The application of data-driven machine learning (ML) has created the prospect of a new paradigm in research and development. ML has proven an excellent method for identifying structure-activity connections in material data. The introduction of LLMs has changed business management and operations completely. Generative AI is an advanced version of artificial intelligence merged with ML and deep learning. It can be utilized via models and techniques to create human resembling data. To effectively understand the utilization of Gen AI, you must collaborate with a Gen AI consulting company. Let’s know the types of Gen AI models.

Types Of Gen AI Models

GANs
GAN, which stands for Generative Adversarial Network, is an advanced deep learning architecture comprising two key components: a generator and a discriminator. The generator's principal role is to produce synthetic data resembling accurate data, whereas the discriminator discriminates between genuine and created data. The generator improves the authenticity of its produced data through adversarial training, whereas the discriminator accurately decides whether the data is natural or synthetic. GAN, a generative model, is widely used in deep learning to generate samples that improve data augmentation and pre-processing approaches.

Diffusion Models
Generative diffusion models can generate new data from the ones they were trained on. For example, when trained on various human faces, a diffusion model can develop new and lifelike faces with multiple traits and expressions not included in the original dataset. The fundamental principle behind diffusion models is to convert a simple and easily accessible distribution into a more complex and relevant data distribution. This conversion is achieved through a sequence of reversible operations, a key technical aspect of the model. Once the model comprehends this transformation process, it can generate new samples by initiating from a point in the simple distribution and progressively expanding it to the desired complex data distribution.

Variational Autoencoders
VAEs are generative models that use autoencoders and probabilistic modeling to create a compact data representation. VAEs encode input data in a lower-dimensional latent space, enabling the creation of fresh samples by sampling points from the acquired distribution. VAEs demonstrate adaptability across domains, with practical applications ranging from picture production to data compression, anomaly detection, and drug development.

Transformer Based Models
Transformer-based models have transformed natural language processing (NLP) by adding a very effective technique called self-attention, which allows the model to weigh the value of distinct words in a phrase relative to one another, regardless of their position. This architecture, described by Vaswani et al. in the paper "Attention is All You Need," processes input data in parallel, resulting in higher efficiency and performance than previous models such as RNNs and LSTMs. Google developed BERT (Bidirectional Encoder Representations from Transformers), an example of a transformer-based model. BERT understands the context of a word based on its surrounding words, allowing it to execute tasks like question answering and linguistic inference with high accuracy.

Large Language Models
Large Language Models (LLMs) are robust AI systems that analyze and generate human-like language using massive pre-training data. These models, like OpenAI's GPT-3 and Google's BERT, are based on transformer architecture, which collects contextual relationships inside text. LLMs gain a profound understanding of language nuances through training on different and large datasets, allowing them to execute a wide range of tasks with outstanding accuracy. For example, GPT-3 can make logical essays, answer complicated questions, write code, and even write limericks by anticipating the next word in a sequence, depending on the context provided. This versatility makes LLMs essential in various applications, including automated customer service, content production, language translation, and more, demonstrating their transformational power.

Also read A brief introduction to Generative AI and its relevance in AI research

Conclusion

Enterprises must evaluate their alternatives when implementing and deploying foundation models for their use cases. Each use case has unique requirements, and numerous factors must be addressed while deciding on deployment alternatives. These considerations include cost, effort, data privacy, intellectual property, and security. A Gen AI consulting company can help you in finding the right Gen AI model for your business use case. Integrating Generative AI can transform your business for better.