Chat bot mature
The results were logged in wit.ai, and we reviewed them the next morning.
Whenever the bot was unsure of what to say (its confidence level drops below a certain threshold), it provided the default response and logged the uncomprehended message in the to the uncomprehended messages, a process calls validation.
To ensure our bot was nag-proof, we considered interactions between a mother and her son, boiling them down to 12 major scenarios which you can see listed on the left panel as Stories. Well, not quite…The bot we set up was still an infant and wouldn’t be able to fend off requests from all the pesky mums out there. ) test our bot, so we set up a nifty website containing a text input.
To turn it into a streetsmart, gangster teenage boy, we’d have to train it through machine learning. That night, we tested our bot with more than 30 subjects.
We had the most fun watching people test our chatbot, which surprised them with some of the funniest mumbo jumbos.
” could have the same intent as “What is the weather like in London?
” Treating both questions with the same intent, a bot would respond to the two questions with London’s weather conditions.
Next time when the bots encounter a similar message, it will give the correct response.
As you can see, machine learning is a matter of trial and error..