Who has not heard of Siri?! With Apple's iPhone 4S launch, it enjoyed immense popularity, only to be mocked a few months later as yet another futuristic AI promise that never quite lived up to its hype. Yet, it's too easy to dismiss Siri as flop before we appreciate the complexity of the problem. Natural Language Understanding tackles a daunting task: teaching computers to understand human speech. Not merely transcribing speech into words – that can be done already - but actually extracting the meaning. A sound solution to this problem is the holy grail of Artificial Intelligence, and Siri was an important milestone towards that goal, spawning a new generation of voice assistants. Yet, that “Siri generation” can only support a narrow set of commands and actions, and now both consumers and businesses are yearning for more. It’s time for the next disruptive cycle. Enter Robin.
Use case: task management on the go
- A mobile workforce/task management system called FleetOp wants to add a voice UI for their users to be able to manage their tasks on the go. Possible queries include: “What’s next on my list?.. Give me directions there!”, “Reassign this task to X”, etc.
- With Robin.AI, FleetOp can achieve this task without being an NLP expert or even a programmer!
Since Robin.AI already has a Task Management Agent, the Task concept is already defined, along with typical task attributes such as task owner, priority, deadline, etc. Since FleetOp tasks also possess a geographical context, they require an extra Location attribute, which automatically enables support for route direction and other location-based queries. Additionally, to support references to teams and team members, FleetOp needs to import their respective names into the system and voila: the FleetOp voice assistant is born!
Use case: cooking assistant
Alice wants to create a cooking assistant called Le Chef to teach him recipes and guide her in the kitchen with speech-interactive culinary directions. Le Chef would have to support questions like, “How do I make a Tiramisu?”, “What should the oven temperature be”, etc. Again, Robin.AI comes in handy. Handling the Tiramisu query involves two tasks: searching (Vertical Search Agent) and then presenting the recipe to the user as a navigable list of instructions (List Navigation Agent). In fact, Alice doesn’t have to be aware of these technicalities: Robin already knows that any search command should trigger a Search Agent and that Recipe ISA list-of (Instructions), engaging a List Navigation as a result.