What is generative AI?
Generative AI is a type of artificial intelligence that can create new content and ideas, including conversations, stories, images, videos, and music. Like all artificial intelligence, generative AI is powered by machine learning models—very large models that are pre-trained on vast amounts of data and commonly referred to as Foundation Models (FMs). Apart from content creation, generative AI is also used to improve the quality of digital images, edit video, build prototypes quickly for manufacturing, augment data with synthetic datasets, and more.
It's important to note that at its core, an FM leverage the latest advances in machine learning. FMs are the result of the latest advancements in a technology that has been evolving for decades. A class of FMs, such as the GPT models, commonly referred to as large language models (LLMs) are specifically focused on language based tasks such as such as summarization, text generation (for example, creating a blog post), classification, open-ended Q&A, and information extraction. What makes large language models special is that they can perform so many more tasks because they contain such a large number of parameters that make them capable of learning advanced concepts. And through their pre-training exposure to internet-scale data in all its various forms and myriad of patterns, LLMs learn to apply their knowledge in a wide range of contexts.
While the capabilities and resulting possibilities of pre-trained FMs are amazing, their adaptability through customization to perform domain-specific functions makes it even more exciting to businesses. As a result, businesses can build highly differentiated applications with FMs using only a small fraction of the data and compute required to train a model from scratch.
Why is generative AI important?
Generative AI applications like ChatGPT have captured widespread attention and imagination because generative AI can help reinvent most customer experiences and applications, create new applications never seen before, and help customers reach new levels of productivity. According to Goldman Sachs, generative AI could drive a 7% (or almost $7 trillion) increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period.
What are the common applications of generative AI?
With generative AI, you can take advantage of machine learning for your business more quickly and apply it to a broader set of use cases. You can apply generative AI across all lines of business including engineering, marketing, customer service, finance, and sales. Code generation is one of the most promising applications for generative AI and, with Amazon CodeWhisperer, an AI coding companion, we are seeing great results on developer productivity. During preview, Amazon ran a productivity challenge, and participants who used Amazon CodeWhisperer were 27% more likely to complete tasks successfully and did so an average 57% faster than those who did not use CodeWhisperer.
Besides code generation, there are many applications where you can put generative AI to work to achieve a step change in customer experience, employee productivity, business efficiency, and creativity. You can use generative AI to improve customer experience through capabilities such as chatbots, virtual assistants, intelligent contact centers, personalization, and content moderation. You can boost your employees’ productivity with generative AI powered conversational search, content creation, and text summarization among others. You can improve business operations with intelligent document processing, maintenance assistants, quality control and visual inspection, and synthetic training data generation. Finally, you can use generative AI to turbocharge production of all types of creative content from art and music with text, animation, video and image generation.
How will generative AI affect industries?
While we see generative AI happening everywhere over time, there are certain industries that are poised to benefit quickly from generative AI.
Financial Services
Financial services companies can bring the power and cost-effectiveness of generative AI to serve their customers better while reducing costs. Financial institutions can use conversational bots powered by FMs to improve customer service by generating product recommendations and responses to customer inquiries. Lending institutions can fast-track loan approvals using FMs for financially underserved markets, especially in developing nations. Banks can quickly detect fraud in claims or credit card/loan. Investment firms can use the power of FMs to provide personalized financial advice to their clients at low cost.
Healthcare and Life Sciences
One of the most promising use cases of generative AI is to accelerate drug discovery and research by using models to create novel protein sequences with specific properties for design of antibodies, enzymes, and vaccines, as well as gene therapy. HCLS companies can also use FMs to design synthetic gene sequences for applications in synthetic biology and metabolic engineering, such as creating new biosynthetic pathways or optimizing gene expression for biomanufacturing purposes. Lastly, FMs can create synthetic patient and healthcare data, which can be useful for training AI models, simulating clinical trials, or studying rare diseases without access to large real-world datasets.
Automotive and Manufacturing
Automotive companies can use generative AI for a multitude of use cases, from engineering to in-vehicle experiences and customer service. Generative AI will help automotive companies optimize the design of mechanical parts to reduce drag in vehicle designs. Generative AI will also create new in-vehicle experiences, allowing for the design of personal assistants. Auto companies are using generative AI to deliver better customer service by providing quick responses to most common customer questions. New material, chip, and part designs can be created with generative AI to optimize manufacturing processes and drive down costs. Generative AI can also be used for synthetic data generation to test applications, especially for data not often included in testing datasets, such as defects or edge cases.
Media and Entertainment
From animations and scripts to full-length movies, generative AI can produce high-quality, novel content at a fraction of the cost and time it would traditionally take. Artists can complement and enhance their albums with AI-generated music to create whole new genres. Media organizations can use generative AI to improve their audience experiences by offering personalized content and ads to grow revenues. Gaming companies can use generative AI to create new games and allow players to build avatars.
Telecom
Early use cases of Generative AI and Telecom are focused on reinventing Customer Experience. Customer experience is defined by the cumulative interactions of subscribers across all touchpoints of the customer journey. For instance, Telecom organizations can apply generative AI to improve customer service with live human-like conversational agents, optimize network performance by analyzing network data to recommend fixes as well as reinvent customer relationships with hyper-personalized one-to-one sales assistants.
Energy
Generative AI is suitable for energy sector tasks that involve complex data analysis, pattern recognition, forecasting, and optimization. Energy organizations can improve customer service by analyzing customer data to identify usage patterns and develop targeted product offerings, energy efficiency programs or demand response initiatives. Generative AI can help with grid management, increase operational site safety and optimize energy production through reservoir simulation.
How AWS can help Generative AI?
Generative AI will have a profound impact across more areas and across industries. From services like Amazon Bedrock and Amazon SageMaker to custom silicon accelerators like Trainium and Inferentia and AI-enabled applications such as Amazon CodeWhisperer, AWS offers the breadth and depth of tools to build and use generative AI. Choose the services that are best suited to your approach and kickstart your generative AI journey right away:
- Deploy Amazon CodeWhisperer and achieve immediate developer productivity gains.
- Get started with FMs quickly through the Amazon Bedrock preview.
- Use Amazon SageMaker JumpStart to discover, explore, and deploy open source FMs such as OpenLLaMA, RedPajama, Mosiac MPT-7B, FLAN-T5/UL2, GPT-J-6B/Neox-20B, Bloom/BloomZ, and more.
- If you prefer to create your own FMs, use Amazon SageMaker, which provides managed infrastructure and tools to accelerate scalable, reliable, and secure model building, training, and deployment.
Next Steps on AWS
