Conversational Artificial Intelligence — The Final Phase Of The Information Revolution

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The development of artificial intelligence (AI)-powered assistants for business conversations has shown that they are highly effective for supporting sales, service and other business functions across multiple industries. For example, virtual sales assistants have humanistic conversations with customers and prospects, and they enable companies to handle more leads than ever before while delivering polite and persistent lead follow-up that translates to increased sales – and these virtual assistants never get tired or sick or take a day off. Companies love these technologies because the assistant’s efforts free up salespeople to do more valuable activities, and customers love them because they have a great experience and get attention when they need it.

It is important to remember that today’s era of intuitive conversational AI was built on past innovations in AI, and I like to think that today’s AI is the final phase of the "information revolution" that we’re currently experiencing. It started with email and technologies like Usenet discussions, as well as simple search engines like Lycos and directories like Yahoo, and it has morphed into assistants like Google, Alexa, Cortana and Siri – and the pace of innovation is not slowing.

The Evolution Of The Information Revolution

Here are the developments that led to where we are today with conversational AI:

Directories: The information revolution started with Usenet forums in the 1980s. They organized all the content (referred to as news) into categories or hierarchies. The concept of directories, where a human organizes articles, news or webpages, started with Usenet and was popularized by Yahoo co-founders Jerry Yang and David Filo. Yang and Filo initially created a bunch of web pages as a hobby, collecting lists of their favorite websites while they were students at Stanford. It then occurred to them that they could organize them into a taxonomy of topics, so they started Jerry and David’s Guide to the World Wide Web. This then became Yahoo! Directories.

Search engines: Simultaneous to Yahoo! Directories, search was becoming prominent. The dominance of search engines over directories can be attributed to the exponential increase in webpages and the ability to process the content into tokens and indexes, store them in a distributed way and search them in almost no time. The number of webpages grew from under 3,000 in 1994 to over 1 billion in 2014. Information retrieval started out as a simple form of natural language processing (NLP) – only taking into account what words or tokens were present in the query and documents and matching them based on it – but evolved to more advanced information extraction-based search.

Conversational AI: Then comes conversational AI. The concept of artificial intelligence assistants that can converse with humans in a humanlike way is central to the famous Turing test proposed in 1950. One of the earliest conversational AI assistants, ELIZA, was created at the MIT artificial intelligence laboratory in the ’60s. This captured the popular imagination, but the actual assistant did not have access to the semantic representation of the natural language messages the user was sending it. Over the last few years, we have seen an increased adoption of conversational AI for businesses and consumers. In many ways, we are now in the final phase of the information revolution.

The result of this innovation is today’s era of conversational AI and what I call a true information revolution.

How Are We Driving The Information Revolution?

Companies today have developed an architecture that enables computers, acting as AI agents, to have humanistic conversations with customers. At the core of this technology is an AI natural language understanding engine, built on computational linguistics, machine learning and deep learning. Human training and data science are also in the mix, as well as a database of millions of business conversations that are constantly being referenced. These core technologies enable translation of conversational inputs into dynamic business conversations that engage humans in a polite, personal and timely fashion, helping both the business and the customer achieve their desired goals.

NLP is a big part of what drives the "naturalness" of human conversations with AI assistants. Four kinds of NLP allow reading, classification and understanding of responses, and the combination of rule-based approaches, traditional machine learning and deep learning from millions of business conversations coalesce into "ensemble" NLP. This ensemble offers a robust and flexible set of tools to handle a wide variety of inquiries and respond to them effectively. NLP helps to transform raw big data – such as information found in customer relationship management (CRM) systems – into knowledge to understand natural language inputs and to generate outputs.

In addition to NLP, there are other key "intelligences" required for true conversational AI, starting with an AI inference engine that turns responses into customer-requested actions, natural language generation (NLG) that is designed to create subject lines and email/text content, and human intelligence that is designed to seamlessly handle exceptions and train the machine for such exceptions.

Real Results And A Brighter Future

The proof of the value of conversational AI is evident, as AI-powered assistants applied to sales leads set 20-30% more appointments than human teams working alone, based on my company Conversica’s findings. This sort of AI-powered conversation automation provides a cost-effective way to thoroughly engage sales leads because it can understand intent, sentiment and urgency. It can also extract important data, such as telephone numbers and email addresses. In addition, the AI can sense the language of the person’s response and automatically switch the remaining dialogue to that language.

For businesses across many sectors of the global economy, conversational AI is a key to increased productivity and greater customer satisfaction. AI has come a long way from its rudimentary early days, and there is no doubt that future innovations will drive greater acceptance of conversational AI, pushing it into new markets and applications that can leverage the work of AI engineers past and present. That’s a real information revolution.

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The development of artificial intelligence (AI)-powered assistants for business conversations has shown that they are highly effective for supporting sales, service and other business functions across multiple industries. For example, virtual sales assistants have humanistic conversations with customers and prospects, and they enable companies to handle more leads than ever before while delivering polite and persistent lead follow-up that translates to increased sales – and these virtual assistants never get tired or sick or take a day off. Companies love these technologies because the assistant’s efforts free up salespeople to do more valuable activities, and customers love them because they have a great experience and get attention when they need it.

It is important to remember that today’s era of intuitive conversational AI was built on past innovations in AI, and I like to think that today’s AI is the final phase of the “information revolution” that we’re currently experiencing. It started with email and technologies like Usenet discussions, as well as simple search engines like Lycos and directories like Yahoo, and it has morphed into assistants like Google, Alexa, Cortana and Siri – and the pace of innovation is not slowing.

The Evolution Of The Information Revolution

Here are the developments that led to where we are today with conversational AI:

Directories: The information revolution started with Usenet forums in the 1980s. They organized all the content (referred to as news) into categories or hierarchies. The concept of directories, where a human organizes articles, news or webpages, started with Usenet and was popularized by Yahoo co-founders Jerry Yang and David Filo. Yang and Filo initially created a bunch of web pages as a hobby, collecting lists of their favorite websites while they were students at Stanford. It then occurred to them that they could organize them into a taxonomy of topics, so they started Jerry and David’s Guide to the World Wide Web. This then became Yahoo! Directories.

Search engines: Simultaneous to Yahoo! Directories, search was becoming prominent. The dominance of search engines over directories can be attributed to the exponential increase in webpages and the ability to process the content into tokens and indexes, store them in a distributed way and search them in almost no time. The number of webpages grew from under 3,000 in 1994 to over 1 billion in 2014. Information retrieval started out as a simple form of natural language processing (NLP) – only taking into account what words or tokens were present in the query and documents and matching them based on it – but evolved to more advanced information extraction-based search.

Conversational AI: Then comes conversational AI. The concept of artificial intelligence assistants that can converse with humans in a humanlike way is central to the famous Turing test proposed in 1950. One of the earliest conversational AI assistants, ELIZA, was created at the MIT artificial intelligence laboratory in the ’60s. This captured the popular imagination, but the actual assistant did not have access to the semantic representation of the natural language messages the user was sending it. Over the last few years, we have seen an increased adoption of conversational AI for businesses and consumers. In many ways, we are now in the final phase of the information revolution.

The result of this innovation is today’s era of conversational AI and what I call a true information revolution.

How Are We Driving The Information Revolution?

Companies today have developed an architecture that enables computers, acting as AI agents, to have humanistic conversations with customers. At the core of this technology is an AI natural language understanding engine, built on computational linguistics, machine learning and deep learning. Human training and data science are also in the mix, as well as a database of millions of business conversations that are constantly being referenced. These core technologies enable translation of conversational inputs into dynamic business conversations that engage humans in a polite, personal and timely fashion, helping both the business and the customer achieve their desired goals.

NLP is a big part of what drives the “naturalness” of human conversations with AI assistants. Four kinds of NLP allow reading, classification and understanding of responses, and the combination of rule-based approaches, traditional machine learning and deep learning from millions of business conversations coalesce into “ensemble” NLP. This ensemble offers a robust and flexible set of tools to handle a wide variety of inquiries and respond to them effectively. NLP helps to transform raw big data – such as information found in customer relationship management (CRM) systems – into knowledge to understand natural language inputs and to generate outputs.

In addition to NLP, there are other key “intelligences” required for true conversational AI, starting with an AI inference engine that turns responses into customer-requested actions, natural language generation (NLG) that is designed to create subject lines and email/text content, and human intelligence that is designed to seamlessly handle exceptions and train the machine for such exceptions.

Real Results And A Brighter Future

The proof of the value of conversational AI is evident, as AI-powered assistants applied to sales leads set 20-30% more appointments than human teams working alone, based on my company Conversica’s findings. This sort of AI-powered conversation automation provides a cost-effective way to thoroughly engage sales leads because it can understand intent, sentiment and urgency. It can also extract important data, such as telephone numbers and email addresses. In addition, the AI can sense the language of the person’s response and automatically switch the remaining dialogue to that language.

For businesses across many sectors of the global economy, conversational AI is a key to increased productivity and greater customer satisfaction. AI has come a long way from its rudimentary early days, and there is no doubt that future innovations will drive greater acceptance of conversational AI, pushing it into new markets and applications that can leverage the work of AI engineers past and present. That’s a real information revolution.

(Excerpt) Read more Here | 2018-08-17 22:38:58