AI

A philosophy of work

What makes work valuable? Michal Masny, the NC Ethics of Technology Postdoctoral Fellow in the MIT Department of Philosophy, investigates the role work plays in our lives and its impact on our well-being.  Masny sees numerous benefits to work, beyond a paycheck. It’s a space for people to develop excellence at something, make a social contribution, […]

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Understanding Amazon Bedrock model lifecycle

Amazon Bedrock regularly releases new foundation model (FM) versions with better capabilities, accuracy, and safety. Understanding the model lifecycle is essential for effective planning and management of AI applications built on Amazon Bedrock. Before migrating your applications, you can test these models through the Amazon Bedrock console or API to evaluate their performance and compatibility.

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The future of managing agents at scale: AWS Agent Registry now in preview

Now available through Amazon Bedrock AgentCore, use AWS Agent Registry to discover, share, and reuse agents, tools, and agent skills across your organization. As enterprises scale to hundreds or thousands of agents, platform teams face three critical challenges: visibility (knowing what agents exist across the organization), control (governing who can publish and what becomes discoverable

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Embed a live AI browser agent in your React app with Amazon Bedrock AgentCore

When you build AI-powered applications, your users must understand and trust AI agents that navigate websites and interact with web content on their behalf. When an agent interacts with web content autonomously, your users require visibility into those actions to maintain confidence and control, which they don’t currently have. The Amazon Bedrock AgentCore Browser BrowserLiveView

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Introducing stateful MCP client capabilities on Amazon Bedrock AgentCore Runtime

Stateful MCP client capabilities on Amazon Bedrock AgentCore Runtime now enable interactive, multi-turn agent workflows that were previously impossible with stateless implementations. Developers building AI agents often struggle when their workflows must pause mid-execution to ask users for clarification, request large language model (LLM)-generated content, or provide real-time progress updates during long-running operations, stateless MCP

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New technique makes AI models leaner and faster while they’re still learning

Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational resources. Traditionally, obtaining a smaller, faster model either requires training a massive one first and then trimming it down, or training a small one from scratch and accepting weaker performance.  Researchers at MIT’s Computer Science and Artificial

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Customize Amazon Nova models with Amazon Bedrock fine-tuning

Today, we’re sharing how Amazon Bedrock makes it straightforward to customize Amazon Nova models for your specific business needs. As customers scale their AI deployments, they need models that reflect proprietary knowledge and workflows — whether that means maintaining a consistent brand voice in customer communications, handling complex industry-specific workflows or accurately classifying intents in

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