General Motors is taking Google's AI chatbot on the road. The automaker announced today that it's using Google Cloud's Dialogflow to automate some non-emergency OnStar features like navigation and call routing. Crucially, the automaker claims the bot can pinpoint keywords indicating an emergency situation and "quickly route the call" to trained humans when needed. GM says the system frees up OnStar Advisors to spend more time with customers requiring a live human. According to GM, the OnStar Interactive Virtual Assistant (IVA) has used Google Cloud's Dialogflow under the hood since IVA's 2022 launch.

A chatbot is a software developed to help reply to text or voice conversations automatically and quickly in real time. In the agriculture sector, the existing smart agriculture systems just use data from sensing and internet of things (IoT) technologies that exclude crop cultivation knowledge to support decision-making by farmers. To enhance this, the chatbot application can be an assistant to farmers to provide crop cultivation knowledge. Consequently, we propose the LINE chatbot application as an information and knowledge representation providing crop cultivation recommendations to farmers. It works with smart agriculture and recommendation systems. Our proposed LINE chatbot application consists of five main functions (start/stop menu, main page, drip irri gation page, mist irrigation page, and monitor page). Farmers will receive information for data monitoring to support their decision-making. Moreover, they can control the irrigation system via the LINE chatbot. Furthermore, farmers can ask questions relevant to the crop environment via a chat box. After implementing our proposed chatbot, farmers are very satisfied with the application, scoring a 96% satisfaction score. However, in terms of asking questions via chat box, this LINE chatbot application is a rule-based bot or script bot. Farmers have to type in the correct keywords as prescribed, otherwise they won't get a response from the chatbots. In the future, we will enhance the asking function of our LINE chatbot to be an intelligent bot.

Two of our most enduring commitments to partners include our mission to provide you with the support, tools, and resources you need to grow and drive customer delivery excellence, and to ensure Google Cloud partners stand apart as deeply skilled technology pace setters. This includes working with partners to stay ahead of important new trends that have the potential to disrupt our shared customers--and that also have the potential to accelerate your business growth. To help do this, we've rolled out three new Specializations that are aligned to three very important new trends. I am also very proud to announce that we have several partners who have already earned these Specializations. I'd like to briefly talk about why each area is important, who the launch partners are, and provide you with information to learn more about each one. Google worked with IDC on multiple studies involving global organizations across industries.

If software is eating the world, AI isn't far behind. AI-powered text-, art- and audio-generating systems will soon make -- and already are making -- their way into the tools people use every day, from programming environments and spellcheck plugins to concept art creation platforms. The video game industry is no exception to this, and that hardly comes as a surprise. As illustrated by games like AI Dungeon, AI -- while imperfect -- can inject surprising creativity and novelty into branching narrative storytelling. Inworld AI was founded on this premise.

Automating a factory where robots are involved is neither trivial nor cheap. Engineering the factory automation process in such a way that return of interest is maximized and risk for workers and equipment is minimized, is hence of paramount importance. Simulation can be a game changer in this scenario but requires advanced programming skills that domain experts and industrial designers might not have. In this paper we present the preliminary design and implementation of a general-purpose framework for creating and exploiting Virtual Environments via Natural language Agents (VEsNA). VEsNA takes advantage of agent-based technologies and natural language processing to enhance the design of virtual environments. The natural language input provided to VEsNA is understood by a chatbot and passed to a cognitive intelligent agent that implements the logic behind displacing objects in the virtual environment. In the VEsNA vision, the intelligent agent will be able to reason on this displacement and on its compliance to legal and normative constraints. It will also be able to implement what-if analysis and case-based reasoning. Objects populating the virtual environment will include active objects and will populate a dynamic simulation whose outcomes will be interpreted by the cognitive agent; explanations and suggestions will be passed back to the user by the chatbot.

Conversation as an interface is the best way for machines to interact with us using the universally accepted human tool that is language. Chatbots and voice user interfaces are two flavors of conversational UIs. Chatbots are real-time, data-driven answer engines that talk in natural language and are context-aware. Voice user interfaces are driven by voice and can understand and respond to users using speech. This book covers both types of conversational UIs by leveraging APIs from multiple platforms.

Google's business platform services just became a lot more conversational. Google Cloud announced yesterday a new AI-powered service product, Bot-in-a-Box, which is a Google Cloud Platform (GCP) Business Messages feature designed to assist enterprises in initiating conversations with customers. GCP Business Messages is a conversational messaging service designed to enable organizations to connect with people to answer questions that come through Google Search, YouTube, Gmail, Google Maps, or their own business channels. Bot-in-a-Box uses natural-language understanding and Google's Dialogflow software to create chatbots that can understand and respond to customer questions without developers needing to write code. Using machine learning to understand a customer's request, Bot-in-a-Box features Custom Intents, which finds the information a customer needs without human intervention.

The Dataset we are going to use is the Loan prediction dataset. The loan prediction dataset is a unique dataset that contains 12 columns. The data was gathered to predict if a customer is eligible for a loan. The Dataset is publicly available on Kaggle and can be accessed using this link. Let's Start with the bottom-up approach and build a simple Machine learning model.

Before diving into the ML behind customer-facing chatbots, I'll first give a quick overview of the system (tl;dr). I will also speak from my experience with Dialogflow, and make the assumption that the approach is similar across other ML-based chatbots. Basically, everything is built around "Intents" and "Entities". Intents: "When an end-user writes or says something, referred to as an end-user expression, Dialogflow matches the end-user expression to the best intent in your agent. Matching an intent is also known as intent classification."

Dialogflow is a powerful tool that allows us to create conversational tools without the complications of needing to handle natural language processing. But before we dive into the platform, it's important to understand all of the different concepts that tie together to create the conversational agents that we can create. When I started exploring the platform I jumped in without knowing what was what -- so in this article I want to quickly run through each of the concepts to help provide some foundational understanding for the platform. Just as you would say Hello to your friend before conversing with them, invoking an agent on the actions platform is carried out in the same way -- this kicks off the experience with our Agent in a conversational manner. At this point, this is the user requesting to speak to our agent -- this invocation is detected using the recognisable terms that we define in the Dialogflow console.