The rise of AI-native Low-Code/No-Code (LCNC) platforms enables autonomous agents capable of executing complex, long-duration business processes. However, a fundamental challenge remains: memory management. As agents operate over extended periods, they face "memory inflation" and "contextual degradation" issues, leading to inconsistent behavior, error accumulation, and increased computational cost. This paper proposes a novel hybrid memory system designed specifically for LCNC agents. Inspired by cognitive science, our architecture combines episodic and semantic memory components with a proactive "Intelligent Decay" mechanism. This mechanism intelligently prunes or consolidates memories based on a composite score factoring in recency, relevance, and user-specified utility. A key innovation is a user-centric visualization interface, aligned with the LCNC paradigm, which allows non-technical users to manage the agent's memory directly, for instance, by visually tagging which facts should be retained or forgotten. Through simulated long-running task experiments, we demonstrate that our system significantly outperforms traditional approaches like sliding windows and basic RAG, yielding superior task completion rates, contextual consistency, and long-term token cost efficiency. Our findings establish a new framework for building reliable, transparent AI agents capable of effective long-term learning and adaptation.

Many of us are familiar with the challenge of selecting a suitable machine learning model for a specific prediction task, given the vast number of models to choose from. On top of that, we also need to find optimal hyperparameters in order to maximize our model's performance. These challenges can largely be overcome through automated machine learning, or AutoML. I say largely because, despite its name, the process is not fully automated and still requires some manual tweaking and decision-making by the user. Essentially, AutoML frees the user from the daunting and time-consuming tasks of data preprocessing, model selection, hyperparameter optimization, and ensemble building.

The past decade has witnessed rapid progress in AI research since the breakthrough in deep learning. AI technology has been applied in almost every field; therefore, technical and non-technical end-users must understand these technologies to exploit them. However existing materials are designed for experts, but non-technical users need appealing materials that deliver complex ideas in easy-to-follow steps. One notable tool that fits such a profile is scrollytelling, an approach to storytelling that provides readers with a natural and rich experience at the reader's pace, along with in-depth interactive explanations of complex concepts. Hence, this work proposes a novel visualization design for creating a scrollytelling that can effectively explain an AI concept to non-technical users. As a demonstration of our design, we created a scrollytelling to explain the Siamese Neural Network for the visual similarity matching problem. Our approach helps create a visualization valuable for a short-timeline situation like a sales pitch. The results show that the visualization based on our novel design helps improve non-technical users' perception and machine learning concept knowledge acquisition compared to traditional materials like online articles.

Machine Learning (ML) interpretability and explainability are important concepts that refer to the ability of humans to understand and interpret the decisions made by machine learning models. These concepts have become increasingly important as machine learning models are being used in more critical applications, such as healthcare, finance, and criminal justice, where the decisions made by these models can have a significant impact on people's lives. One of the main challenges of ML interpretability and explainability is the complexity of the models. Machine learning models can be very complex, with many layers of neurons and thousands or even millions of parameters. This complexity can make it difficult for humans to understand how the model is making its decisions, which can be a problem when trying to explain the results to non-technical users.

Redbird, a New York-based enterprise analytics operating system, announced it has raised $7.6 million in an oversubscribed seed round. The Redbird platform allows non-technical users to automate and unify analytics work without writing code and connects all data sources into a no-code environment for data prep, wrangling, analysis, reporting, and data science, according to a company release. Though the company touts its platform's no-code features as friendly for non-technical users, it also aims to make life easier for data professionals: "Even for technical teams with these [data] skill sets, it can be challenging and time consuming, ultimately distracting them from higher value work. We created Redbird with the goal of making it easier for organizations who would like all of their employees to be equipped with a more unified, automated, and accessible approach to doing this type of work," said Erin Tavgac, Redbird CEO and co-founder. Data engineers may find Redbird helpful for building data integrations, managing ETL workflows, provisioning data views, and maintaining data science models.

The proliferation of the deployment of algorithms in several areas of society brought big concerns when it comes to ethics and morality. AI models can be extremely harmful to a fair and just society as they may reinforce some bias that is already present in the data fed to the models. Cathy O'Neill's book is a very concise read on the dangers of blindly applying AI models to every aspect of our society. If you are just starting to understand the impact of AI in our daily lives, this book will give you a thoughtful perspective of how machine learning models should be deployed and why you need to be careful with certain AI "magical" solutions. As with most technological breakthroughs, artificial intelligence has the power to be applied to a majority of use cases -- some of them will probably make society extremely productive, while others may have extremely harmful caveats.

The proliferation of the deployment of algorithms in several areas of society brought big concerns when it comes to ethics and morality. AI models can be extremely harmful to a fair and just society as they may reinforce some bias that is already present in the data fed to the models. Cathy O'Neill's book is a very concise read on the dangers of blindly applying AI models to every aspect of our society. If you are just starting to understand the impact of AI in our daily lives, this book will give you a thoughtful perspective of how machine learning models should be deployed and why you need to be careful with certain AI "magical" solutions. As with most technological breakthroughs, artificial intelligence has the power to be applied to a majority of use cases -- some of them will probably make society extremely productive, while others may have extremely harmful caveats.

No-code artificial intelligence (AI) tool sets out to demystify and democratize AI by providing non-technical users with code-free environments for building AI models. No-code tools use techniques like intuitive interfaces, templates, and drag-and-drop editors to build AI for tasks like image recognition, object detection, data classification, and predictive analytics. Here, we look at some of the no-code products currently available-- from free computer vision tools for home users to enterprise-level platforms. An uncluttered interface offers three categories of project to pick from: image, audio, or pose (body positions). Training data--image files and one-second audio clips--can be uploaded or captured via a Webcam or mic.

Automated machine learning (AutoML) helps to lower the barrier to entry for machine learning model building by streamlining the process thereby allowing non-technical users to harness the power of machine learning. On the other hand, the availability of AutoML also helps to free up the time of data scientists (that they would have otherwise spent doing redundant and repetitive pre-processing tasks or model building tasks) by allowing them to explore other areas of the data analytics pipeline. In a nutshell, users can supply an input dataset to the AutoML system that it uses for model building (feature transformation, feature selection, hyperparameter optimization, etc.) and finally it returns the predictions as the output. Wouldn't it be great if you can build your very own AutoML App that you can custom tailor to your heart's content? The development of this AutoML App is 2 folds: (1) Model deployment helps to complete the data life cycle and (2) AutoML helps to make ML accessible to non-technical users.

Companies are trying every possible way to meet customers' expectation. Yet another way that enhances their relationship with customers is coming up with chatbot, a software that's capable of carrying out conversations with the customers. What better than being addressed to your issue at any instant of time? Right from responding to your queries to offering you the best, chatbots have got you covered. Chatbots are employed to improve customer engagement and reduce customer support expenses as well.