Generative artificial intelligence refers to deep learning models that can generate content such as images, code, text, and other forms of media. These algorithms are trained to identify patterns using large datasets, enabling them to produce similar output.

Maria Carrisa Sanchez, writing for TechRepublic Premium, presents this explanation of generative AI, including its evolution, features, examples, drawbacks, and benefits.

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HOW DOES GEN AI WORK?

Gen AI is a complex interplay of components and technologies, enabling the generation of creative and contextually relevant content. The key components and underlying mechanisms responsible for the functioning of gen AI are:

• Data collection: Gen AI begins with the gathering of large and diverse datasets relevant to the task at hand. For text generation, this may entail accumulating a massive collection of text documents, whereas for picture production, it could entail image collection. The quantity and quality of data are essential for efficiently training a gen AI model.

• Statistical models: Gen AI is based on statistical models that learn relationships and patterns in data. These models are mathematical and based on probability. Text is processed using statistical language models such as n-grams, or more complex models such as recurrent neural networks. CNNs are popular statistical models for image creation.

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TIME SAVED: Crafting this content required 16 hours of dedicated writing, editing, research, and design.