My interest in Virtual Assistants stems from the fact that I spent 12 years at NoHold, a small company in Silicon Valley providing a virtual assistant platform for businesses to build custom self-service virtual assistant applications.
Virtual assistants have been used mostly and most successfully for self-service customer support use cases to help consumers help themselves, and in the process reduce support costs, improve customer satisfaction. Most of these services have a very focused approach, i.e., targeted at specific domains of knowledge and with the same overall objective of knowledge management systems and search engines: help users find relevant answers to their issues or questions easily and quickly. Most of these systems use some form of AI to let users express their questions in natural language and guide users find relevant answers, specifically, the areas of AI known as Expert Systems and Natural Language Processing. The approach and sophistication of the NLP vary substantially but most typically based on statistical analysis. These assistants are generally referred to as rule-based systems, because of the steps of having to manually create and maintain the rules on how to browse/navigate the corpus of domain knowledge. These systems have been around for many years, and they will still be around for many more as they bring real value, as in better productivity to both consumers and businesses. A new wave of virtual assistants has emerged recently with the advent of smartphones, the personal digital assistants, e.g., Siri and Alexa. They are trying to solve the same general problem of virtual assistants, but differently. One thing in particular that they do differently is that they “do” things for you, as in booking an appointment in your calendar, setting an alert, connecting to your smart home devices, e.g., turning light on or off, or make a reservation to a restaurant, etc., you get the idea. Users interact, with the personal digital assistants primarily through voice, through the smartphone for Siri or smart speaker for Alexa. So, for the digital personal assistant to set an alert, for example, they need to have a connection built with your reminder app, or with your travel booking app. The virtual assistant application provides an API and integration toolkit for these apps to integrate with. For Apple, so far, these APIs have been mostly for internal use, most likely to change, while Amazon is pushing these API to external developers. Another difference with personal digital assistants is that they also attempt to give answers to any question you may have, but they do this poorly because that's a tall order and, while performing the actions is rather 'simple', once you match the question to the predefined intents, answering questions of any kind and be smart is difficult. Apple’s Siri leverages a third party vast knowledge base to do this, Wolfram Alpha from Wolfram Research, while Alexa supports only specific categories of trivia questions today. Where they fall short is in understanding the natural language questions, and therefore the answers are not helpful. To be said, that the speech recognition aspect works remarkably well, at least for Siri, it’s the handoff and processing and understanding of the actual question text that is weak. NLP is an area that is improving by leaps and bounds thanks to a new breed of algorithms based on Machine Learning and Deep Learning. Both Alexa and Siri NLP appears to be based on these ML models, and I am sure they will improve over time. ML-based NLP attempts to solve one of the inefficiencies of rule-based systems as they don't require the continuous manual effort by Knowledge Specialists to improve the system. With an ML-based NLP, once you have the model built and deployed, it's "supposed" to be easier and faster to maintain. However, getting the right ML-model built is not easy either, and it requires lots of data, in most cases curated data that needs to be prepared and 'massaged' to be useful. This work is done not by Knowledge Specialists, but rather by Data Scientists. If an ML-model does not work well, it takes much longer to pinpoint where the problem is and requires highly skilled Data Scientists to do that. For a rule-based system, typically, it's much easier to find out why the system gave the wrong answer and take corrective actions rather quickly. So, keep this in mind before you invest in Virtual Assistants; this is a critical aspect that you need to consider. It’s a fascinating world, and I will post here my thoughts from time to time on new trends, improvements in all aspects of virtual assistants, personal digital assistants, search, or cognitive search. Comments are closed.
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