Cerebras initially filed to go public in September 2024 but ran into several regulatory delays due to foreign investments.Cerebras initially filed to go public in September 2024 but ran into several regulatory delays due to foreign investments.[#item_full_content]
Whoop Advanced Labs marries subscription blood tests with its fitness device monitoring for tech-powered wellness advice.Whoop Advanced Labs marries subscription blood tests with its fitness device monitoring for tech-powered wellness advice.[#item_full_content]
California Governor Gavin Newsom has signed into law groundbreaking legislation requiring the world’s largest artificial intelligence companies to publicly disclose their safety protocols and report critical incidents, state lawmakers announced Monday.California Governor Gavin Newsom has signed into law groundbreaking legislation requiring the world’s largest artificial intelligence companies to publicly disclose their safety protocols and report critical incidents, state lawmakers announced Monday.Internet[#item_full_content]
US startup Anthropic on Monday announced the launch of its new generative artificial intelligence model, Claude Sonnet 4.5, which it says is the world’s best for computer programming.US startup Anthropic on Monday announced the launch of its new generative artificial intelligence model, Claude Sonnet 4.5, which it says is the world’s best for computer programming.Machine learning & AI[#item_full_content]
The Y Combinator startup, raised a Series A led by Peak XV Partners.The Y Combinator startup, raised a Series A led by Peak XV Partners.[#item_full_content]
A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as variational autoencoders with infinitely many latent variables, reducing computational costs and preventing overfitting. By appropriately interrupting the training of the encoder, this approach enabled development of more efficient generative AI, with broad applicability beyond standard diffusion models.A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as variational autoencoders with infinitely many latent variables, reducing computational costs and preventing overfitting. By appropriately interrupting the training of the encoder, this approach enabled development of more efficient generative AI, with broad applicability beyond standard diffusion models.[#item_full_content]
A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as variational autoencoders with infinitely many latent variables, reducing computational costs and preventing overfitting. By appropriately interrupting the training of the encoder, this approach enabled development of more efficient generative AI, with broad applicability beyond standard diffusion models.A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as variational autoencoders with infinitely many latent variables, reducing computational costs and preventing overfitting. By appropriately interrupting the training of the encoder, this approach enabled development of more efficient generative AI, with broad applicability beyond standard diffusion models.Computer Sciences[#item_full_content]
Bar-Ilan University announced today that a team from its Department of Computer Science has developed a breakthrough in video processing that significantly simplifies the separation of foreground objects from their backgrounds, without the need for extensive training or optimization. The new method, called OmnimatteZero, was developed by Dr. Dvir Samuel and Prof. Gal Chechik, who also serves as a senior director of AI research at NVIDIA. The paper is published on the arXiv preprint server.Bar-Ilan University announced today that a team from its Department of Computer Science has developed a breakthrough in video processing that significantly simplifies the separation of foreground objects from their backgrounds, without the need for extensive training or optimization. The new method, called OmnimatteZero, was developed by Dr. Dvir Samuel and Prof. Gal Chechik, who also serves as a senior director of AI research at NVIDIA. The paper is published on the arXiv preprint server.[#item_full_content]
Bar-Ilan University announced today that a team from its Department of Computer Science has developed a breakthrough in video processing that significantly simplifies the separation of foreground objects from their backgrounds, without the need for extensive training or optimization. The new method, called OmnimatteZero, was developed by Dr. Dvir Samuel and Prof. Gal Chechik, who also serves as a senior director of AI research at NVIDIA. The paper is published on the arXiv preprint server.Bar-Ilan University announced today that a team from its Department of Computer Science has developed a breakthrough in video processing that significantly simplifies the separation of foreground objects from their backgrounds, without the need for extensive training or optimization. The new method, called OmnimatteZero, was developed by Dr. Dvir Samuel and Prof. Gal Chechik, who also serves as a senior director of AI research at NVIDIA. The paper is published on the arXiv preprint server.Computer Sciences[#item_full_content]
Lithium-ion batteries power most electronics, but they have limited energy density—they can store only a certain amount of energy per mass or volume of the battery.Lithium-ion batteries power most electronics, but they have limited energy density—they can store only a certain amount of energy per mass or volume of the battery.Energy & Green Tech[#item_full_content]
