Generative AI
Generative AI (GenAI) is a subset of AI that focuses on creating new, original content. It involves training and deploying AI models to generate data such as images, text, or audio that closely resemble examples from the training dataset.
GenAI algorithms use advanced techniques like deep learning and neural networks to produce realistic and coherent outputs that enable applications like image synthesis, text generation, and even creative artwork.
Types of GenAI
The four primary types of generative AI models are: large language models (LLM), diffusers, audio and video generation, and code generation. The type of model used depends on the task to be accomplished.
LLMs
With the ability to generate text, summarize and translate content, respond to questions, engage in conversations, and perform complex tasks such as solving math problems or reasoning, LLMs have the potential to benefit society at scale.
GenAI Essentials: LLM Inference
Llama* 2 Fine-Tuning with Low-Rank Adaptations (LoRA) on Intel® Gaudi®2 AI Accelerator
Image, Video, and Audio Generation
Image or video generation creates new content based on descriptive text or image input. Audio generation clones and creates voices, sounds, and music from text prompts or input audio. These techniques typically rely on diffusion or transformer models.
Text-to-Image Generation with Stable Diffusion* on Intel® Developer Cloud
Controllable Music Generation with MusicGen and OpenVINO™ Toolkit
Text-to-Video Generation with ZeroScope and OpenVINO Toolkit
Code Generation
GenAI can create or suggest new code snippets using natural language or text prompts. This technology can also translate code from one programming language to another and efficiently test and debug computer code.
Optimize Code Development with LLMs on the Intel Developer Cloud
Retrieval-Augmented Generation (RAG)
RAG supplements pretrained models with up-to-date, proprietary, and confidential data from vector databases during inference. This simplifies customization and updating, and enables attribution of generated information to its source.
Use Cases
GenAI has the potential to revolutionize creative industries, enhance content generation processes, and drive innovation across industries and applications.
Creative Content Generation
Create new and compelling content such as images, videos, music, and text for entertainment, advertising, and design.
Data Augmentation
Generate synthetic data to augment training datasets for machine learning and deep learning models, or help improve model performance and generalization.
Simulation and Gaming
Construct virtual environments, characters, and scenarios that enhance realism and interactivity in simulation and gaming applications.
Healthcare and Medicine
Produce synthetic medical images or data that can be used to assist in medical research, diagnosis, and treatment planning.
Natural Language Processing (NLP)
Build natural language generation, dialogue systems, and text synthesis for chatbots, language translation, and content summarization applications.
Personalization and Recommendation Systems
Deliver personalized recommendations, advertisements, or product suggestions based on user preference and behavior.
Design Visualization
Render images of design ideas for a customer's home or office or of clothing options shown on a customer's avatar.
Materials Science and Drug Discovery
Identify compounds that solve multiple objectives with an understanding of their underlying complex physical and chemical relationships.
HoneyBee: A State-of-the-Art Language Model for Materials Science
How GenAI Works
GenAI is made possible by enormous datasets that teach AI models how to respond to user-provided prompts. These models find commonalities between similar types of data and use this information to generate new content. Data scientists and subject-matter experts can refine the algorithm’s learning by reviewing its outputs and associated prompts.
GenAI solutions take advantage of LLMs that use deep neural networks to process and generate unique text. These models are trained on large amounts of text data and are designed to deliver meaningful outputs. LLMs rely on transformer models to process input sequences in a parallel fashion, which improves performance and speed compared to traditional neural networks.
AI Webinar Series
Join our new GenAI webinar series to learn about the latest trends and best practices in generative AI from industry leaders and practitioners.
Get Started with GenAI Using Intel® AI Software and Hardware
Intel offers an end-to-end software portfolio to support use cases across generative AI applications.
Hugging Face*
This is a popular platform for sharing AI models and datasets. Intel collaborates with Hugging Face* to:
- Facilitate generative AI and language AI training.
- Build state-of-the-art hardware and software acceleration to train, fine-tune, and predict using transformer models.
Intel® Distribution of OpenVINO™ Toolkit
Tap into generative AI with high-performance, deep learning inference with the new release of this toolkit. Focused on generative AI, version 2024.0 makes it easier to get projects into production and includes new features such as increased LLM coverage and more performance optimization choices for TensorFlow* and PyTorch*. By minimizing code changes, developers are empowered to focus on what’s important: building the next great AI application that can run anywhere.
AI Frameworks
All major open source frameworks used for GenAI development and inference have been optimized with contributions from Intel along with oneAPI libraries, providing optimal performance across Intel CPUs and GPUs. This ensures the fastest turnaround for GenAI training and inference on the Intel hardware available for your project.
AI Development Software
Intel offers a comprehensive portfolio of GenAI development software, including those for data preparation, training, inference, deployment, and scaling. All tools are built on the foundation of a standards-based, unified oneAPI programming model so they plug into the rapidly-growing GenAI ecosystem to deliver productive end-to-end pipelines.
Intel® Optimization for Horovod*
This optimization makes distributed deep learning workloads run faster and easier on Intel GPU devices.
Intel® Extension for Transformers*
This toolkit features a seamless user experience for model compression, advanced software optimizations, a unique compression-aware runtime, and optimized model packages, including Stable Diffusion*, GPT-J 6BM, and BLOOM 176B. NeuralChat is a customizable framework available under the Intel® Extension for Transformers* that lets you build chatbots in minutes. This framework offers a rich set of plug-ins and supports fine-tuning, optimization, and inference.
Intel® Extension for DeepSpeed*
DeepSpeed* is a deep learning optimization software suite that powers scale and speed for training and inference. Intel® Extension for DeepSpeed* is an extension that brings Intel GPU (XPU) support to DeepSpeed.
Recommended Resources
Intel® Tiber™ Developer Cloud
Accelerate AI development using Intel-optimized software on the latest Intel® Xeon™ Scalable processors and GPU compute.
Unlock GenAI with Ubiquitous Hardware and Open Software
Get detailed information on the concept of an "AI brain" and how to maximize its potential.
A Use-Case-Specific Approach to Generative AI
Learn how focusing on your use case can help ease getting started with generative AI.
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