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A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. These algorithms use existing data like text, images, and audio to generate content that looks like it comes from the real world.
AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand. Common choices include neural networks (used in deep learning), decisiontrees, support vector machines, and more. Over time, the algorithm improves its accuracy and can make better predictions on new, unseen data.
Algorithmic Accountability: Explainability ensures accountability in machine learning and AI systems. It allows developers, auditors, and regulators to examine the decision-making processes of the models, identify potential biases or errors, and assess their compliance with ethical guidelines and legal requirements. Russell, C. &
Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data. When and where each kernel is used?
In 2018, there were extensive news reports that an Uber self-driving car made an accident with a pedestrian in Tempe, Arizona. Image Credits: The New York Times Read more: [link] In another 2018 story , Amazon was found to show bias toward male candidates in the recruitment process because of an issue with their AI-powered HR recruiting tool.
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