Large visual collections, such as paintings, photographs, drawings, and other forms of visual media, offer valuable insights into historical events, social life, and artistic expression. These collections are key to understanding how societies produce and use images to shape cultural meaning over time. Yet they remain difficult to study due to their sheer size, often consisting of hundreds of thousands of items, and their intrinsic complexity, including diverse visual features, contents, contexts, and metadata structures.Large visual collections, such as paintings, photographs, drawings, and other forms of visual media, offer valuable insights into historical events, social life, and artistic expression. These collections are key to understanding how societies produce and use images to shape cultural meaning over time. Yet they remain difficult to study due to their sheer size, often consisting of hundreds of thousands of items, and their intrinsic complexity, including diverse visual features, contents, contexts, and metadata structures.[#item_full_content]

The founder experience at TechCrunch All Stage: Built for people building what’s next

For a founder, time is the one resource you can’t raise. That’s why TechCrunch All Stage — happening July 15 in Boston’s SoWa Power Station — is designed to make every minute count. Whether you’re at the whiteboard sketching v1 of your product or figuring out how to lead a team of 50, TechCrunch All […]For a founder, time is the one resource you can’t raise. That’s why TechCrunch All Stage — happening July 15 in Boston’s SoWa Power Station — is designed to make every minute count. Whether you’re at the whiteboard sketching v1 of your product or figuring out how to lead a team of 50, TechCrunch All[#item_full_content]

Meta’s big AI bet and our not-so-hot-take on fintech IPOs

Meta just made a $14.3 billion bet on data-labeling company Scale AI, but it’s not a traditional takeover: Meta’s taking a 49% stake in the company and adding Scale’s co-founder Alexandr Wang to its team. The move signals Meta’s growing urgency to keep up in the AI race, even if its strategy for competing with […]Meta just made a $14.3 billion bet on data-labeling company Scale AI, but it’s not a traditional takeover: Meta’s taking a 49% stake in the company and adding Scale’s co-founder Alexandr Wang to its team. The move signals Meta’s growing urgency to keep up in the AI race, even if its strategy for competing with[#item_full_content]

A new explainable AI technique transparently classifies images without compromising accuracy. The method, developed at the University of Michigan, opens up AI for situations where understanding why a decision was made is just as important as the decision itself, like medical diagnostics.A new explainable AI technique transparently classifies images without compromising accuracy. The method, developed at the University of Michigan, opens up AI for situations where understanding why a decision was made is just as important as the decision itself, like medical diagnostics.[#item_full_content]

A new explainable AI technique transparently classifies images without compromising accuracy. The method, developed at the University of Michigan, opens up AI for situations where understanding why a decision was made is just as important as the decision itself, like medical diagnostics.A new explainable AI technique transparently classifies images without compromising accuracy. The method, developed at the University of Michigan, opens up AI for situations where understanding why a decision was made is just as important as the decision itself, like medical diagnostics.Computer Sciences[#item_full_content]

New research from Rensselaer Polytechnic Institute (RPI) could help shape the future of artificial intelligence by making AI systems less resource-intensive, higher performing, and designed to emulate the human brain. The research was published in Patterns, titled “Dimensionality and dynamics for next-generation neural networks.”New research from Rensselaer Polytechnic Institute (RPI) could help shape the future of artificial intelligence by making AI systems less resource-intensive, higher performing, and designed to emulate the human brain. The research was published in Patterns, titled “Dimensionality and dynamics for next-generation neural networks.”[#item_full_content]

New research from Rensselaer Polytechnic Institute (RPI) could help shape the future of artificial intelligence by making AI systems less resource-intensive, higher performing, and designed to emulate the human brain. The research was published in Patterns, titled “Dimensionality and dynamics for next-generation neural networks.”New research from Rensselaer Polytechnic Institute (RPI) could help shape the future of artificial intelligence by making AI systems less resource-intensive, higher performing, and designed to emulate the human brain. The research was published in Patterns, titled “Dimensionality and dynamics for next-generation neural networks.”Computer Sciences[#item_full_content]

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