Generative AI

AWS AI updates: Amazon Bedrock and 3 generative AI innovations

Amazon unleashes Gen AI for product descriptions

The large models that power generative AI applications—those foundation models—are built using a neural network architecture called “Transformer.” It arrived in AI circles around 2017, and it cuts down development process significantly. During this 8-hour deep dive, you will be introduced to the key techniques, services, and trends that will help you understand foundation models from the ground up. This means breaking down theory, mathematics, and abstract concepts combined with hands-on exercises to gain functional intuition for practical application. Throughout the course, we focus on a wide spectrum of progressively complex generative AI techniques, giving you a strong base to understand, design, and apply your own models for the best performance.

Generative AI updates from AWS Summit 2023 NYC – About Amazon

Generative AI updates from AWS Summit 2023 NYC.

Posted: Wed, 26 Jul 2023 07:00:00 GMT [source]

In the case of an unlabeled evaluation prompt catalog, there is an additional step for an HIL or LLM to review the results and provide a score and feedback (as we described earlier). The final outcome will be aggregated results that combine the scores of all the outputs (calculate the average precision or human rating) and allow the users to benchmark the quality of the models. The following is an example of two shortlists, one for proprietary models and one for open-source models. You might compile similar tables based on your specific needs to get a quick overview of the available options. Note that the performance and parameters of those models change rapidly and might be outdated by the time of reading, while other capabilities might be important for specific customers, such as the supported language.

generative AI innovations from AWS Summit New York 2023

The generative AI end-users interact with the generative AI applications front end via the internet (such as a web UI). On the other side, data labelers and editors need to preprocess the data without accessing the backend of the data lake or data mesh. Therefore, a web UI (website) with an editor is necessary for interacting securely with the data. The fine-tuners adapt an FM model based on a specific context to use it for their business purpose. That means that most of the time, the fine-tuners are also consumers required to support all the layers, as we described in the previous sections, including generative AI application development, data lake and data mesh, and MLOps.

If I wanted to do translation with a deep learning model, for example, I would access lots of specific data related to translation services to learn how to translate from Spanish to German. The model would only do the translation work, but it couldn’t, for example, go on to generate recipes for paella in German. It could translate a paella recipe from Spanish into German that already exists, but not create a new one. Video Generation involves deep learning methods such as GANs and Video Diffusion to generate new videos by predicting frames based on previous frames. Video Generation can be used in various fields, such as entertainment, sports analysis, and autonomous driving. Speech Generation can be used in text-to-speech conversion, virtual assistants, and voice cloning.

free and low-cost AWS courses that can help you use generative AI

This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services. LLMs have demonstrated remarkable capabilities in natural language understanding and generation, serving as foundation models that can be adapted to various domains and tasks. It reduces computation costs, reduces carbon footprints, and allows you to use state-of-the-art models without having to train one from scratch. 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.

  • Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
  • “It sounds easy. But depending on the programming language, there is some uncertainty when it comes to types.”
  • This prompt catalog is a central location for storing prompts to avoid replications, enable version control, and share prompts within the team to ensure consistency between different prompt testers in the different development stages, which we introduce in the next section.
  • In a nutshell, that means less human labor, more sophisticated data collection, and virtually zero potential for breach of privacy.
  • Instead, recent blog posts from two SBOM vendors warned about the security hazards of generative AI.

With Runway, the difficult tasks of composition, stylization, inpainting, motion tracking, and other processes are made easier and quicker for creators, allowing them to focus on more idea concepts and to deliver faster iterations. These tools also cut down production costs and lower the barrier for filmmakers—professionals and amateurs alike—to push the boundaries of movie making and let their imagination run free. Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. However, bringing such solutions and models to the business-as-usual operations is not an easy task. In this post, we discuss how to operationalize generative AI applications using MLOps principles leading to foundation model operations (FMOps).

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.

Enter, the CodeWhisperer

We’ll start with recapping foundation models, understanding where they come from, how they work, how they relate to generative AI, and what you can to do customize them. 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.

aws generative ai

A model can learn in the pre-training phase, for example, what a sunset is, what a beach looks like, and what the particular characteristics of a unicorn are. With a model designed to take text and generate an image, not only can I ask for images of sunsets, beaches, and unicorns, but I can have the model generate an image of a unicorn on the beach at sunset. And with relatively small amounts of labeled data (we call it “fine-tuning”), you can adapt the same foundation model for particular domains or industries. Using Transformer architecture, generative AI models can be pre-trained on massive amounts of unlabeled data of all kinds—text, images, audio, etc. There is no manual data preparation, and because of the massive amount of pre-training (basically learning), the models can be used out-of-the-box for a wide variety of generalized tasks.

Business operations will improve with intelligent document processing or quality controls built with generative AI. And customers will be able to use generative AI to turbocharge the production of all types of creative content. Based on this example, to perform evaluation, we need to provide the example prompts, which we store in the prompt catalog, and an evaluation labeled or unlabeled dataset based on our specific applications.

aws generative ai

Building powerful applications like CodeWhisperer is transformative for developers and all our customers. We have a lot more coming, and we are excited about what you will build with generative AI on AWS. Our mission is to make it possible for developers of all skill levels and for organizations of all sizes to innovate using generative AI. This is just the beginning of what we believe will be the next wave of ML powering new possibilities for you. We know generative AI is going to change the game for developers, and we want it to be useful to as many as possible.

“It sounds easy. But depending on the programming language, there is some uncertainty when it comes to types.” Lineaje software uses crawlers to collect up to 170 attributes on each software component Yakov Livshits listed in an SBOM, including open source libraries and dependencies, Hasan said. This naturally leads to an overwhelming number of vulnerabilities reported — thousands, in many cases.

aws generative ai

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