![]() ![]() Today, there are better, “intentless” ways to design chatbots. What are chatbots capable of, and why aren’t we there yet? Recent advances in several areas of AI, however, offer opportunities for producing something better. Instead, they are glorified Q&A bots that classify queries and issue a canned response. This isn’t what today’s chatbots deliver. Ideally, a chatbot should be able to ingest a user’s query, understand what the user is trying to achieve, and then help the user achieve their objective-either by taking action or by generating a helpful, human-sounding response. Now suppose the program encounters a bear skin on a couch-that’s multiple matches, so now you have a conflict. Now you have to relabel everything to be more specific. Suppose at some point you need to differentiate bears from dogs as well as couches from chairs. You feed it a pile of labeled images-this is an animal, this is furniture-and it learns to recognize them. ![]() Imagine an image classification program that identifies animals and furniture. This function is error-prone and a lot of work to build, but more importantly, it’s fundamentally wrong. You’re not having a conversation you’re interacting with a conversation machine, playing a text adventure like Zork. In my view, this hardly qualifies as AI-it’s closer to a search function. The chatbot might know the answer to the question, “Can I change my billing address so it matches other profiles on my account?”, but have the information under account settings, not address change. Intent-based classification just finds the intent that a conversation resembles and pushes the canned response for that bucket. Conversations with real people can be messy and full of nuance, and people often want multiple things at once. To classify a customer conversation, the conversation designer must anticipate the correct intent and add all possible conversation triggers for those intents.Įven if those intents have been well anticipated with good trigger phrases, the chatbot can only deliver on a single intent. While this can handle some common requests, it’s difficult to provide and maintain a satisfying customer experience. ![]() They parse conversations through intent classification-trying to organize everything a customer might say into a preconceived bucket based on the intention of their inquiry.įor example, “Hello, I would like to change my billing address” might be classified into the change billing address bucket-and the chatbot would reply accordingly. Why do chatbots suck?ĭespite all of the advances in natural language processing (NLP), most chatbots only use the most basic form of it. Let’s explore what’s happening with the technology together and see if you agree. So how did we get here? And will chatbots ever live up to the hype? I wouldn’t be writing this if I didn’t believe the answer was yes, but don’t take my word for it. It certainly wasn’t my game plan when I built the first version of Botpress in 2015. I don’t think that was anybody’s game plan for chatbots. I’m the CEO of a chatbot company, and even I can’t name a single chatbot that’s great. Despite years of hype-and some incredible technological breakthroughs-many people think of chatbots as an even more frustrating replacement for offshore call centers. ![]()
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