Newsweek interview with Yann LeCun, Meta's chief AI scientist, detailing his skepticism of current LLMs and his focus on Joint Embedding Predictive Architecture (JEPA) as the future of AI, emphasizing world modeling and planning capabilities.
Ryan speaks with Edo Liberty, Founder and CEO of Pinecone, about building vector databases, the power of embeddings, the evolution of RAG, and fine-tuning AI models.
This article examines the dual nature of Generative AI in cybersecurity, detailing how it can be exploited by cybercriminals and simultaneously used to enhance defenses. It covers the history of AI, the emergence of GenAI, potential threats, and mitigation strategies.
This article provides a beginner-friendly explanation of attention mechanisms and transformer models, covering sequence-to-sequence modeling, the limitations of RNNs, the concept of attention, and how transformers address these limitations with self-attention and parallelization.
This Space demonstrates a simple method for embedding text using a LLM (Large Language Model) via the Hugging Face Inference API. It showcases how to convert text into numerical vector representations, useful for semantic search and similarity comparisons.
Mistral Small 3.1 is an open-source multimodal AI model optimized for consumer hardware, offering strong performance in text and image processing, multilingual capabilities, and a balance between performance and accessibility. While excelling in many areas, it has limitations in long-context tasks and Middle Eastern language support.
NVIDIA DGX Spark is a desktop-friendly AI supercomputer powered by the NVIDIA GB10 Grace Blackwell Superchip, delivering 1000 AI TOPS of performance with 128GB of memory. It is designed for prototyping, fine-tuning, and inference of large AI models.
AlexNet, a groundbreaking neural network developed in 2012 by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, has been released in source code form by the Computer History Museum in collaboration with Google. This model significantly advanced the field of AI by demonstrating a massive leap in image recognition capabilities.
This study demonstrates that neural activity in the human brain aligns linearly with the internal contextual embeddings of speech and language within large language models (LLMs) as they process everyday conversations.
ByteDance Research has released DAPO (Dynamic Sampling Policy Optimization), an open-source reinforcement learning system for LLMs, aiming to improve reasoning abilities and address reproducibility issues. DAPO includes innovations like Clip-Higher, Dynamic Sampling, Token-level Policy Gradient Loss, and Overlong Reward Shaping, achieving a score of 50 on the AIME 2024 benchmark with the Qwen2.5-32B model.
