Dutch AI startup DAISYS might have a headstart with its breakthrough in voice technology. These interactions have forced big tech companies to make their voice assistants sound less robotic, less monotone and more human. While these digital assistants can do a number of things, people often tend to converse with them by asking personal or emotional questions. “Tell me a joke” is one of the first things people say to their voice assistant. These digital assistants sound like a machine, often reminding users about HAL 9000 from 2001: A Space Odyssey. While Alexa and Assistant have entered our homes, they suffer from a common pitfall. Amazon, Google, Apple and even Facebook are putting more emphasis on their AI platforms and building digital assistants such as Alexa, Google Assistant and Siri. You will not get far. Only AI-based chatbots, overcome the enterprise search automation in a more agile way for end users and accelerate the data-to-decision timeline.Įven if our 2020 chatbot is still not like ”HAL” today, it’s up to us to provide it with all the myriad of AI capabilities we have in hand.The future of personal devices is widely considered to be voice-assisted. Setting up a bot without Machine Learning? You are stuck to a limited script and a set of finite rules. Once your intent has been determined, you will be oriented through a specific conversation: that describes the full pipeline of logic. Rules are automatically generated behind the scenes based on training data and thanks to Machine Learning. Then it analyzes the grammatical structure of a sentence and identifies semantic relationships between words, “latest” here is related to “trend”. Initially, the dependency parser detects part-of-speech, “trend” is a noun. Here, the Natural Language Understanding – NLU - comes into play to extract meaning from natural language. They could assist business experts, operational or executives in answering critical business questions -the latest sales trends, insights about a process line performance- just by using the natural language: voice or text. The chatbots remove the hassle to query manually a lot of operational systems, databases or reports. Real potential appeared when we merge AI and BI. According to Gartner, 50% of analytical queries, will be generated by search, NLP* or voice, or will be automatically generated. Now the number of conversational agents is soaring in call centers, retail, manufacturing, healthcare, insurance … The Stakes We reached the tipping point with this huge momentum of Machine Learning, Deep Learning. To make a difference, you must leverage AI capabilities which paved the way of modern and mature chatbots. User question: What’s the latest trend related to the spread of COVID-19?.Bot: Fever, cough, sore throat and headaches.User: Which are the first symptoms of the coronavirus disease?.Here is an example managed by keyword-based rules: Eliza, a simple rule-based chatbot, had - let's say - room for improvement. From the 1960s, Eliza, one of the first chatbots, simulated a psychotherapist by rephrasing and asking most of the “patient's” statements. Gartner predicted that, by 2020, “you’re more likely to have a conversation with a chatbot than with your spouse!” True or not, you have the answer … Are Chatbots truly a game-changing technology in 2020? Does reality live up to this predicted future? No, but we are getting a lot closer each time.
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