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It is common knowledge that up to 87% of data science projects fail to go from Proof of Concept to production; NLP projects for the Insurance domain make no exception. On the contrary, they must overcome several hardships inevitably connected to this space and its intricacies.
The most known difficulties come from:
Google Actions can be developed using Firebase Cloud functions or a REST API endpoint. Firebase Cloud Function function is Googles's implementation of serverless functions available in Firebase. Google recommends using Firebase Cloud Function functions for Google Action development.
This is a very lightweight and powerful approach to developing our Google Action. However, it is complex to work locally with serverless functions like Firebase Cloud Functions.
Google Actions can be developed using Firebase Cloud functions or a REST API endpoint. Firebase Cloud Function function is Googles's implementation of serverless functions available in Firebase. Google recommends using Firebase Cloud Function functions for Google Action development.
In this post, we will implement a Google Action for Google Assistant by using Node.js, yarn, and Firebase Cloud Functions. This Google Action is basically a Hello World example.
Any solution aimed at processing unstructured data (i.e., language, specifically text in most cases) is today based on one of two main approaches: Machine Learning and Symbolic. Both can be delivered in multiple ways (different algorithms in the case of ML, from shallow linguistics to semantic technology in the case of Symbolic), but not much has been done so far in the realm of hybrid approaches. While choosing one over the other is always going to present a compromise between advantages and drawbacks (higher accuracy coming from Symbolic, more flexibility derived from ML), Hybrid AI — or Hybrid NL — is a revolutionary path to solve linguistic challenges that can leverage the best of both worlds and, ultimately, make your NLP practices graduate to NLU (Natural Language Understanding). I won’t spend time explaining how ML or Symbolic work since there’s a ton of literature about that already, I’ll focus this page on Hybrid instead.
To frame this conversation in a practical fashion, we must look at two aspects: development, and workflow. At the development stage, going hybrid means that a Symbolic solution will support the creation of a Machine Learning model in order to either reduce the effort or enhance its quality. On the other hand, at the production stage, our workflow can be supported by both ML and Symbolic to deliver a more precise outcome. In a project that considers the Machine Learning piece the pivot of the solution, the first type of integration places Symbolic at the top (before even creating a Machine Learning model), and the second one at the bottom (curating or enhancing the final output). Naturally, both of these hybrid ways can be present at the same time in a linguistic project.
Natural Language Processing (NLP) or Natural Language Understanding (NLU) is a subset of Artificial Intelligence (AI). There are many benefits when using the technology, and I am surprised at the pushback from technical people when talking about deploying it. I guess there is a difference between learning about technology in academia and the complexity of actually deploying it.
So, how do we get past all the pushback when chatbots are having conversations and intelligent automation promises to be better than old-school EAI and SOA?
Businesses are quickly acknowledging the importance of Conversational AI (CAI) to increase their customer engagement and revenues. The question is no longer whether to deploy CAI, but rather which platform to use and how to leverage its capabilities.
In this series, see some insight on important aspects of a conversational AI platform that buyers often overlook. For example, what does language support really mean? What is localization? How do different deployment models impact the TCO? And maybe most importantly – How can the CAI platform not only help me during the first development sprints – but across the entire bot lifecycle?
Online shopping is on the rise as it’s convenient to shop from home and get products delivered. According to NPR, 92 percent of American consumers make purchases through ecommerce, and that number is only increasing.
When it comes to online shopping, consumers have high expectations about how their experience should be. They’re looking for quick, easy solutions to their problems from brands who care about their buying motives.
In this article, I’ll show how to build a natural language interface for a typical home light switch so that you could turn the lights on and off with simple commands like Turn off all the lights, please, or Get the lights on in the kids bedroom.
We’ll concentrate on Natural Language Interface (NLI) part, and I’ll leave speech-to-text and the actual light controlling outside of the scope of this short blog. You can easily add speech-to-text with WebSpeech, if necessary, while Arduino/HomeKit can provide simple API to control the lights in your house.
Humans are good at conversations. We understand what someone means when they say something and can understand when a word like “bank” is used in the context of a financial institute or a river bank. We use the power of logical, linguistic, emotional reasoning and understanding in order to respond during conversations.
In order to get machines to truly understand natural language like we do, our first task is to teach them the meaning of words — a task that is easier said than done. The past couple of years has seen significant progress in this field.