Ostfalia University of Applied Sciences Abstract: This study investigates the interaction between humanoid robots NAO and Pepper, emphasizing their potential applications in educational settings. NAO, widely used in education, and Pepper, designed for social interactions, offer new opportunities for autonomous communication and collaboration. Through a series of programmed interactions, the robots demonstrated their ability to communicate and coordinate actions autonomously, highlighting their potential as tools for enhancing learning environments. The research also explores the integration of emerging technologies, such as artificial intelligence, into these systems, allowing robots to learn from each other and adapt their behavior. The findings suggest that NAO and Pepper can significantly contribute to both technical learning and the development of social and emotional skills in students, offering innovative pedagogical approaches through the use of humanoid robotics.

Current chatbot development platforms and frameworks facilitate setting up the language and dialog part of chatbots, while connecting it to backend services and business functions requires substantial manual coding effort and programming skills. This paper proposes an approach to overcome this situation. It proposes an architecture with a chatbot as frontend using an IoT (Internet of Things) platform as a middleware for connections to backend services. Specifically, it elaborates and demonstrates how to combine a chatbot developed on the open source development platform Rasa with the open source platform Node-RED, allowing low-code or no-code development of a transactional conversational user interface from frontend to backend. This is the author's version of the accepted version of the paper. It is posted here for your personal use. The final published version of the paper is in CUI'21 3rd Conference on Conversational User Interfaces, July 27-29, 2021, Bilbao (online), Spain. It can be accessed at https://doi.org/10.1145/3469595.3469632 1 INTRODUCTION Chatbots offer a means to use services and control smart home devices connected to the web.

In this follow on from my last PowerWire article on ActiveMQ, we will be using a Raspberry Pi, with Machine Learning, to take photos of birds feeding in our garden. This part of the series will focus on the ML segment to take a photograph and let machine learning decide what is in it. After that, in my next article, we will send the information over to the IBM i for notification purposes. For ease, and time constraints, we will be using Node-RED to take a picture, then pass that image through to a Machine Learning module to interrogate the photo and decide if there are any birds in there and not the squirrels after their share of the nuts! Node-RED, which also runs on many platforms, including the IBM i, is very easy to use for this type of project.

Imagine you could build a system that can automatically reach out to users to collect feedback? This is possible today and this blog post will outline how to do it. In this post I will take you through how you build a chatbot using Rasa Open Source. The post was inspired by a series of great blog posts on this topic, but adds in a little of my own experience in doing the deployment. Rasa provides a framework that is understandable and intuitive, avoiding blackbox approaches and making it possible to get something up and running very quickly.

In this video, Yi-Hong talks about how he made Docker community images more accessible by adding support for more architectures, so you can use them on any platform. TensorFlow allows AI to be integrated using JavaScript both on the back end in Node.js and in the browser. He's also working to make it easier to develop flows with AI using Node-RED. He's building custom nodes that you can use in Node-RED. Here are some of the modules Yi-Hong is working on for Node-RED.

Machine learning dependencies are a hassle, whether ensuring that the right versions are installed on all systems or that versions of dependencies in your projects are still compatible with the version on your cloud-based system when you deploy. But with containers, you can create a clean, virtual environment to set up and train your neural networks in. To try it yourself, these exercises start with a "Hello World" app of machine learning. You build, deploy and train your neural network, and then deploy it to your local OpenShift environment. In the previous exercises in Kubernetes with OpenShift 101 and Kubernetes with OpenShift 101 Node-RED you got an introduction to Minishift, a Node.js This tutorial can help you understand how to deploy and manage a machine learning app on Minishift and Red Hat OpenShift on IBM Cloud .

Legacy IIoT devices can be modernized utilizing edge of network devices to send data to the Azure IoT hub and Machine Learning. This can create cost and efficiency improvements and reduced downtime. I will try to quickly explain the issue of liquid loading and slow legacy communications. Keep in mind there are many other issues that can be alleviated with this solution and there is no way I could mention them all. Oil & Gas Wells can "Load Up" with liquid reducing production and possibly incurring costly intermediation to relieve the issue.