Anthropic has launched Claude 2.1, an AI model that addresses common issues. With a 200,000-token context window, it can recall information from extensive documents, reducing the risk of incorrect responses. The model also allows the use of external tools, broadening its applications. System prompts enable users to set specific contexts for consistent responses. While there…

Large multimodal models like LLaVA, MiniGPT4, mPLUG-Owl, and Qwen-VL have made rapid progress in handling and analyzing various types of data. However, there are obstacles to overcome, such as dealing with complex scenarios and the need for higher-quality training data. In response, researchers from Huazhong University of Science and Technology and Kingsoft have developed a…

LEO is a generalized agent developed by researchers at the Beijing Institute for General Artificial Intelligence, CMU, Peking University, and Tsinghua University. It is trained in an LLM-based architecture and is capable of perceiving, reasoning, planning, and acting in complex 3D environments. LEO incorporates 3D vision-language alignment and action, and has demonstrated proficiency in tasks…

The article on Towards Data Science explains the usage and benefits of typing.Literal, which allows for the creation of literal types. It highlights the power and versatility of this feature.

This article provides a guide on how to effectively use the cloud for all stages of the data science workflow. It offers valuable insights for implementing cloud technology in data science projects.

Researchers at Microsoft have proposed a deep learning compiler called Permutation Invariant Transformation (PIT) to optimize models for dynamic sparsity. PIT leverages a mathematically proven property to consolidate sparsely located micro-tiles into dense tiles without changing computation results. The solution accelerates dynamic sparsity computation by up to 5.9 times compared to state-of-the-art compilers and offers…

Researchers from McMaster University and FAIR Meta have developed a new machine learning technique called orbital-free density functional theory (OF-DFT) for accurately replicating electronic density in chemical systems. The method utilizes a normalizing flow ansatz to optimize the total energy function and solve complex problems. This approach shows promise for accurately describing electronic density and…

Lookahead decoding is a novel technique that improves the speed and efficiency of autoregressive decoding in large language models (LLMs) like GPT-4 and LLaMA. It eliminates the need for preliminary models and reduces the number of decoding steps by utilizing parallel processing. The technique has been shown to significantly decrease latency in LLM applications like…

UltraFastBERT, developed by researchers at ETH Zurich, is a modified version of BERT that achieves efficient language modeling with only 0.3% of its neurons during inference. The model utilizes fast feedforward networks (FFFs) and achieves significant speedups, with CPU and PyTorch implementations yielding 78x and 40x speedups respectively. The study suggests further acceleration through hybrid…