How can Business Leaders 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. Machine Rules: What and how will the AI learn, and how is that learning encoded? This is the trickiest part. The best tip would be to 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. Training Data: What information, and in what format, should the machine consume to think through its Learning Rules. While this is the easiest part, in practice it could take the most time to make the right interfaces, APIs, ETL, etc.
  1. Purpose: Decide whether the car needs a charge to reach a given destination.
  2. Machine 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. A simple neural network, perhaps with one or two hidden layers should be sufficient.
  3. Business Rules: Written by above. Primarily correlating percentage of battery remaining and distance that can be safely traveled, and distance that can potentially be traveled under the right conditions, for each car model.
  4. Training Data: Google map APIs and the data, which is presumably in a Big Table kind of place.




AI, Product, Strategy, Digital Transformation

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Praful Krishna

Praful Krishna

AI, Product, Strategy, Digital Transformation

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