How to Build an AI in a Day?

  1. Purpose: What is the AI going to be used for? This question has the biggest impact on everything else in the system.
  2. Learning Rules: What and how will the AI learn, and how is that learning encoded? This is the trickiest part. The best tip would make it flexible and modular because typically this goes through multiple iterations.
  3. Business Rules: How does the machine decide based on some inputs? These rules can be learned or be provided by humans, esp. for priming the engine. It is also important to identify an unknown answer from the wrong answer and report it as such.
  4. Learning Corpus: What information, and in what format, should the machine consume to take through its Learning Rules. While this is the easiest part, in practice it could take the most time to make the right readers, APIs, ETLs, etc.
  1. Purpose: Decide whether the car needs a charge to reach a given destination.
  2. Learning Rules: Translate all the data from GPS and battery usage to a simple formula per model based on elevation, miles through city, mile on highway, time of day and traffic patterns. Add margin of error, and learn simple correlations.
  3. Business Rules: Written by above. Primarily correlating percentage of battery remaining and distance that can be safely travelled, and distance that can potentially be travelled under the right conditions, for each car model.
  4. Learning Corpus: Google map APIs and the data, which is presumably in a Big Table kind of place.



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Praful Krishna

Praful Krishna

AI, Product, Strategy, Digital Transformation