An Introduction to Generative AI: Concepts, Tools, and Examples

Generative AI is a branch of artificial intelligence which could create new facts or content material, inclusive of textual content, images, code, video, and tune, based totally on existing data or content. In recent years, generative AI has made amazing strides and garnered a whole lot of interest thanks to many discoveries and advancements.

Generative AI has a wide array of use cases across various industries and domains, such as marketing, entertainment, education, design, and healthcare.

There are different types of generative models used in generative AI, and each model has a unique way of creating new content or data from scratch. Here are some of the common types of generative models:

1. Generative adversarial networks (GANs):

GAN model has two neurons, a generator and a discriminator, which compete in a game-like manner. Meanwhile, the discriminator works to distinguish between real and fake data, and the generator tries to generate real data that can filter it out GANs are more efficient in tasks such as data enhancement, style setting , and in image creation.

Popular frameworks and libraries that offer tools for working with Generative Adversarial Networks (GANs) include Tensorflow, Keras, PyTorch, GANLib, MXNet, Chainer, and GANs in Action. GAN architectures are simpler to use, train, and experiment with thanks to these tools. These tools have a variety of functions, ranging from lower-level frameworks that allow more control and flexibility to higher-level abstractions that make GAN implementation simpler. Some applications of GANs are Image Synthesis and Generation, Image-to-Image Translation, Text-to-Image Synthesis, Data Augmentation, Data Generation for Training.

2. Variational autoencoders (VAEs):

Variational autoencoders (VAEs) are a type of AI model that can create new data, like images or text, by learning from existing data. They use two parts: an encoder and a decoder. The encoder transforms the input data into a simplified version called latent space, which is like a map of the data’s main features. The decoder then uses this latent space to recreate the original data. VAEs are special because they add a bit of randomness to make the new data realistic and diverse.

While working with VAEs, Tensorflow, Keras, PyTorch, Edward, Pyro, MXNet, and Chainer are frequently utilized. When using VAEs, it’s critical to select a tool or library based on attributes like usability, adaptability, and harmony with the user’s current workflow and preferences. Some applications of VAE are Synthetic Data Creation, Image Generation and Style Transfer, Denoising Images, Anomaly Detection, Text-to-Image Synthesis.

3. Autoregressive models:

Autoregressive models produce data via forecasting a sequence’s next fee primarily based on its previous values. The correlations and dependencies among the statistics factors may be captured by way of them. The exceptional fashions for speech synthesis, track composition, and text era are autoregressive ones.

Several deep learning frameworks have been used to put in force generative AI autoregressive models, like PixelCNN and PixelRNN. Tensorflow, Keras, PyTorch, JAX, MXNet, Chainer, and Flax are a few frequently used tools. Some utility of Autoregressive fashions are Image Synthesis, Time Series Data, Music Generation.

4. Transformers:

In generative AI applications such as natural language processing (NLP), transformer models represent a class of deep learning models that are frequently employed.

The concept of self-attention, which permits the model to discover the connections and dependencies between the tokens in an input sequence — such as words in a sentence or paragraph — is the foundation of transformer theory. Self-attention allows the model to understand the relationships between all tokens in the input sequence.

The two primary parts of a transformer are an encoder and a decoder. The input sequence is fed into the encoder, which then converts it into a chain of context vectors — hidden representations. Using the context vectors, the decoder produces an output sequence that could be a summary, a translation, or a response. In order to be aware of the context vectors and its own prior outputs, the decoder also employs self-interest.

Some examples of transformer models are:

• BERT (Bidirectional Encoder Representations from Transformers): a model that uses only the encoder part of the transformer to learn representations of text for various NLP tasks, such as question answering, sentiment analysis, and named entity recognition3.
• GPT (Generative Pre-educated Transformer): OpenAI’s GPT series (such as GPT-three and GPT-4) makes use of transformers to generate coherent and context-conscious textual content. These fashions excel at tasks like language translation, summarization, and chatbot responses
• T5 (Text-To-Text Transfer Transformer): a model that makes use of both the encoder and the decoder components of the transformer to perform a wide variety of NLP responsibilities, inclusive of summarization, translation, textual content simplification, and text era. summarization, translation, text simplification, and text generation.

Large language models (LLMs):

Large language models represent a major advance in native AI, especially in the field of natural language processing. These models can understand, produce, and process human-like information with unprecedented accuracy and slowness.

Some of the most popular and powerful LLMs are:

• GPT-4: Generative Pre-trained Transformer model built by OpenAI. It can answer natural language questions, gather needed information, or even code. GPT-4 is a Transformer-based model that is pre-trained to predict the next token in the document. The process of post-training alignment results in better reality decisions and compliance with desired behaviors.
• LaMDA: A model developed by Google that can engage in open-ended conversations on any topic. It uses a technique called latent alignment to match the user’s intent and context with the most relevant response. It is capable of supporting a very vast variety of languages and formats, including text, speech, and graphics. It can also handle multiple languages and modalities, such as text, speech, or images.
• PaLM: A model developed by Google. It can be filled with information from the following areas: computer programming, mathematics, or creative writing. It uses a technique called pre-training and adaptation to fine-tune a large language model on a smaller and more focused dataset. It can also leverage external knowledge sources, such as Wikipedia or Stack Overflow, to enhance its output.

Generative AI has been a hot topic in 2023, and there are many examples of how it is transforming various industries. Here are some popular generative AI models used in 2023:

1. ChatGPT : Developed by means of OpenAI, ChatGPT is an AI-powered language model that could produce text that looks human by using context and previous conversations.
2. DALL-E2 : DALL·E 2 is an AI system that can create realistic images and art from a description in natural language. It can generate original, realistic images and art from a text description by combining concepts, attributes, and styles.
3. GPT-4 : GPT-4 is a deep learning model that could produce, adjust, and refine technical and creative writing duties, like writing screenplays, songs, or figuring out a person’s writing style
4. GitHub Copilot : GitHub Copilot is an AI-powered coding assistant developed by GitHub and OpenAI. It is a cloud-based tool that that allows users write code faster and smarter by means of autocompleting code.
5. AlphaCode : AlphaCode is a platform for learning and debugging programming problems. It is an AI system developed by DeepMind that can translate problem descriptions into code.
6. Bard : Bard is built on the foundation of the LaMDA family of large language models (LLMs), which enables it to understand natural language descriptions and generate responses based on context.
7. Cohere Generate : Cohere Generate uses large language models (LLMs) to generate text that is relevant and high-quality. The models are trained on vast amounts of data from the internet, enabling them to learn from patterns and generate optimized content.
8. Claude : Claude is built on the foundation of the LaMDA family of large language models (LLMs), which enables it to understand natural language descriptions and generate responses based on context.
9. Synthesia : Synthesia is an AI video creation platform that enables users to create professional-quality videos without the need for cameras, microphones, actors, or studios. It uses AI avatars and voiceovers in over 120 languages to create engaging videos in minutes.
10. Descript : Descript is an all-in-one video and podcast editing platform that allows users to write, record, transcribe, edit, collaborate, and share videos and podcasts with AI features. It is a powerful tool that can be used for editing video, audio, recording screens, and transcribing.
11. Type Studio : Type Studio is an online video text converter and editor tool that can transcribe video text automatically and convert it into written text. It offers features like subtitling, image and text addition, recording, and podcast editing for various industries and languages. The platform supports over 30 different languages and allows exporting in various formats.
12. Designs.ai : Designs.ai is an online platform that uses artificial intelligence to help users create, edit, and scale content for various purposes and platforms.

References:

1. Google Generative AI — Google AI
2. What is Generative AI? — Examples, Definition & Models (geeksforgeeks.org)
3. Generative AI: Definition, Tools, Models, Benefits & More (analyticsvidhya.com)
4. GitHub — microsoft/generative-ai-for-beginners: 12 Lessons, Get Started Building with Generative AI ???? https://microsoft.github.io/generative-ai-for-beginners/
5. Generative Adversarial Networks — mxnet documentation (apache.org)
6. GAN Libraries for Deep Learning | GAN for Data Scientists (analyticsvidhya.com)