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  • Writer's pictureMario Candido

What is Generative AI?

Generative AI refers to a class of artificial intelligence models that can generate new data samples that are similar to a given set of input data. In other words, these models can produce novel content, sometimes even content that wasn't explicitly present in their training data but shares the same underlying patterns. Here are some notable applications and examples of generative AI:

  1. Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, that are trained together. The generator tries to create fake data, and the discriminator tries to distinguish between real and fake data. Over time, the generator gets better at producing realistic data. GANs have been used to generate realistic images, music, and even video.

  2. Variational Autoencoders (VAEs): VAEs are a kind of autoencoder that can generate new data samples. They learn to encode and decode data, and can generate new samples by decoding random encodings.

  3. Text Generation: Models like OpenAI's GPT (like the one you're interacting with) can generate coherent and contextually relevant text based on a given prompt.

  4. Music and Video Generation: Generative models have been used to create new pieces of music or short video clips by understanding and mimicking the patterns in the training data.

  5. Art Creation: AI has been used to generate novel pieces of art, both in the style of existing artists and in entirely new styles.

  6. Drug Discovery: Generative models can predict molecular structures for potential new drugs by learning from existing molecular data.

  7. Video Games: Generative AI can be used to create levels, characters, and even story elements for video games.

Generative AI has shown promise in a wide range of applications but is also accompanied by challenges and concerns. One notable concern is the potential misuse in creating realistic fake content, such as deepfakes in videos, or generating misleading text. It's an evolving field, and researchers are constantly finding new applications and refining the technology.

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