In a fast-moving world, customers require efficiency and promptness when talking to any company**. Here is where chatbots and Intelligent Virtual Assistants** (IVAs) come into play.

Thanks to their ability to engage into more advanced conversations, unlike rule-based chatbots, AI-powered systems are equipped with a multitude of features to assist and even entertain the users in their day-to-day activities. In addition to their customizable features, their self-learning ability and scalability have lead virtual assistants to gain popularity across various global enterprises.

According to Grand View Research, the global intelligent virtual assistant market size was valued at USD 3.7 billion in 2019, growing at a Compound Annual Growth Rate (CAGR) of 34.0% over the forecast period. The need for effectiveness across service-based companies and the integration of AI digital assistants among various devices, such as computers, tablets and smartphones, is anticipated to boost the market.

What can bots do in 2020?

There is certainly no doubt that recent advancements in technology have significantly improved the performance of chatbots and IVAs. But, however flawless they may seem at first sight, we could all agree on the fact that bots are still terrible conversationalists.

Rule-based chatbots. AI-driven chatbots.

The basic **rule-based chatbots **are only accessible within chats and work on a single-turn exchange. In a nutshell, they react to questions asked by the user, detect the main intent, and return a single pre-defined answer accordingly. They are able to handle basic routine queries, for instance: **FAQs, reservations, online orders or appointment scheduling **(survey botsmeeting plannersforeign language tutorstravel & hospitality bots). Nevertheless, as soon as the user asks a question out of the bot’s learned set of knowledge, it will automatically lead to failure.

On the other hand, we distinguish the AI-powered chatbots, that rely on core Machine Learning technologies like Natural Language Processing (NLP) and Information Retrieval (IR) techniques. By applying such methods, tech giants like Facebook and Google have released open-domain multi-turn chatbots (see Meena and Blender), that are able to reproduce more human-like conversations. However, the implementation of open-domain bots remains incredibly challenging due to many direct limitations of deep-learning.


In April 2020, Facebook AI developed and open-sourced BlenderBotthe first chatbot to blend a diverse set of conversational skills — including empathy, knowledge, and personality — together in one system.

For all the great progress it represents for conversational AI, Blender is still far from reaching the level of humans. One of the challenges lies in its tendency to make up facts — because sentences are being generated from statistical correlations, and not from a knowledge database. As a consequence, it can string together an in-depth and coherent description of a well-known superstar, for example, but with entirely false information. The team intends to experiment further with integrating a knowledge database into the chatbot’s response generation system.

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Conversational AI: Intelligent Virtual Assistants and the road ahead.
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