Technical concepts explained in plain language
In this post, we discuss how Amazon Nova in Amazon Bedrock can be used to implement an AI-powered image recognition solution that automates the detection and validation of module components, significantly reducing manual verification efforts and improving accuracy.
Amazon Nova Sonic delivers real-time, human-like voice conversations through the bidirectional streaming interface. In this post, you learn how Amazon Nova Sonic can solve some of the challenges faced by cascaded approaches, simplify building voice AI agents, and provide natural conversational capabilities. We also provide guidance on when to choose each approach to help you make informed decisions for your voice AI projects.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Lots of influential people in tech last week were describing Moltbook, an online hangout populated by AI agents interacting with one another, as a glimpse into the future. It appeared to show…
This post walks through how agent-to-agent collaboration on Amazon Bedrock works in practice, using Amazon Nova 2 Lite for planning and Amazon Nova Act for browser interaction, to turn a fragile single-agent setup into a predictable multi-agent system.
For years, our newsroom has explored AI’s limitations and potential dangers, as well as its growing energy needs. And our reporters have looked closely at how generative tools are being used for tasks such as coding and running scientific experiments. But how is AI actually being used in fields like health care, climate tech, education,…
In this post, we demonstrate how to use the CLI and the SDK to create and manage SageMaker HyperPod clusters in your AWS account. We walk through a practical example and dive deeper into the user workflow and parameter choices.
In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs of two different LLMs using SageMaker training jobs.
Building upon our earlier work of marketing campaign image generation using Amazon Nova foundation models, in this post, we demonstrate how to enhance image generation by learning from previous marketing campaigns. We explore how to integrate Amazon Bedrock, AWS Lambda, and Amazon OpenSearch Serverless to create an advanced image generation system that uses reference campaigns to maintain brand guidelines, deliver consistent content, and enhance the effectiveness and efficiency of new campaign creation.
In this post, we illustrate how Clarus Care, a healthcare contact center solutions provider, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a generative AI-powered contact center prototype. This solution enables conversational interaction and multi-intent resolution through an automated voicebot and chat interface. It also incorporates a scalable service model to support growth, human transfer capabilities--when requested or for urgent cases--and an analytics pipeline for performance insights.
AI-powered content generation is now embedded in everyday tools like Adobe and Canva, with a slew of agencies and studios incorporating the technology into their workflows. Image models now deliver photorealistic results consistently, video models can generate long and coherent clips, and both can follow creative directions. Creators are increasingly running these workflows locally on Read Article
NVIDIA and Lilly are putting together “a blueprint for what is possible in the future of drug discovery,” NVIDIA founder and CEO Jensen Huang told attendees at a fireside chat Monday with Dave Ricks, chair and CEO of Lilly. The conversation — which took place during the annual J.P. Morgan Healthcare Conference in San Francisco Read Article
OpenAI introduces a new framework and evaluation suite for chain-of-thought monitorability, covering 13 evaluations across 24 environments. Our findings show that monitoring a model’s internal reasoning is far more effective than monitoring outputs alone, offering a promising path toward scalable control as AI systems grow more capable.
OpenAI and the U.S. Department of Energy have signed a memorandum of understanding to deepen collaboration on AI and advanced computing in support of scientific discovery. The agreement builds on ongoing work with national laboratories and helps establish a framework for applying AI to high-impact research across the DOE ecosystem.
OpenAI shares details about a Mixpanel security incident involving limited API analytics data. No API content, credentials, or payment details were exposed. Learn what happened and how we’re protecting users.
AI has been compared to various historical precedents: electricity, industrial revolution, etc., I think the strongest analogy is that of AI as a new computing paradigm because both are fundamentally about the automation of digital information processing. If you were to forecast the impact of computing on the job market in ~1980s, the most predictive feature of a task/job you'd look at is specifiability, i.e. are you just mechanically transforming information according to rote, easy to specify algorithm (examples being typing, bookkeeping, human calculators, etc.)? Back then, this was the class of programs that the computing capability of that era allowed us to write (by hand, manually). I call hand-written programs "Software 1.0". With AI now, we are able to write new programs that we could never hope to write by hand before. We do it by specifying objectives (e.g. classification accuracy, reward functions), and we search the program space via gradient descent to find neural networks that work well against that objective. This is my Software 2.0 blog post from a while ago. In this new programming paradigm then, the new most predictive feature to look at is verifiability. If a task/job is verifiable, then it is optimizable directly or via reinforcement learning, and a neural net can be trained to work extremely well. It's about to what extent an AI can "practice" something. The environment has to be: resettable (you can start a new attempt), efficient (a lot attempts can be made) and rewardable (there is some automated process to reward any specific attempt that was made). The more a task/job is verifiable, the more amenable it is to automation in the new programming paradigm. If it is not verifiable, it has to fall out from neural net magic of generalization fingers crossed, or via weaker means like imitation. This is what's driving the "jagged" frontier of progress in LLMs. Tasks that are verifiable progress rapidly, including possibly beyond the ability of top ex
OpenAI is exploring mechanistic interpretability to understand how neural networks reason. Our new sparse model approach could make AI systems more transparent and support safer, more reliable behavior.
Vineet Mehra, Chief Marketing Officer at Chime, shares how AI is reshaping marketing into an agent-driven discipline. He explains why CMOs who champion AI literacy and thoughtful adoption will lead in the new era of growth.
Meeting the demands of the Intelligence Age will require strategic investment in energy and infrastructure. OpenAI’s submission to the White House details how expanding capacity and workforce readiness can sustain U.S. leadership in AI and economic growth.
In this Executive Function series from OpenAI, discover how CNA is transforming its newsroom with AI. Editor-in-Chief Walter Fernandez shares insights on AI adoption, culture, and the future of journalism.
New research from the largest study of ChatGPT use shows how the tool creates economic value through both personal and professional use. Adoption is broadening beyond early users, closing gaps and making AI a part of everyday life.