Machine learning (ML) has become crucial in modern life, with growing interest from researchers and the public. Despite its potential, a significant entry barrier prevents widespread adoption, making it challenging for non-experts to understand and implement ML techniques. The increasing desire to leverage ML is counterbalanced by its technical complexity, creating a gap between potential and practical application. This work introduces asanAI, an offline-first, open-source, no-code machine learning toolkit designed for users of all skill levels. It allows individuals to design, debug, train, and test ML models directly in a web browser, eliminating the need for software installations and coding. The toolkit runs on any device with a modern web browser, including smartphones, and ensures user privacy through local computations while utilizing WebGL for enhanced GPU performance. Users can quickly experiment with neural networks and train custom models using various data sources, supported by intuitive visualizations of network structures and data flows. asanAI simplifies the teaching of ML concepts in educational settings and is released under an open-source MIT license, encouraging modifications. It also supports exporting models in industry-ready formats, empowering a diverse range of users to effectively learn and apply machine learning in their projects. The proposed toolkit is successfully utilized by researchers of ScaDS.AI to swiftly draft and test machine learning ideas, by trainers to effectively educate enthusiasts, and by teachers to introduce contemporary ML topics in classrooms with minimal effort and high clarity.

TMIC is an App Inventor extension for the deployment of ML models for image classification developed with Google Teachable Machine in educational settings. Google Teachable Machine, is an intuitive visual tool that provides workflow-oriented support for the development of ML models for image classification. Aiming at the usage of models developed with Google Teachable Machine, the extension TMIC enables the deployment of the trained models exported as TensorFlow.js to Google Cloud as part of App Inventor, one of the most popular block-based programming environments for teaching computing in K-12. The extension was created with the App Inventor extension framework based on the extension PIC and is available under the BSD 3 license. It can be used for teaching ML in K-12, in introductory courses in higher education or by anyone interested in creating intelligent apps with image classification. The extension TMIC is being developed by the initiative Computa\c{c}\~ao na Escola of the Department of Informatics and Statistics at the Federal University of Santa Catarina/Brazil as part of a research effort aiming at introducing AI education in K-12.

Sean Cusack, a software engineer at Microsoft and beekeeper on the side, wanted to know if anything besides bees was going into his hives. So he built a tiny photo booth (a sort of bee vestibule) that took pictures whenever something appeared around it. But sorting through thousands of insect portraits proved tedious. Colleagues told him about a new product that the company was working on called Lobe.ai, which allows anybody to train a computer-vision system to recognize objects. Cusack used it to identify his honeybees -- but also to keep an eye out for the dreaded murder hornet.

The objective of an online Mart is to match buyers and sellers, to weigh animals and to oversee their sale. A reliable pricing method can be developed by ML models that can read through historical sales data. However, when AI models suggest or recommend a price, that in itself does not reveal too much (i.e., it acts like a black box) about the qualities and the abilities of an animal. An interested buyer would like to know more about the salient features of an animal before making the right choice based on his requirements. A model capable of explaining the different factors that impact the price point is essential for the needs of the market. It can also inspire confidence in buyers and sellers about the price point offered. To achieve these objectives, we have been working with the team at MartEye, a startup based in Portershed in Galway City, Ireland. Through this paper, we report our work-in-progress research towards building a smart video analytic platform, leveraging Explainable AI techniques. Keywords: Explainable AI · Video Analytics · Internet of Things · vision based feature extraction · ML based price prediction.

Teachable Machine is an experiment from Google to bring a no-code and low-code approach to training AI models. Anyone with a modern browser and webcam can quickly train a model with no prior knowledge or experience with AI. Teachable Machine is not exactly new. It was initially launched in 2017 and got revamped in 2019 with additional capabilities, including saving the model to Google Drive and exporting it to other applications. The community behind the project is continuously making it better. It has become so popular that education researcher Blakeley H. Payne and her teammates have been using Teachable Machine as part of an open-source curriculum that teaches middle-schoolers about AI through a hands-on learning experience.

While the technology industry talks about the superpowers of artificial intelligence (AI), incorporating it in business applications is not easy. Even for the most tech-savvy individual, AI is complex and intimidating technology. There have been many efforts in making AI accessible to developers, but there is still a lot of plumbing that needs to be done. From acquiring the data to labeling it and training the model to optimizing it, deep learning and AI demand niche skills that combine mathematics with data science. After all the effort, utilizing a fully trained model with applications is another tricky task.

I spent the first era learning how to build models with tools like scikit-learn and TensorFlow, which was hard and took forever. I spent most of that time feeling insecure about all the things I didn't know. The second era–after I kind of knew what I was doing–I spent wondering why building ML models was so damn hard. After my insecurity cleared, I took a critical look at the machine learning tools we used today and realized this stuff is a lot harder than it needs to be. That's why I think the way we learn ML today is about to change.

I spent the first era learning how to build models with tools like scikit-learn and TensorFlow, which was hard and took forever. I spent most of that time feeling insecure about all the things I didn't know. The second era–after I kind of knew what I was doing – I spent wondering why building machine learning models was so damn hard. After my insecurity cleared, I took a critical look at the machine learning tools we used today and realized this stuff is a lot harder than it needs to be. That's why I think the way we learn machine learning today is about to change. It's also why I'm always delighted when I discover a tool that makes model-building fun, intuitive, and friction-less.

Businesses today run on information and insights, which in turn, run on data. And to study this data that generally exists in an unstructured format, we need machine learning. Next, we also need AI to provide personalized services on a massive scale. However, the problem is that mastering machine learning is super hard and requires a time investment. Meanwhile, though businesses need decision-making insights about their customers, products, and usage to stay relevant in market flux, more than ever, for employees, it is frustrating to write SQL queries. Along with that, waiting on web engineers who would try to figure out algorithms proved quite arduous.

Teachable Machine is a web-based tool that makes creating machine learning models fast, easy, and accessible to everyone. You train a computer to recognize your images, sounds and poses without writing any machine learning code. Anyone can use this like Educators, artists, students, innovators, makers of all kinds -- really, anyone who has an idea they want to explore. You can currently train Teachable Machine with images (pulled from your webcam or image files), sounds (in one-second snippets from your mic), and poses (where the computer guesses the position of your arms, legs, etc from an image). Train your model by just clicking a single button no need for any smoothing or pre-processing required, Teachable Machine will train a model based on the examples you provided.