Alibaba International Launches Accio Work, an Enterprise AI Agent for Global Businesses - PR Newswire
<a href="https://news.google.com/rss/articles/CBMi2gFBVV95cUxOVEFOY0JkQlpCYXpyeHI2Nm1NYjhuY01ET0NBR1AydV84bnZfZUYzNWcxTzV2bGtVS1VKc1l2X05hdXZKVGxONUs1Xzk3OEExNXlmczFYRy13ZTN6OTVRYWllQ2VxalM2NXhGa2xCWmpSRFhRSjBTR2dCYVVKcHQ5NUhFNzRGZ3RQbzgyV2NGZHVCNkhfdXJuLVYtbVdzbzNzY2NLRUZtdzdhZGFKeGZkVXMxSDM1Wi1meGFYMUZ6aHJTUlRVaHBsTkFxaGNQMFZmTlBDLVhDSGV0dw?oc=5" target="_blank">Alibaba International Launches Accio Work, an Enterprise AI Agent for Global Businesses</a> <font color="#6f6f6f">PR Newswire</font>
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Baton
<p> Orchestrate your AI coding agents </p> <p> <a href="https://www.producthunt.com/products/baton-2?utm_campaign=producthunt-atom-posts-feed&utm_medium=rss-feed&utm_source=producthunt-atom-posts-feed">Discussion</a> | <a href="https://www.producthunt.com/r/p/1112791?app_id=339">Link</a> </p>
Handling Extreme Class Imbalance in Fraud Detection
<p><em>Originally published at <a href="https://riskernel.com/blog/extreme-class-imbalance-fraud-detection.html" rel="noopener noreferrer">Riskernel</a>.</em></p> <p>Fraud is one of the easiest machine learning problems to misunderstand because the target is so rare.</p> <p>In many portfolios, fraud is well below one percent of total events. That means a model can look excellent in offline evaluation while still creating a terrible operational outcome once it meets production traffic.</p> <p>If you are evaluating a fraud vendor or building your own stack, the first thing to understand is that this is not a standard classification problem. It is a rare-event decisioning problem with operational consequences.</p> <h2> Why the base rate changes everything </h2> <p>When fraud is extremely rare
GazeCLIP: Gaze-Guided CLIP with Adaptive-Enhanced Fine-Grained Language Prompt for Deepfake Attribution and Detection
arXiv:2603.29295v1 Announce Type: new Abstract: Current deepfake attribution or deepfake detection works tend to exhibit poor generalization to novel generative methods due to the limited exploration in visual modalities alone. They tend to assess the attribution or detection performance of models on unseen advanced generators, coarsely, and fail to consider the synergy of the two tasks. To this end, we propose a novel gaze-guided CLIP with adaptive-enhanced fine-grained language prompts for fine-grained deepfake attribution and detection (DFAD). Specifically, we conduct a novel and fine-grained benchmark to evaluate the DFAD performance of networks on novel generators like diffusion and flow models. Additionally, we introduce a gaze-aware model based on CLIP, which is devised to enhance t
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The Inside Story of the Greatest Deal Google Ever Made: Buying DeepMind - WSJ
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A Pragmatic View of AI Personhood - Google DeepMind
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Anthropic Dials Back AI Safety Commitments - WSJ
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Anthropic releases part of AI tool source code in 'error' - Indiana Gazette Online
<a href="https://news.google.com/rss/articles/CBMi3gFBVV95cUxNTXBZc0V2VjNHQWpqOGtXczVFcUZOenZGYXZnX00wVHh5SWxYMmJWaHNlXzNUSFRhbGhWU2x0cG5FNEdPeFdmTVlIV1pYU1I3ZWZiUFFfOHBXSHNzMkQ3ZXFpY19tUFhlNG4yQllkTldvbHVOWDNmQnJURFhjNDdpNm01NDVONFZaU2t3cUFCMWFhd0FSUTlQZTZOakJfREQxbThOWl90YnhjV0RWYUl4dkJCUUZVaHJEUkFkVzNLcEtsSHZydHpzRE0zcERIOG5YeS1IdjBKMVQ3LVV1cXc?oc=5" target="_blank">Anthropic releases part of AI tool source code in 'error'</a> <font color="#6f6f6f">Indiana Gazette Online</font>
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