The AI trap

Photo by Andrea De Santis on Unsplash
  1. It’s about getting feedback
  2. It’s about showing rapid progress
  3. It’s about actually using AI

Getting feedback

An important reason for starting with a simple back-end implementation and actually focussing on the front-end first, is because it allows us to get feedback early on. Feedback is super relevant for products, and startups in particular. It drives the build-measure-learn loop, introduced by Eric Ries in his book Lean Startup. The front-end is a natural component to collect feedback, because it’s where a customer interacts with the product.

Showing rapid progress

Another big reason for starting with the interface is the need for rapid iteration and showing progress. As Marty Cagan wrote in Inspired, iteration is important to learn how much time it will cost to build something and, more importantly, how to provide a stable solution for the customers. Some quotes from the book that I found, ahem, inspiring:

Actually using the AI

The result of our recommender system, is something that needs to be consumed; embedded in a workflow by one of our customers. As noted previously, some users feel right at home with a spartan interface with white letters on a black screen, but most often than not, people prefer to use something with a bit more polish. Furthermore, navigation of the results will be crucial, and for that good interaction is essential. This navigation works by allowing the user to very easily and intelligibly modify their search criteria, based on their own changes in understanding of a particular field. We also want to make the product a pleasure to use. Ines Montani, co-founder of Explosion, wrote in How front-end development can improve Artificial Intelligence on the importance of frontend development for AI:

What AI we are applying now

Since we’ve wrapped up our first increment of frontend work, we are now starting to work on the AI-part of the product. At this time, search and comparison is done by relatively simple methods such as pre-trained word-embeddings and cosine distances. Now, we start to move towards improving the keyword extraction first, and representation and comparison of companies second. This is a necessity for a useful MVP: if our customers can’t find any relevant companies at all, they will not use the product.

References

JP van Oosten (2021) LinkedIn Announcement of Netwerk AI https://www.linkedin.com/posts/jpvoosten_in-the-past-8-years-ive-had-a-few-different-activity-6843840226008276992-VMbl/

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JP van Oosten

JP van Oosten

Experienced Machine Learning engineer and co-founder of Netwerk AI, with a strong focus on applying AI to real-life problems & belief in humans in the loop.