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Story graph of 5000 TED Talks

Do you wonder how the TED talks narratives flow? Do you wonder if there are any similarities in terms of the ebbs and flows of emotions in each of these narratives.

Building upon my earlier project of open sourcing 5000+ TED Talk narratives, I did something fun. For each of these talks, I built a story graph. Essentially, it is a graph of how the emotions (sentiments, magnitude of the sentiments) vary over the narrative of the entire talk. The results, if I may so, are magical. 

Now you understand the reason for my short hiatus from the blog. I was building this.

Head out to and play with it. Let me know what you like and don't. One thing that I am still working on is similarity search based on the story telling pattern. The intent is to cluster similar speakers around audience engagement, story telling style, narratives and words used, etc. 

Here are the story graphs from some of my personal favorites.

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The art of stillness

Pico Iyer

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Bring on the learning

Sir Ken Robinson

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The price of shame

Monica Lewinskey

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The power of vulnerability

Brene Brown

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Your elusive creative genius

Elizabeth Gilbert

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The danger of a single story

Chimamanda Ngozi Adichie

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Haunting photos of polar ice

Camille Seaman

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The augmented reality of techno magic

Marco Tempest

Hope you like it. If you do, be an angel and spread the word. 


Some design considerations for AI

  1. We need to first develop models where we can co-exist with AI. And, the AIs can co-exist with us. I am not talking about a robot physically sitting next to us. But, we need to be aware of their presence in the form of algorithms, tools or technology. And, they should be aware of our presence by constantly remembering who they serve by aid of interacting via feedback.  
  2. Co-existence with AI is the first step to collaborating with AI. Today, people don't have a good intuitive feel for AI because it feels like magic. It is top down and driven into their lives. It's not a choice. It has to be become a choice to permeate mainstream among the long tail population. It can only become only a choice when people recognize (even at a high level) what the technology can do.
  3. No technology can work like magic. We need to change that perception. Machine learning, AI, NLP, Chatbots, etc, are more buzz words in the industry today in majority of places and people use it as leverage. But we rarely get into an intellectual conversation around what the technology can accomplish and how to design for the technology to be effective.
  4. Current designs and interaction models will fail in a world where AI co-exists among us.
  5. Collaboration with AI is key to developing better algorithms. Today, there is a fair bit of bias. The technology should become self-correcting. As it (AI in the form of product, technology, algorithm, etc) collaborates with more people, it should be able to heal its biases.
  6. Ethics are important. Most of AI algorithms operate on understanding patterns and behavior, which requires a lot of data. The data comes from its interaction with people. We should be open to sharing what data is being used for what. A healthy debate needs to happen among not just the researchers and scientists but the larger community in general,