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Thursday, March 15, 2018

Optimizing the Usefulness of Chatbots

Still awaiting reasonably adept and useful chatbots that can do real conversation.  So below starts some key thoughts.  Tracking what the base of their knowledge looks like, and how they are being effectively used.   And how they need to be maintained.  Ultimately relevant common sense and common context will also be key to understand.

Using machine learning to monitor and optimize chatbots

The O’Reilly Data Show Podcast: Ofer Ronen on the current state of chatbots.   By Ben Lorica

In this episode of the Data Show, I spoke with Ofer Ronen, GM of Chatbase, a startup housed within Google’s Area 120. With tools for building chatbots becoming accessible, conversational interfaces are becoming more prevalent. As Ronen highlights in our conversation, chatbots are already enabling companies to automate many routine tasks (mainly in customer interaction). We are still in the early days of chatbots, but if current trends persist, we’ll see bots deployed more widely and take on more complex tasks and interactions. Gartner recently predicted that by 2021, companies will spend more on bots and chatbots than mobile app development.

Like any other software application, as bots get deployed in real-world applications, companies will need tools to monitor their performance. For a single, simple chatbot, one can imagine developers manually monitoring log files for errors and problems. Things get harder as you scale to more bots and as the bots get increasingly more complex. As in the case of other machine learning applications, when companies start deploying many more chatbots, automated tools for monitoring and diagnostics become essential.  .... " 

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