The document discusses digital disruption and the future of various industries and skills. It notes that artificial intelligence will be on par with human intelligence by 2030 and that people will need to continuously upgrade their skills through lifelong learning. Specifically, the top 10 skills needed by 2020 are identified as complex problem solving, critical thinking, creativity, people management, coordinating with others, emotional intelligence, judgment and decision making, service orientation, negotiation, and cognitive flexibility.
The document discusses key metrics for evaluating chatbots, including user metrics, conversation metrics, and bot metrics. User metrics include total users, active users, engaged users, and new users. Conversation metrics cover starter messages, bot messages, incoming messages, missed messages, total conversations, and new conversations. Bot metrics focus on retention rate, goal completion rate, goal completion time, fallback rate, user satisfaction, and virality. These metrics help measure a chatbot's performance.
The document discusses digital disruption and the future of various industries and skills. It notes that artificial intelligence will be on par with human intelligence by 2030 and that people will need to continuously upgrade their skills through lifelong learning. Specifically, the top 10 skills needed by 2020 are identified as complex problem solving, critical thinking, creativity, people management, coordinating with others, emotional intelligence, judgment and decision making, service orientation, negotiation, and cognitive flexibility.
The document discusses key metrics for evaluating chatbots, including user metrics, conversation metrics, and bot metrics. User metrics include total users, active users, engaged users, and new users. Conversation metrics cover starter messages, bot messages, incoming messages, missed messages, total conversations, and new conversations. Bot metrics focus on retention rate, goal completion rate, goal completion time, fallback rate, user satisfaction, and virality. These metrics help measure a chatbot's performance.
The document discusses virtual assistants, defining them as software agents that can perform tasks or services for individuals. It outlines the key components of virtual assistant architecture, including automatic speech recognition (ASR), natural language understanding (NLU), and text-to-speech (TTS) synthesis. Trends in the virtual assistant market include growing demand in both consumer and enterprise domains as well as integration with smart home devices. The document concludes that virtual assistants consist of three main technologies: ASR for listening, NLU for understanding language, and TTS for responding.
This document discusses trends in chatbot adoption and customer expectations of chatbots. It notes that by 2024, the chatbot market for banking is expected to reach $2.1 billion and the healthcare chatbot market $314 million by 2023. Gartner predicts that by 2020, 10% of customer service requests will be handled by chatbots. The document outlines how chatbots can benefit businesses through automation, accuracy, lower costs, and increased productivity. It also discusses factors for chatbot success, including understanding customer needs and how the chatbot enhances the business. The conclusion emphasizes that customers care more about the benefits a chatbot provides rather than its intelligence.
The document discusses knowledge discovery for chatbots, including question answering systems that use natural language processing, search algorithms, and question answering to understand user questions. It also discusses using a knowledge base and ontology to represent information as a graph of relationships between concepts, which allows a chatbot to understand the meaning of questions and provide relevant responses. Key components include defining a domain and range for concepts, representing knowledge as subject-object-predicate triples, and using standards like the Resource Description Framework and Web Ontology Language to structure the ontology.