Originally posted on twitter.
Criticizing your models is an important part of modeling.
In statistics this is well recognized. We check things like heteroskedasticity to avoid drawing the wrong conclusions.
What do you do in machine learning? Only check cross-validation score?
This blog was originally posted at Xebia.com, my employer at the time of writing.
Have you ever had a conversation with a chatbot? Was it a positive experience? It might have been. But more likely than not it left you a bit frustrated. Chatbots try to understand your message and help you with an appropriate response. However, most of the time they're not that great yet. In a way chatbots are like baseball players.
"Baseball is the only field of endeavor where a man can succeed three times out of ten and be considered a good performer." — Ted Williams, baseball Hall of Famer
The same holds true for chatbots. A deflection percentage of 32% [users helped by the bot without human intervention] is what google considers a success story!
As a data science consultant I've worked with multiple companies on chatbots and helped them do better. During these projects I have discovered a pattern that might help others build better chatbots too. In this article I outline three tips that should help you focus on what matters.
This blog was originally posted at Xebia.com, my employer at the time of writing.
Machines may take over the world within the year;
But creating rhymes instills in us the most fear!
Luckily, pre-trained neural networks are easy to apply.
With great pride we introduce our new assistant: Rhyme with AI.