Generative AI refers to a class of AI technologies that is capable of generating various forms of content, including but not limited to text, images, audio, and video. These AI systems can generate new content, based on their training data and input parameters, which usually include text prompts but can also involve other forms of input such as images. The recent buzz around generative AI comes from the simplicity with which new user interfaces powered by this AI technology can create high-quality text, graphics, and videos in seconds.

In very simple terms, generative AI is about making new data that looks like the data from which it has been trained. In other words, learning the underlying patterns, structures, and distributions of input data enables a procedure within the model allowing it to generate new data in a similar way. For instance, if trained on a dataset of human faces, a generative AI model will be able to create completely new faces of...

Generative AI is an exciting domain of AI that deals with the generation of new, synthetic data by learning patterns from existing datasets, aiming to generate outputs that share similar statistical properties and characteristics with the training data. Here is a broad overview of some of the most prominent types of generative models: VAEs, GANs, and autoregressive models.

VAEs

One of the most popular generative models is the VAE. The core idea behind VAE consists of learning a probabilistic mapping between data and a latent space, and vice versa. This means learning how to convert real data into a simplified representation (such as a compressed form) and then back again into data that looks real. VAEs are designed to ensure a high likelihood of the data while preserving a well-structured latent space to enable the generation of new data samples similar to the training data. Some of the most common flavors of VAE are as follows:

• VAE: The...

Generative AI has innovation capabilities that cover a diverse set of fields. As discussed earlier, industries such as healthcare, finance, education, media and entertainment, marketing, manufacturing, retail, and so on all have the potential to benefit from generative AI capabilities. The following is a survey of only a few of the primary applications of generative AI:

• Generation of images and videos: Multimodal generative models (that is, LLMs that can handle images, text, audio, and videos) are making it possible to generate realistic audio/visuals through various platforms and tools. For example, in media and entertainment, generative AI can help in generating visual effects, designing avatars, and developing virtual reality content. In the fashion and design industry, it is being used to conceptualize new clothing designs, prepare virtual fashion shows, and make fashion trend predictions. In marketing ads and promotion space, generative AI...

Though generative AI has immense benefits, it is not without its own set of challenges and limitations. Some of these challenges and limitations need to be taken into account with a lot of caution while considering a generative AI technology for any particular use case. Here’s a brief discussion of some of the most common caveats related to LLMs and some of the ways to mitigate them.

Quality of data and bias

The generative models are largely aided by the quality and diversity of data in the training dataset. Any model trained with biased or unrepresentative data will reproduce outputs with the same kind of bias, hence solidifying existing biases or allowing marginalization of one or several groups in case of bias in the training data.

One way this challenge can be dealt with is by ensuring richness in diversity, good quality data, and so on for a wide array of perspectives within the dataset itself used for training. As with...

Generative AI is a rather rapidly growing area, having huge disruptive potential for industries and changing areas of our lives. In contrast, applications of generative AI include very realistic image generation and generation of text, accelerated drug discovery, and enriched creative expression.

One of the key points regarding this is an understanding of the different generative models, such as VAEs, GANs, and autoregressive models, that provide insight into how technologies work and where they can be applied. It would also, however, be important to state the many problems and limitations generative AI faces, including data quality, computational resources, ethical considerations, and even creativity.

In the next chapter, we will learn about the principles of agentic systems, which include the concepts of agency and autonomy, the characteristics of intelligent agents, various architectures of intelligent systems, and multi-agent systems.

1. Generative AI models can only take text data as input. True or false?
2. What are the different types of generative AI models?
3. What are some of the ethical and social implications of generative AI?
4. What are some of the methods to mitigate bias in training data?
5. What are some of the text and content generation open source LLMs?

1. False. Generative AI models can take text as well as image, video, and audio data as inputs.
2. VAEs, GANs, and autoregressive models.
3. Deepfakes, misinformation, issues with copyright or intellectual property, and job displacements are some of the social and ethical implications of generative AI.
4. Some of the common methods to address bias in training data are oversampling or undersampling.
5. Mistral, LLaMA, and Bloom are some of the open source text and content generation LLMs.

• Mastering Machine Learning Algorithms – Second Edition by Giuseppe Bonaccorso
• Machine Learning for Imbalanced Data by Kumar Abhishek and Dr. Mounir Abdelaziz
• Generative AI with Python and TensorFlow 2 by Joseph Babcock and Raghav Bali

1. Auto-Encoding Variational Bayes: https://arxiv.org/abs/1312.6114
2. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework: https://openreview.net/forum?id=Sy2fzU9gl
3. Learning Structured Output Representation using Deep Conditional Generative Models: https://papers.nips.cc/paper_files/paper/2015/hash/8d55a249e6baa5c06772297520da2051-Abstract.html
4. Generative Adversarial Networks: https://arxiv.org/abs/1406.2661
5. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks: https://arxiv.org/abs/1511.06434
6. Wasserstein GAN: https://arxiv.org/abs/1701.07875
7. A Style-Based Generator Architecture for Generative Adversarial Networks: https://ieeexplore.ieee.org/document/8953766
8. Attention Is All You Need: https://arxiv.org/abs/1706.03762
9. Conditional Image Generation with PixelCNN Decoders: https://arxiv.org/abs/1606.05328
10. PixelSNAIL: An Improved Autoregressive Generative Model: https:/...

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