In a week, I couldn’t manage to make Suzannebot good for much more than a punchline.
But Apple’s had seven years to do more with Siri, and we’re all still waiting.
Humans disproportionately equate language with intelligence, even if that language is just a few automatic responses, typed out over a lunch break. And, of course, I made it clear that it took The core problem I had trained Suzannebot 2.0 to solve was simple: Real-life Suzanne can’t always respond to pitches.
Its market is anyone from hobbyists like me, to small businesses, to Fortune 500 companies–anyone who might need a chatbot, but doesn’t have the resources to build their own Siri. Dexter’s code is pulled from the open source project Rivescript, which is infinitely simpler than the command-filled code of other chatbot options.
The two-year-old startup wants to make chatbots both easier to build, and easier to actually deploy on Facebook Messenger, Slack, SMS, and Twitter. It’s one-half tutorial, a superb, step-by-step FAQ that explains in very plain terms how to build a bot in a language that’s similar to HTML. It’s a terminal window in which you code, along with a mock-up i Phone where you can quickly preview if your code worked.
I felt sudden disgust–and also sympathy–for the corny humor fed into the Valley’s brightest bots. Finally, the day had come to reveal my killer app for Suzannebot 2.0.
Just like me, a guy who learned a few lines of a basic coding language yesterday, the most powerful technology companies in the world are hiding their AI inadequacies behind jokes. By now, I’d gone so far as to disable Suzannebot’s autoresponder. I began my presentation in Slack, walking through the trials that all innovators, such as myself, faced.