Generative AI is a subset of machine learning powered by ultra-large ML models, including large language models (LLMs) and multi-modal models (e.g., text, images, video, and audio). Applications like ChatGPT and Stable Diffusion have captured everyone’s attention and imagination, and all that excitement is for good reason. Generative AI is poised to have a profound impact across industries, from health care and life sciences, media and entertainment, education, financial services, and more. We know that building with the right FMs and running Generative AI applications at scale on the most performant cloud infrastructure will be transformative for customers. With generative AI built-in, users will be able to have more natural and seamless interactions with applications and systems. Think of how we can unlock our mobile phones just by looking at them, without needing to know anything about the powerful ML models that make this feature possible.
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 Yakov Livshits approvals using FMs for financially underserved markets, especially in developing nations. Investment firms can use the power of FMs to provide personalized financial advice to their clients at low cost.
“FMs can perform so many more tasks because they contain many parameters that make them capable of learning complex concepts. And through their pre-training exposure to internet-scale data in various forms and myriad patterns, FMs learn to apply their knowledge within various contexts,” Swami highlighted. With the new EC2 P5 instances, customers like Anthropic, Cohere, Hugging Face, Pinterest, and Stability AI will be able to build and train the largest ML models at scale. The collaboration through additional generations of EC2 instances will help startups, enterprises, and researchers seamlessly scale to meet their ML needs. Rather, organizations will need to be able to choose the right model for the right job.
So why is this technology—which has been percolating for decades—seeing so much interest now? Simply put, AI has reached a tipping point thanks to the convergence of technological progress and an increased understanding of what it can accomplish. Couple that with the massive proliferation Yakov Livshits of data, the availability of highly scalable compute capacity, and the advancement of ML technologies over time, and the focus on generative AI is finally taking shape. Think back to when a new set of technologies or a tech-enabled gizmo completely grabbed your attention and imagination.
Now, the largest models are more than 500B parameters—a 1,600x increase in size in just a few years. The size and general-purpose nature of FMs make them different from traditional ML models, which typically perform specific tasks, like analyzing text for sentiment, classifying images, and forecasting trends. 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. At this stage, the generative AI developers will use the corresponding FM by calling the API as has been provided by the FM providers of fine-tuners.
Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
The models used for text generation can be Markov Chains, Recurrent Neural Networks (RNNs), and more recently, Transformers, which have revolutionized the field due to their extended attention span. Text generation has numerous applications in the realm of natural language processing, chatbots, and content creation. All the produced models and code automation are stored in a centralized tooling account using the capability of a model registry. The infrastructure code for all these accounts is versioned in a shared service account (advanced analytics governance account) that the platform team can abstract, templatize, maintain, and reuse for the onboarding to the MLOps platform of every new team.
Here at AWS, we believe the startup community will be the driving force moving these innovations forward. The AWS Generative AI Accelerator is designed to act as catalyst, helping some of the most promising companies in this space to take their ideas off the ground. With a program tailored to meet the needs of generative AI startups, the AWS Generative AI Accelerator will provide access to impactful AI models and tools, customized go-to-market strategies, machine learning stack optimization, and more. Selected startups will also have access to networking opportunities with industry luminaries, potential investors, and customers. In addition, the selected startups will receive up to $300,000 in AWS credits to build their products and services on our tech stack, as well as dedicated business and technical mentors matched based on industry vertical, market, and stage.
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Whatever customers are trying to do with FMs—running them, building them, customizing them—they need the most performant, cost-effective infrastructure that is purpose-built for ML. This ability to maximize performance and control costs by choosing the optimal ML infrastructure is why leading AI startups, like AI21 Labs, Anthropic, Cohere, Grammarly, Hugging Face, Runway, and Stability AI run on AWS. P5 instances are ideal for training and running inference for increasingly complex LLMs and computer vision models behind the most-demanding and compute-intensive generative AI applications, including question answering, code generation, video and image generation, speech recognition, and more.
After the evaluation results have been collected, we propose choosing a model based on several dimensions. In this role, Swami oversees all AWS Database, Analytics, and AI & Machine Learning services. His team’s mission is to help organizations put their data to work with a complete, end-to-end data solution to store, access, analyze, and visualize, and predict. The long-term focus for Lineaje is not on generative code snippets for DevSecOps teams to remediate issues in software — though this is in the works — but on assisting with the remediation of open source supply chain vulnerabilities, Hasan said.
