The End of Dumb AI

Dinesh Vadhia
Oct 1, 2017 · 4 min read

The future of AI is realtime learning, interactive and dynamic

Star Wars — A New Hope (1977)

The world turns and changes. As does data. Change needs to be captured to reflect a new reality just like modern software systems do. With conventional AI, feature vectors are a means to producing a trained static model. With Thingy, a dynamic AI, the feature vectors are central characters on the stage(*) with new ones appearing, current ones remaining unchanged or updated or even terminated.

In this post we:

  • Add images to the Thingy system,
  • Show how a query can contain both known and unknown items,
  • Briefly discuss integrating systems-of-record and systems-of-intelligence,
  • Lay out a vision of a universal vector space for dynamic AI to flourish.

Litmus test

  1. Query with an unknown image. If the image is known to Thingy then its duplicate will show as the first result. In this case, it doesn’t.
  2. Add a copy of the unknown image to the Thingy system.
  3. Query with the unknown image again. If the image is known to Thingy then its duplicate will show as the first result. This time, it does.
  4. Repeat steps 1, 2 and 3 but add three unknown images at the same time.

The slidedeck shows these steps in action.

Knowns and unknowns

For example, a photo of the Golden Gate Bridge is added to Thingy with a unique id and becomes a known item. A person can i) query with an image of the Bay Bridge which is unknown to Thingy or ii) query with both the known Golden Gate Bridge and unknown Bay Bridge images at the same time. A query can contain both known and unknown items.

The next slidedeck walks through an example.

Integrating systems-of-intelligence with systems-of-record

“Travelling through hyperspace ain’t like dusting crops, farm boy”, Han Solo to Luke Skywalker

The bottom-up universe of dynamic vectors of things are married to top-down AI agents navigating and interacting to discover and detect things in realtime.

Science fiction writers and movie makers are typically decades ahead of the rest of us. In the Blade Runner, Minority Report or Star Wars movies, the AI systems don’t need cranking-up regularly to keep them fit-for-purpose. They won’t in our world either.

Last reel

Thingy makes everything look new.

Bottom line: The future of AI is realtime learning, interactive and dynamic.

(*) The output of deep learning is a trained model for object classification or recognition. Feature vectors of unknown objects can be extracted from the pre-trained model in a process called transfer learning. These dense vectors are employed in machine learning pipelines and in distance metric operations to find similar objects in an n-dimensional geometric space.

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Dinesh Vadhia

Written by

Maker of Thingy, a recommendation engine which learns in realtime, is dynamic and interactive. Founder, architect and engineer at