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Changing paradigms in ai prototyping

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Changing paradigms in ai prototyping

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Developing an AI First Draft instead of an AI MVPs, an approach to incremental usefulness. This work pushes the concept of "First draft instead of Minimum viable product" when it comes to an AI related project. This is mainly because an AI MVP may never see the light if we are looking for a "Viable" first version. There are some design principles and lessons that I have learned from industry, academia, and the startup world.

Developing an AI First Draft instead of an AI MVPs, an approach to incremental usefulness. This work pushes the concept of "First draft instead of Minimum viable product" when it comes to an AI related project. This is mainly because an AI MVP may never see the light if we are looking for a "Viable" first version. There are some design principles and lessons that I have learned from industry, academia, and the startup world.

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Changing paradigms in ai prototyping

  1. 1. Changing paradigms in AI prototyping From Minimum Viable Product to a First Draft approach Carlos Toxtli @ctoxtli
  2. 2. Minimum “Viable” Product
  3. 3. Expectations in AI Reality @ctoxtli
  4. 4. Why? ● Conventional solutions are deterministic 1 + 1 = 2 (100% sure) ● AI solutions are stochastic (randomness) There is a cat in the image (86% sure) ??? @ctoxtli
  5. 5. So how can we effectively prototype AI? Lets learn from academia @ctoxtli
  6. 6. In fact, Data scientists currently code in notebooks
  7. 7. Hypothesis and baseline definition You want to prove that your proposed algorithm is better than existing ones. Try with the simplest algorithms first. @ctoxtli
  8. 8. Common AI pipeline in Industry ● Collect data (tons of data) ● Pre process data (refine the data to its most optimal representation) ● Find best approach to implement (usually the state-of-the-art) ● Implement the algorithm (usually using a published code) @ctoxtli
  9. 9. First Draft AI pipeline ● Collect data (small data but well stratified and the most discriminative) ● Pre process data (simplest data cleaning) ● Find the simplest approach (use vanilla versions of algorithms or one-shot learning) ● Implement the algorithm (use a widely and easy to use framework i.e. sklearn, keras)
  10. 10. Quick iterations ● Fine tune it (hyper parameters) ● Try several already implemented ML algorithms and ensembled models. ● Evaluate and create a benchmark @ctoxtli
  11. 11. Define your error mitigation strategy ● Step 1: Try a mechanism to give a solution when accuracy is low (i.e. human-in-the-loop, heuristics) ● Step 2: Try both (AI model and mitigation strategy) triggered by a certain accuracy threshold. ● Step 3: Try the UX when the system is only driven by AI. @ctoxtli
  12. 12. Hey! now you have a working solution ● This version 0 would be super inefficient but the model and the mitigation process are implemented. ● Implementing the state-of-the-art algorithm will be a simple module replacement task. @ctoxtli
  13. 13. Conclusions ● An AI MVP may never see the light if we are looking for a "Viable" first version. ● Having an implemented first draft of an AI model and a mitigation strategy can boost the development of robust AI solutions.
  14. 14. Thanks http://www.carlostoxtli.com @ctoxtli

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