¶ ML enables higher level abstraction in user instructions. ¶ Users foresee the relationship (with the product) improve, where the relationship is the recommendations we are giving them.1!
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What is a good problem for ML? ¶ Focus on problems that would be difficult to solve with traditional programming. ¶ Know the Problem Before Focusing on the Data1!
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