How Two Otter Encounters Inspired Me to Change My New AI Book

Last week, I interviewed an AI startup named AISense whose single product, Otter Voice Notes, a cloud-based platform, seems to me to have as many uses as Word does. It records conversations of almost any length between multiple people with uncanny accuracy and speed.

Start with the billion-dollar transcription industry that serves medical procedures, legal testimony and depositions, then move into all forms of education and training, and there’s the foundation for a big business opportunity. Add to that all your business meetings and Zoom video conversations. Take all the time required to turn talk into text, correct the invariable typos and homonyms, delete “um’s” and so on. Eliminate the time and expense of turning spoken words into accurate text, and I think you will see how such technology can change your life.

It turns out there are already 13 AI Speech startups: almost all have technologies in the market and such competitive markets invariably accelerate innovations while keeping prices low. AISense is the apparent leader right now and seems to be well-positioned to maintain that lead. Whether or not it does, users still win because this level of competition usually leads to rapid innovation and refinement. Otter provides users 600 free minutes a month and the platform allows users much longer recording periods, so that you can automatically record 10 hours of conference presentations if you wish.

For me, there was another revelation: Otter.ai changed the actual dynamics of my meeting with its founders. Because I trusted their tech to take notes, I could enjoy a more immersive and authentic face-to-face interaction with co-founders Sam Liang and Yun Fu and JD Lasica, my friend and fellow author, who was also present.

Otter produced an entirely more comprehensive transcript than I ever could have otherwise accomplished, and the tags produced let me find what I needed afterward with great speed. This is AI Augmentation at its best: the humans get to do humanly interactive things, while Otter tirelessly takes notes.

Liang and Fu explained how Otter’s AI can extract conversation summaries with bulleted key points and action items. It will use social graphs to see the relationships between conversation participants and can detect emotions. My talks with the Otter guys have excited me for other, more personal, reasons. They have to do with Augmenting People: Why AI Should Back Us Up, Not Push Us Out.

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