Category: FAANG

Generative AI is rapidly transforming the modern workplace, offering unprecedented capabilities that augment how we interact with text and data. At Amazon Web Services (AWS), we recognize that many of our customers rely on the familiar Microsoft Office suite of applications, including Word, Excel, and Outlook, as the backbone of their daily workflows. In this …

Read more “Integrate generative AI capabilities into Microsoft Office using Amazon Bedrock”

Software development teams are under constant pressure to deliver at an ever-increasing pace. As sponsors of the DORA research, we recently took a look at the adoption and impact of artificial intelligence on the software development lifecycle. The research shows that more than 75% of developers are relying on AI in their daily responsibilities. With …

Read more “A framework for adopting Gemini Code Assist and measuring its impact”

This post is cowritten with Abdullahi Olaoye, Akshit Arora and Eliuth Triana Isaza at NVIDIA. As enterprises continue to push the boundaries of generative AI, scalable and efficient model training frameworks are essential. The NVIDIA NeMo Framework provides a robust, end-to-end solution for developing, customizing, and deploying large-scale AI models, while Amazon SageMaker HyperPod delivers …

Read more “Running NVIDIA NeMo 2.0 Framework on Amazon SageMaker HyperPod”

The quest to develop new medical treatments has historically been a slow, arduous process, screening billions of molecular compounds across decade-long development cycles. The vast majority of therapeutic candidates do not even make it out of clinical trials.  Now, AI is poised to dramatically accelerate this timeline.  As part of our wide-ranging, cross-industry collaboration, NVIDIA …

Read more “Accelerating AI in healthcare using NVIDIA BioNeMo Framework and Blueprints on GKE”

Palantir’s Response to the White House Office of Science and Technology Policy (OSTP) on Developing an AI Action Plan The United States must act decisively to maintain its AI leadership position At Palantir, we see our work as a duty and privilege: to support the United States of America, its core national interests, and the civilization …

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Large language models (LLMs) have revolutionized the field of natural language processing, enabling machines to understand and generate human-like text with remarkable accuracy. However, despite their impressive language capabilities, LLMs are inherently limited by the data they were trained on. Their knowledge is static and confined to the information they were trained on, which becomes …

Read more “Intelligent healthcare assistants: Empowering stakeholders with personalized support and data-driven insights”

Generative AI is poised to revolutionize the gaming landscape. It will offer — and in many cases, already is offering — personalized NPCs, dynamic narratives, and tailored environments, thereby reshaping player experiences. It’s a concept increasingly known as living games. The recent unveiling of Genie 2: a large scale foundation world model created by Google …

Read more “Enhancing multiplayer games with gen AI and cloud gaming: a technical deep dive”

Computer use is a breakthrough capability from Anthropic that allows foundation models (FMs) to visually perceive and interpret digital interfaces. This capability enables Anthropic’s Claude models to identify what’s on a screen, understand the context of UI elements, and recognize actions that should be performed such as clicking buttons, typing text, scrolling, and navigating between …

Read more “Getting started with computer use in Amazon Bedrock Agents”

This post was co-written with Vishal Singh, Data Engineering Leader at Data & Analytics team of GoDaddy Generative AI solutions have the potential to transform businesses by boosting productivity and improving customer experiences, and using large language models (LLMs) in these solutions has become increasingly popular. However, inference of LLMs as single model invocations or …

Read more “How GoDaddy built a category generation system at scale with batch inference for Amazon Bedrock”