Looks like everyone wants to be a chatbot entrepreneur these days. Conversational AI is one of the hottest startup market opportunities right now.
If you are preparing to throw your hat in the ring as a (B2B) chatbot entrepreneur, as a reality check, it would be a good idea to read this article (a little dated, but relevant).
Now, before you despair, while the questions from this VC are worth pondering, Roger Chen makes a fundamental assumption: The technology enabling true conversational AI exists. In reality, it doesn’t. Even for narrow vertical enterprise applications. What you see advertised as chatbots are, in reality, laboriously scripted apps with an NLP layer. Many of his questions center around the value proposition of a “better NLP” in the context of simple chatbots where NLP is the key technology.
But while machine learning and AI are inherent in NLP/NLU, that is not the “conversational AI” technology that excites people. The conversational AI involves learning outside of the NLP context; that is, learning what it takes to converse with humans. Understanding many relevant contexts: linguistic context pertaining to the semantics and the syntax of the conversation (for examples, entities and their values), personality context pertaining to the persona of the human with whom the bot is engaging in the dialog, and organizational context pertaining to the particular B2B enterprise domain/applications that this bot is connecting to.
As a specific example of this distinction of conversational AI learning outside of NLP, consider the vocabulary used by the bot. Normally, one would consider vocabulary to be part of NLP. But while character/word/sentence representations (such as word embeddings) may be NLP technologies, the vocabulary itself is domain-specific. So constructing a suitable vocabulary for the bot may itself be a distinguishing aspect of the proposed innovation. While vocabulary is seldom considered crucial, in practical applications, it plays a big role. A large vocabulary is often a hindrance (at the least, a resource hog) while a small vocabulary leads to the problem of handling out-of-vocabulary (OOV) words/tokens. This is just one example of many details that go into creating a truly conversational AI bot, that are orthogonal to the NLP technology itself.
Current chatbots or “virtual assistants” essentially serve as new NLP-enabled messaging interfaces to existing enterprise systems with the real value coming from data integration from diverse sources. In other words, many of these “chatbots” are (semantic) search engines with NLP capability; they find answers to simple NLP queries that involve extracting data from multiple enterprise silos. This serves a useful automation function with a newer common interface: messaging front-ends. In many of these applications, the aim is, in fact, to limit or avoid conversations, and to return the information quickly. This is just a convenient productivity application for enterprise users, but they are not intended to be true conversational AI interfaces.
Most of these “chatbots” will break easily when the user goes off script which will happen in a real conversation involving complex workflows (not simple IVR type work-flows). For enterprise applications, for example, customer-service call-center type applications especially those that involve complex troubleshooting, these chatbots often require hand-coded rules that capture domain knowledge (like the old-day expert systems). This approach will not generalize or scale.
So, the biggest opportunity right now is for startup tech companies that can create the (deep learning) technology to engineer a realistic conversation, even in a narrow domain. For a discussion of some of the technical challenges of a purely data-driven deep learning approach, see this article.