Passing the mantle of 'Flagship AI use case'

The mantle of being the ‘flagship AI use case’ seems to have passed to the two month old ChatGPT. This title was held by the autonomous car for a long time.





ChatGPT is a text generator. It takes one sequence of text (what you type) and generates another sequence of text (its response). Because of its training, this appears to be a conversation; as if you have asked a question and ChatGPT has replied.

Within five days of its launch, ChatGPT acquired one million subscribers, something that took Facebook ten months to do. ChatGPT is being tried by everyone. Industry leaders, eminent professors, writers and celebrities are all asking questions to ChatGPT and coming out impressed. They are then writing about it, thus fueling a storm of popularity.

Meanwhile the autonomous car, the outgoing flagship of AI, is losing its charm. For around 10 years, self-driving vehicles occupied a slide in every presentation on AI. They used to be one of the use cases cited in most business articles on AI or ML. But this is changing fast. P ai eople are disillusioned with the results. In 2012, Sergie Brin forecasted that self-driving cars will be commonly available in five years. It’s 2023 now, and most of us haven’t even seen one.

ChatGPT is the ‘miracle’ technology that comes once in a while and leaves the world awestruck. The last such technology was the iPhone. Autonomous car could have been that, but it hasn’t been (yet) adopted for common use. What is the reason that ChatGPT is adopted so easily and autonomous cars are not?

The reason has to do with the Cost of Errors. Both ChatGPT and self-driving vehicles make mistakes. In fact, the current self-driving vehicles make far fewer mistakes than ChatGPT. But ChatGPT always has a human-in-loop. The human who asks questions decides whether to use the answer or not. I came across an experiment in which ChatGPT explained in detail how eating glass can be good for health. But the human (hopefully) will make sure that this is not implemented. The autonomous car does not have this protection. The cost of even a small error is huge.

This is good learning for those of us applying AI in business. An AI use case has a much better chance of adoption if it has a low cost or error and when there is a human-in-loop.

By Devesh Rajadhyax
Co-Founder, Cere Labs

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