Large language models (LLMs) are used in various applications, such as machine translation, summarization, and content creation. However, a significant challenge with LLMs is their tendency to produce hallucinations—statements that sound plausible but are not grounded in factual information. This issue affects the reliability of AI-generated content, especially in domains requiring high accuracy, such as… →

In a groundbreaking achievement, AI systems developed by Google DeepMind have attained a silver medal-level score in the 2024 International Mathematical Olympiad (IMO), a prestigious global competition for young mathematicians. The AI models, named AlphaProof and AlphaGeometry 2, successfully solved four out of six complex math problems, scoring 28 out of 42 points. This places… →

Databricks announced the public preview of the Mosaic AI Agent Framework and Agent Evaluation during the Data + AI Summit 2024. These innovative tools aim to assist developers in building and deploying high-quality Agentic and Retrieval Augmented Generation (RAG) applications on the Databricks Data Intelligence Platform. Challenges in Building High-Quality Generative AI Applications Creating a… →

The field of language models has seen remarkable progress, driven by transformers and scaling efforts. OpenAI’s GPT series demonstrated the power of increasing parameters and high-quality data. Innovations like Transformer-XL expanded context windows, while models such as Mistral, Falcon, Yi, DeepSeek, DBRX, and Gemini pushed capabilities further. Visual language models (VLMs) have also advanced rapidly.… →

Deep learning has demonstrated remarkable success across various scientific fields, showing its potential in numerous applications. These models often come with many parameters requiring extensive computational power for training and testing. Researchers have been exploring various methods to optimize these models, aiming to reduce their size without compromising performance. Sparsity in neural networks is one… →

In the ever-evolving landscape of artificial intelligence (AI), the challenge of creating systems that can effectively collaborate in dynamic environments is a significant one. Multi-agent reinforcement learning (MARL) has been a key focus, aiming to teach agents to interact and adapt in such settings. However, these methods often grapple with complexity and adaptability issues, particularly… →

In the domain of sequential decision-making, especially in robotics, agents often deal with continuous action spaces and high-dimensional observations. These difficulties result from making decisions across a broad range of potential actions like complex, continuous action spaces and evaluating enormous volumes of data. Advanced procedures are needed to process and act upon the information in… →

Large Language Models (LLMs) face deployment challenges due to latency issues caused by memory bandwidth constraints. Researchers use weight-only quantization to address this, compressing LLM parameters to lower precision. This approach improves latency and reduces GPU memory requirements. Implementing this effectively requires custom mixed-type matrix-multiply kernels that move, dequantize, and process weights efficiently. Existing kernels… →