In this article, we explain how AI uses a “loss function” to quantify its mistakes and improve its prediction accuracy through repeated learning based on those errors.
AI is transforming solo entrepreneurship by streamlining product design and marketing, making it easier for individuals to bring their ideas to life.
Test data, which is used to measure the true capabilities of AI, plays a crucial role in evaluating how well an AI system can handle unfamiliar problems. It serves as a way to assess the AI’s ability to apply what it has learned to new situations, and therefore must be used with great care.
Validation data in AI is a crucial step used to check the model’s generalization ability and to fine-tune its settings, known as hyperparameters. This process can be compared to tasting a dish while cooking—it helps adjust the recipe before it’s finished.
To become smarter, AI relies heavily on “training data”—a vast collection of information from which it learns patterns and gains knowledge. This article explains how AI grows by learning from such data and why the quality and diversity of that data are essential to its development.
AWS simplifies generative AI integration for businesses with RAG technology, enabling efficient use of internal data without extensive retraining costs.
The reason AI appears to “understand” lies in its ability to apply what it has learned to new situations—this is known as “generalization.” This skill is key to enhancing AI’s adaptability and usefulness across different contexts.
Reinforcement learning in AI is a mechanism through which optimal actions are learned by trial and error. It resembles the way humans grow through experience, gradually improving by trying different approaches and learning from the outcomes.
Swisscom’s AI-powered Network Assistant simplifies network management for engineers, enhancing efficiency and decision-making while ensuring data security.
AI is developing the ability to identify patterns and organize information on its own through a method called “unsupervised learning,” which allows it to learn from data without being given the correct answers.
