Generative AI, or GenAI, a revolutionary branch of artificial intelligence, lets users create original content based on several data types. As a result, it has gained prominence in creative fields, including art and music, language and design, and problem-solving. Its use cases extend beyond these areas.

In this blog, we delve deep into the wonder world of generative AI and discover its unique potential for various industries.

What is Generative AI?

Generative AI is a type of artificial intelligence that can generate several types of content, such as text, images, audio, and synthetic data. GenAI uses advanced neural network topologies and algorithms to create advanced outputs across several domains compared to common AI models. You give a prompt in the form of any input the AI model understands. Based on the prompt, the AI system generates original content.

What are generative neural network architectures?

There are many use cases for Generative AI. To capture the features and patterns of the training dataset, each type of work may demand a deep-learning architecture design. Therefore, neural network architectures assume great importance in the performance of Generative AI models.

Generative Adversarial Networks (GANs), Neural Radiance Fields (NeRF), Normalising Flows, Transformer models, Variational Networks (VAEs), and hybrid forms are a few available designs. Also, each architecture has unique capabilities that add to the use cases and developments in Generative AI.

What are the different types of GenAI models? 

1. Transformers

The transformer model often used in Natural Language Processing tasks is deep learning. Thus, the model is fundamentally used to learn relationships between words in a sentence or a series of texts. For this purpose, it uses a method known as self-attention. The advantages are text comprehension and content/code completion.

Furthermore, it helps with NLP tasks such as text categorization, language modeling, machine translation, and question-answering. Transformer processes input sequences parallel to make them faster than recurrent neural networks (RNNs) for many NLP tasks.

There are three types of transformer models: encoders, decoders, and encoder-decoder designs. Additionally, the classification is based on the several functions these components offer to assist in the overall operation of the model. The encoders grasp the input sequence. They implement the input analysis and recognize its meaning along with the context. 

Meanwhile, decoders generate output based on the information collected by the encoder. From the encoded representations, decoders create the desired output sequences. The encoder-decoder models have encoder-decoder components. When the encoder understands the input sequence, the decoder provides the corresponding output sequence.

The following are a few encoder-only models:

• ALBERT, BERT, Electra – Google
• DeBERTaV3- Microsoft
•  DistilBert- Hugging Face
•  RoBERTa-Facebook.

Some of the decoder-only models are:

• BioGPT-Microsoft
• LLaMa-Facebook
•  CodeGen-Salesforce
• GPT-J, GPT-NEO, GPT-NEOX-20B-EleutherAI
•  GPT, GPT2, GPT-3, GPT-4-OpenAI
• OPT-Facebook
• Bloom-BigScience
• Nemo-Megatron-GPT- NVidia

      Below are a few Encoder-Decoder models:

• T5,FLAN, Pegasus, MT5, UL2, FLAN-UL2- Google
• BART-Facebook
• Code-T5-Salesforce
• DialoGPT
• EdgeFormer-Microsoft
• PaLM

2. Generative Adversarial Networks (GANs)

GANs possess a new adversarial training process that combines two neural networks:

a generator and

a discriminator.

The generator creates synthetic data, and the discriminator assesses if the generated data is genuine or fake. Also, adversarial training helps repeatedly improve the two neural networks.

Therefore, it helps create high-quality, original, and realistic output. Most often, the GAN models are created by different organizations and researchers. Some of these include:

• DCGAN (Deep Convolutional GAN)
• CGAN (Conditional GAN)
• WGAN (Wasserstein GAN)
• CycleGAN
• Pix2Pix
• BigGAN
• StyleGAN
• StyleGAN2
• StyleGAN3
•  PGGAN (Progressive GAN)
•  SN-GAN (Spectral Normalization GAN)
•  ProGAN (Progressive GAN)
•  DiscoGAN ALI (Adversarially Learned Inference)
•  BiGAN (Bidirectional GAN)

3. Variational Networks (VAEs)

A variational network is an AI that can learn to create new things. Therefore, it studies several examples and identifies patterns. Understanding the patterns creates similar outputs, but they are relatively new. Think of teaching a computer how to draw cars by showing many pictures of cars. The system learns how cars look and can then draw new pictures of cars.

In other words, variational networks are a type of generative models that encode input data into a latent space. Each point represents a specific characteristic. The encoded data is then decoded to generate the original input.

In fact, it lets the model generate new and comparable data. Therefore, VAEs consist of an encoder and a decoder. In the latent space, the encoder transforms input data into a probabilistic distribution. The decoder reconstructs data from this distribution.

In VAEs, there is an element of uncertainty in the outcomes. Further, it allows them to generate several outcomes which are very realistic. The use cases of VAEs include text, audio image, and video generation. A few of the most common VAE models include:

• VAE (Variational Autoencoder)
• beta-VAE
• CVAE (Conditional Variational Autoencoder)
• InfoVAE
• HVAE (Hierarchical Variational Autoencoder)
• VLAE (Variational Lossy Autoencoder)
• AAE (Adversarial Autoencoder)
• SVG-VAE (Structured Variational Autoencoder)
• DGMG (Deep Generative Model of Graphs)
• VAE-GAN (Variational Autoencoder Generative Adversarial Network)
•  Neural Processes

4. Diffusion Models

Diffusion models are a type of GenAI model that creates data iteratively using a diffusion process. Further, it helps create high-dimensional samples that are too complex.

The diffusion models convert a simple and easily accessible distribution into a more complicated and useful data distribution using reversible operations.

Once the model understands the transformation process, it creates new samples. It begins from a point in the simple distribution and gradually expands to the required complex data distribution. Also, it is useful for creating pictures and videos or helps with image synthesis.

Let’s see some of the most popular diffusion models:

• Stable Diffusion
• Noise-Contrastive Priors (NCP)
• Denoising Score Matching (DSM)
• Diffusion Models for Image Generation
• Perceptual Diffusion Models
• Generative Diffusion Models
• Continuous Relaxation of Discrete Diffusion Models
• DDPM (Diffusion Probabilistic Models)
• Diffusion Probabilistic Models (DPM)

5. Flow model

Flow-based models learn the underlying structure of a specific dataset. By analyzing the probability distributions of the various values or occurrences in the dataset, flaw models implement this.

Once the model has acquired this probability distribution, it can generate new data points with statistical properties and characteristics identical to those in the initial dataset.

Flow-based models utilize an invertible transformation of the input data. Further, this type of GenAI model may create new samples without requiring complex optimization very fast. As a result, flow-based models are more computationally efficient and faster than others.

The list of some popular flow-based models include:

• Normalizing Flows
• Radial Flows
• Planar Flows
• IAF (Inverse Autoregressive Flow)
• Fréchet Inception Distance (FID)
• MAF (Masked Autoregressive Flow)

6. Neural Radiance Fields (NeRF)

NeRF is another approach in AI that uses deep learning to generate 3D sceneries from 2D photos. Also, it defines a scene as a continuous volumetric radiance field, learned from a series of photos taken from multiple angles, to provide realistic 3D renderings. Additionally,  it is a 3D model from a series of photos of an object from multiple perspectives. 

Some common Neural Radiance Fields models are:

• Original NeRF
• NeRF++
• Instant Neural Graphics Primitives (iNGPs)
• MiDaS-NeRF
• PlenOxels

7. Hybrid Models

Hybrid models in generative AI architectures have become more common as researchers look to improve model performance, efficiency, and stability. Thus, it combines many generative models or integrates generative models with other techniques to maximize their capabilities. Below are some of the examples of hybrid models in generative AI.

• Variational Autoencoder-Generative Adversarial Network (VAE-GAN)
• Flow-Based Generative Models with VAEs
• Adversarially Regularized Autoencoders (ARAE)
• Neural Radiance Fields (NeRF) with GANs
• Generative Adversarial Transformer (GPT, GPT-2, GPT-3)

What are the common classifications of generative AI models?

Generative AI models are text-based or image-based models in accordance with the inputs they process. Therefore, based on the number of input types used, GenAI models are classified as unimodal and multimodal.

1. Unimodal generative AI model

It works with one type of data, for example, text, image, or audio, for both input and output.

• Text-based Large Language Models (LLMs): GPT, Jasper, AI-Writer, and Lex
• Vision-based models: VAEs, Midjourney and Stable Diffusion
• Music-based models: Amper, Dadabots, and MuseNet.
• Audio models: Whisper, VALL-E-
• Code generation models: CodeStarter, Codex, GitHub Copilot, and Tabnine
• Voice synthesis models: Descript, Listnr, and Podcast.ai.

2. Multimodal generative AI model

It can handle inputs from different formats and potentially generate outputs in different formats as well.

• LaMDA: uses text and code for inputs.
• DALL-E 2: uses text and images for inputs.
• GPT-3: uses text and images for inputs.
• Donut- ClovaAI
• Picasso– utilizes text and image pairs for inputs.
• Tr-OCR-Microsoft
• LayoutMV3- Microsoft
• UnifiedIO- Allenai
• CLIP- OpenAI

How Do Generative AI Models Work?

Generative AI models support evolving artificial intelligence technology and are big-data-driven models capable of producing realistic content. Typically, to train generative AI models, we use unsupervised or semi-supervised learning methods. Also, these methods detect small-scale and large-scale patterns and correlations in training datasets from several sources, such as the Internet, Wikipedia, image libraries, and so forth. Further, this training helps GenAI models replicate the patterns while generating new content and makes them appear similar to being created by a human rather than machine-generated.

Generative AI models may replicate actual human content exactly as they are created using layers of neural networks. In fact, they implement it by emulating the connections between neurons in the human brain.

Usually, when the neural network design is combined with a large set of training data, complex deep learning and training algorithms, and regular re-training and updates, these models can improve and “learn” over time and scale. Also, some generative AI models include text-to-text generators, image-to-image generators, and image-to-text generators.

What are the real-world uses for generative AI?

Generative artificial intelligence (AI) is a revolutionary technology that serves many purposes and has endless applications in a wide range of fields.

Data Augmentation: Uses synthetic data to train machine learning models when actual data is scarce or costly.

Text Generation: Creates new articles and text formats with different writing styles.

Language Translation: Converts text from one language into another and corrects grammatical problems.

Content Recommendation: Offers personalized recommendations for e-commerce and other similar sites.

Customer Experience Management: Uses chatbots to answer queries and receive feedback.

Image Generation: Creates and makes changes to many images to discover new creative possibilities.

VR/AR Development: Designs virtual avatars for video games, augmented reality platforms, and metaverse games.

Music Composition: Assists artists and music composers in generating new musical ideas and innovative works.

Healthcare: Creates personalized treatment methods using multimodal patient data. Analyze medical photos and report on the findings.

Product Design: Generates new and innovative product ideas/ concepts online.

Style Transfer: Works along with many artistic styles to bring a single piece of content.

Anomaly Detection: Identifies anomalies in the operations to help with decision-making across various industries. For example, from manufacturing and finance to cybersecurity and many more. 

What generative AI models apply to various industries?

• Generative AI can help identify anomalies in products manufactured and understand the fundamental issues by using the data from sensors.
• In financial firms, using GenAI can help detect fraud by analyzing transactions based on an individual’s history.
• Legal professionals can use generative AI to generate contract creation, implement evidence analysis, and argument development.
• Generative AI can help entertainment businesses develop more cost-effective material and translate it into multiple languages using actors’ voices.
• Architects can use generative AI to generate and modify prototypes rapidly.
• Generative AI can help medical professionals and businesses discover promising new drugs efficiently.
• In the education field, Generative AI can improve education, including content production, personalization, interactive learning, and research.
• Gaming businesses can make use of generative AI to build games.

What are the concerns surrounding generative AI?

There are several concerns with respect to the use of generative AI. It includes the following:

• Generative AI models provide inaccurate information.
• Further, there is a chance that it may encourage plagiarism and thus affect the existing business models built around search engine optimization and advertising.
• It is hard to trust without transparency with respect to the source and the background of information.
• Chances are that photographic evidence can be faked or manipulated by AI.
• Moreover, it will likely impersonate people for more effective social engineering cyber attacks.

Final Remarks

Generative AI serves many purposes with its inherent ability in creative expression, innovation, and problem-solving. Also, concerns remain because of the chances of misuse, plagiarism, and spreading misinformation.

But, as technology evolves, these concerns will certainly be addressed, and creating ethical frameworks will help generative AI address its potential flaws.

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