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Following Anthropic, GDM has released some work from its alignment efforts. The main insightful piece here is using sparse autoencoders on Gemini Ultra. This is a substantial jump in interpretation size.
NVIDIA will power Japan's new quantum supercomputer, ABCI-Q, alongside Fujitsu, integrating 2,000 NVIDIA H100 AI GPUs and CUDA-Q platform for quantum-classical computing applications. The project aims to advance Japan's capabilities in quantum computing and AI. This collaboration is part of a broader technological partnership between NVIDIA and Japan.
Brave Search has revamped its answer engine to include AI-synthesized responses worldwide, leveraging large language models and trusted data sources. The upgrade enables automatic or manual AI-enhanced answers to user queries. Brave acknowledges potential impacts on web publishers and plans to monitor and address the effects of AI-generated content on site traffic.
Segmenting objects across videos is challenging due to temporal consistency. This work presents the combination of a powerful image segmentation model and optical flow which combined gives compelling performance on this task.
A fairly technical RL paper that shows the theoretical underpinning of reward models and base models in language.
While this tune performs worse than Hermes, it is likely due to the small dataset used. Otherwise, this is a great walkthrough on how to tune these models to improve desired performance on certain tasks.
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The newest Llama 3 models were trained on 15T tokens. This new dataset contains an extensively deduplicated corpus from common crawl and results in high quality models.
Mistral has released its tokenizer utilities, chat templates, and more basic tools which it uses internally.
Researchers have developed a way to expand the context window of embedding models up to 32,000 tokens without additional training using strategies like position interpolation.
This article introduces "reasoning tokens" in language models, which generate additional tokens designed to predict future tokens rather than the immediate next one, thereby enhancing the model's ability to think ahead. Experiments demonstrate significant improvements in prediction accuracy, suggesting a potential for more complex reasoning without explicit step-by-step training.
This article discusses the transformative potential and current limitations of generative AI like ChatGPT, noting that while it excels in tasks like coding and generating drafts, it struggles with complex tasks that require specific programming. It highlights the need for a vision that matches AI solutions with practical applications, emphasizing that identifying and integrating these into daily workflows remains a significant challenge.
A visual guide to Vision Transformers (ViTs), a class of deep learning models that have achieved state-of-the-art performance on image classification tasks.
The original Llama models had significantly more incorrect refusals. This brief blog shows some examples that previously failed.
Researchers have demonstrated that OpenAI's GPT-4 model can autonomously exploit security vulnerabilities detailed in CVE advisories with an 87% success rate, far outperforming other models and tools like vulnerability scanners.
50 vision/language datasets combined into a single format to allow for improved training of models.