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AI News Weekly – Issue #425: 5 Notes From the Big A.I. Summit in Paris – Feb 13th 2025
ByWeb Dev
Welcome Interested in sponsorship opportunities? Join the AI conversation and transform your advertising strategy with AI weekly sponsorship aiweekly.co In the News 5 Notes From the Big A.I. Summit in Paris The article discusses the AI summit in Paris, where global leaders and tech executives are addressing AI regulations, optimism about AI’s potential, the impact…
This AI Paper from UT Austin and JPMorgan Chase Unveils a Novel Algorithm for Machine Unlearning in Image-to-Image Generative Models
In an era where digital privacy has become paramount, the ability of artificial intelligence (AI) systems to forget specific data upon request is not just a technical challenge but a societal imperative. The researchers have embarked on an innovative journey to tackle this issue, particularly within image-to-image (I2I) generative models. These models, known for their…
AI News Weekly – Issue #406: Sam Altman : ‘Superintelligent’ AI Is Only a Few Thousand Days Away – Oct 3rd 2024
Powered by getessentialspro.com Welcome Interested in sponsorship opportunities? Join the AI conversation and transform your advertising strategy with AI weekly sponsorship aiweekly.co In the News Sam Altman : ‘Superintelligent’ AI Is Only a Few Thousand Days Away Altman predicts that with AI in the future, “We will be able to do things that would have…
AI News Weekly – Issue #412: Did OpenAI just spend more than $10 million on a URL? – Nov 7th 2024
Powered by name.com Welcome Interested in sponsorship opportunities? Join the AI conversation and transform your advertising strategy with AI weekly sponsorship aiweekly.co In the News Did OpenAI just spend more than $10 million on a URL? On Wednesday, OpenAI CEO Sam Altman posted a simple URL on X: chat.com. It automatically routes to ChatGPT. theverge.com…
Meet Graph-Mamba: A Novel Graph Model that Leverages State Space Models SSM for Efficient Data-Dependent Context Selection
Graph Transformers need help with scalability in graph sequence modeling due to high computational costs, and existing attention sparsification methods fail to adequately address data-dependent contexts. State space models (SSMs) like Mamba are effective and efficient in modeling long-range dependencies in sequential data, but adapting them to non-sequential graph data is challenging. Many sequence models…
Researchers from China Propose Vision Mamba (Vim): A New Generic Vision Backbone With Bidirectional Mamba Blocks
Many people are now interested in the state space model (SSM) because of how recent research has advanced. Modern SSMs, which derive from the classic state space model, benefit from concurrent training and excel at capturing long-range dependencies. Process sequence data across many activities and modalities using SSM-based methods like linear state-space layers (LSSL), structured…
