Generative AI

Gartner Trend: Generative AI

Generative AI learns about artifacts from data, and generates innovative new creations that are similar to the original but doesn’t repeat it.

Generative AI has the potential to create new forms of creative content, such as video, and accelerate R&D cycles in fields ranging from medicine to product creation.

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— https://www.gartner.com/en/information-technology/insights/top-technology-trends

What is Generative AI?

Generative AI is a branch of artificial intelligence that focuses on creating new data or content from existing data or content. Generative AI can produce realistic images, texts, sounds, videos and more, based on the patterns and features learned from the input data.

One of the most popular applications of generative AI is deepfake, which is the process of swapping faces or voices in videos or audio clips. Deepfake can be used for entertainment purposes, such as creating celebrity parodies or impersonations. However, it can also pose ethical and social challenges, such as spreading misinformation or violating privacy.

Another example of generative AI is text generation, which is the task of producing natural language texts from a given prompt or context. Text generation can be used for various purposes, such as summarizing articles, writing captions, generating headlines, composing poems or stories and more. Text generation can also enhance human creativity and productivity by providing suggestions or alternatives.

Generative AI relies on different techniques and models to achieve its goals. One of the most common techniques is generative adversarial networks (GANs), which consist of two competing neural networks: a generator and a discriminator. The generator tries to create fake data that looks like the real data, while the discriminator tries to distinguish between the real and fake data. The generator learns from its mistakes and improves over time.