Author: Agnel Anjuluse

Neuromorphic computing is an approach to computing that draws inspiration from the architecture and functioning of the human brain. The term “neuromorphic” comes from “neuromimesis,” which means mimicking the structure or function of the nervous system. Neuromorphic computing aims to build artificial systems that can perform cognitive tasks in ways similar to the human brain,… Continue reading Neuromorphic computing

Graph Neural Networks (GNNs) are a class of neural networks designed to operate on graph-structured data. In contrast to traditional neural networks that work well with grid-structured data like images, GNNs are specifically tailored for tasks where the data has a graph-like structure, such as social networks, citation networks, molecular structures, and more. Here are… Continue reading Graph Neural Networks (GNNs)

Federated Learning (FL) is a machine learning approach that enables training models across decentralized edge devices without exchanging raw data. Instead of sending data to a central server, model updates are computed locally on each device, and only these updates are aggregated to improve the global model. Testing federated learning systems involves addressing various challenges… Continue reading Federated Learning Testing

Adversarial testing, also known as adversarial machine learning, is a testing method focused on evaluating the robustness and security of machine learning models. The main idea behind adversarial testing is to intentionally introduce small, carefully crafted perturbations or adversarial examples into the input data to observe how the model responds. Here are key aspects of… Continue reading Adversarial testing

When we create a quote, an item line is added automatically Item: Outgoing Freight, Price: 0, Quantity: 1 Try preparing a quote as follows: Customer = Purcee Issue: no tax item is selected for the line item Customer = Transfab Issue: they are on Collect, but the line item still gets added Solution:

Feedback Loop Testing is a type of software testing that focuses on assessing the effectiveness, responsiveness, and accuracy of feedback mechanisms in software systems, especially in the context of artificial intelligence (AI) and machine learning (ML) applications. Feedback loops play a crucial role in AI systems, as they often involve continuous learning and adaptation based… Continue reading Feedback Loop Testing

Diffblue is a software company that specializes in artificial intelligence (AI)-driven test automation for software development. One of its primary products is Diffblue Cover, which is an AI-based tool designed to automatically generate unit tests for Java code. Here are some key features and aspects of Diffblue Cover: Automated Test Generation: Diffblue Cover uses AI… Continue reading Diffblue

Orchestration testing is a specific type of testing focused on verifying the correctness and reliability of orchestration processes in software applications or systems. Orchestration is the coordination and management of multiple components or services to perform a specific task or workflow. This can be a critical aspect of many modern applications, especially in distributed and… Continue reading Orchestration Testing

Neurosymbolic AI testing involves evaluating and validating AI systems that combine neural networks (deep learning) with symbolic reasoning or knowledge representation methods. These systems aim to bridge the gap between traditional symbolic AI, which is strong in logical reasoning and knowledge representation, and neural networks, which excel at handling unstructured data and pattern recognition. Neurosymbolic… Continue reading Neurosymbolic AI testing