Generative AI
Generative AI (GenAI) is a broad term for any type of AI system capable of using generative models to create new forms of humanlike creative content, like text, images, music, audio, video and more.
Large language models (LLMs) are one type of GenAI since they generate text. However, not all GenAI systems are LLMs. GenAI models include various algorithms able to learn the various patterns and structures of input training data before generating novel outputs with similar characteristics.
The massive popularity of ChatGPT, perhaps the most successful proof of concept in technological history, has pushed GenAI enablement to the forefront of digital transformation discussions at all levels of the business, from the board to executives to lines of business. And for good reason. The versatility and potential of GenAI to transform various aspects of business operations make it an attractive investment for companies across industries.
GenAI in action: Meet "Atom Ai" from WWT
Part 1: Meet Atom Ai – the GPT Powering Innovation at WWT
Part 2: Inside Atom Ai – How Retrieval Mechanisms Shape AI Responses
Part 3: Inside Atom Ai – How Generation Processes Enrich AI Conversations
Part 4: Inside Atom Ai – Orchestrating and Deploying RAG at Scale for Robust AI Performance
GenAI in action: Meet "RFP Assistant" from WWT
Part 2: Inside RFP Assistant — Summarizer and Qualification Workflow
Part 3: Inside RFP Assistant — Generating a Full RFP Response
How Atom Ai and RFP Assistant have Evolved and What it Means for the Future
How does GenAI work?
GenAI models rely on various neural network architectures (e.g., transformers, generative adversarial networks (GANs), variational auto-encoders (VAEs) and diffusion networks) to produce their humanlike outputs. Each works a bit differently.
Transformers
Transformers use mechanisms called attention modules to efficiently process input data in parallel, learn their relationships, and then generate new forms of creative content. Examples include GPT-3 and DALL-E.
Variational auto-encoders (VAEs)
VAEs leverage encoders to transform input data into latent vectors, while decoders learn to reconstruct samples of these vectors into original outputs that closely resemble the input data. Examples include VAE-GAN and MusicVAE.
Generative adversarial networks (GANs)
GANs leverage adversarial learning — a technique to simultaneously train two neural networks against each other in a competitive game-like scenario — to improve the accuracy of both models over time. Examples include StyleGAN and CycleGAN.
Diffusion networks
A type of deep neural network that adds noise to a training dataset and then reverses the process to recover the data, gradually learning to remove the noise and produce realistic data similar to the original input. Examples include Stable Diffusion and Midjourney.
The role of Responsible AI
GenAI is an exciting and rapidly evolving field with many benefits and opportunities for innovation. Yet there are also challenges and risks, including issues touching on data quality and privacy, ethics, bias and fairness, and the potential for misuse and abuse by bad actors.
WWT is committed to Responsible AI — the practice of designing, developing and deploying AI systems in a way that is safe, ethical and fair. By considering the downstream risks and benefits of AI systems from a holistic perspective, organizations can often mitigate prospective harm.
At WWT, we've been helping our clients and partners implement responsible business and technology solutions related to AI for years. Our practical approach helps organizations leverage the latest AI systems to achieve results while maturing their data strategy and high-performance architecture (HPA) capabilities.
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