AI

A better way to turn 2D designs into 3D models for rapid prototyping

Engineers often use vision-language models to produce new designs, such as for airplane or automobile components. To simulate how those components will perform in realistic situations, they’ll use tried-and-true computer-aided design (CAD) software to generate 3D models of those designs, which they can put through virtual crash or durability tests.  Researchers from MIT and elsewhere

A better way to turn 2D designs into 3D models for rapid prototyping Read More »

3 Questions: Neural transparency and the future of AI design

Millions of people are now designing their own personalized artificial intelligence companions, yet most have little idea how those creations will actually behave. In a new paper, MIT Media Lab Assistant Professor Pat Pataranutaporn and his graduate student researchers Anthony Baez and Sheer Karny introduce “neural transparency,” a tool that lets everyday users glimpse inside

3 Questions: Neural transparency and the future of AI design Read More »

Built Technologies builds an AI-powered document intelligence solution on AWS to power agents across real estate finance

Document processing in real estate is complex and highly manual, impacting critical business decisions at scale, making it ripe for automation. Built Technologies, a real estate finance software provider, processes over $500B in real estate projects. The company deployed an AI-powered document processing engine on Amazon Bedrock and the AWS Intelligent Document Processing (IDP) Accelerator.

Built Technologies builds an AI-powered document intelligence solution on AWS to power agents across real estate finance Read More »

Agentic vision: Building visual intelligence with Amazon Bedrock and MCP servers

The integration of AI into real-world applications has long been hindered by a fundamental challenge: the disconnect between systems that can see, systems that can think, and systems that can act. Developers have struggled with complex integrations, managing multiple APIs, and creating custom solutions to bridge these gaps, resulting in inefficient, costly, and often fragile

Agentic vision: Building visual intelligence with Amazon Bedrock and MCP servers Read More »

Monitor Amazon SageMaker Pipelines cross-account with custom Amazon CloudWatch dashboards

Using Amazon SageMaker Pipelines, organizations can automate their machine learning (ML) workloads and distribute them over many AWS accounts and AWS Regions as part of their Machine Learning Operations (MLOps) strategy. However, monitoring SageMaker Pipelines can become complex when they are distributed across many AWS environments. Developers and operations engineers must manually switch between multiple

Monitor Amazon SageMaker Pipelines cross-account with custom Amazon CloudWatch dashboards Read More »