IBM AI Fundamentals: Generative AI Models GANs, VAEs, and Autoregressive Models

Discover the key generative AI models used to produce content, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive Models. Prepare for the IBM Artificial Intelligence Fundamentals certification exam with this comprehensive explanation.

Question

Which of the following models does generative AI use to produce content? Select all that apply.

A. Generative adversarial networks (GANs)
B. Variational autoencoders (VAEs)
C. Autoregressive models
D. Distributive anchored categories (DACs)

Answer

Generative AI employs several models to produce content, and the correct answers to this question are:

A. Generative adversarial networks (GANs)
B. Variational autoencoders (VAEs)
C. Autoregressive models

Explanation

There are three primary types of generative AI models: GANs, VAEs, and autoregressive models.

Generative AI employs several models to produce content, and the correct answers to this question are:

A. Generative adversarial networks (GANs)
B. Variational autoencoders (VAEs)
C. Autoregressive models

Here’s a detailed explanation of each model:

1. Generative Adversarial Networks (GANs): GANs consist of two neural networks – a generator and a discriminator – that compete against each other. The generator creates new data instances, while the discriminator evaluates them for authenticity. Through this adversarial process, the generator learns to create more realistic content over time.
2. Variational Autoencoders (VAEs): VAEs are a type of generative model that learns a compressed representation (latent space) of the input data. They consist of an encoder, which maps the input data to the latent space, and a decoder, which reconstructs the original data from the latent representation. By sampling from the latent space, VAEs can generate new content similar to the training data.
3. Autoregressive Models: Autoregressive models predict future values based on past values in a sequence. In the context of generative AI, these models generate new content by predicting the next element (e.g., pixel, word, or token) based on the previously generated elements. Examples of autoregressive models include PixelRNN, WaveNet, and GPT (Generative Pre-trained Transformer).

Distributive Anchored Categories (DACs) is not a generative AI model and is not used to produce content.

In summary, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive Models are the key generative AI models used to produce content, making options A, B, and C the correct answers to this IBM Artificial Intelligence Fundamentals certification exam question.

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