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Responsible AI refers to the practice of designing, deploying, and managing artificial intelligence systems in a way that is ethical, transparent, accountable, and aligned with human values.
Generative Adversarial Networks (GANs) are a class of machine learning models used to generate new, realistic data by training two neural networks in competition with one another. This adversarial setup enables AI systems to create synthetic data, such as images,…
Model Drift refers to the gradual decline in a machine learning model’s performance over time as real-world data diverges from the data it was originally trained on. It can lead to inaccurate predictions, biased outcomes, and unreliable decision-making in production…
Generative AI is artificial intelligence that creates new content. Give it a text prompt and it writes an essay. Feed it a description and it generates an image. It can produce code, music, videos and more. Unlike traditional AI that…
Model interpretability is the ability to understand how and why an artificial intelligence or machine learning model makes its predictions. It provides transparency into a model’s decision-making process — revealing which data features influenced the outcome and how they interacted…
A Generative AI Agent is an autonomous system powered by artificial intelligence that can understand context, reason, and generate original outputs to achieve specific goals. Unlike traditional chatbots or rule-based systems, generative agents use large language models and multimodal capabilities…
Model validation and testing are the processes used to evaluate how accurately and reliably an artificial intelligence or machine learning model performs before it’s deployed in production. They ensure that models make trustworthy predictions, generalise well to new data, and…
Hallucination prevention in AI refers to the methods and techniques used to reduce false or fabricated outputs generated by artificial intelligence systems. In the context of large language models and generative AI, a hallucination occurs when an AI confidently produces…
An AI Moat refers to the competitive advantage a company gains by developing artificial intelligence capabilities that are difficult for competitors to replicate. Just as a moat protects a castle, an AI moat protects a business from market disruption by…
Human-in-the-Loop (HITL) is an approach in artificial intelligence where humans actively participate in the training, validation, or operation of a model to improve its accuracy and reliability. Rather than fully automating decision-making, HITL systems combine the speed of machines with…