Research from ETH Zurich and Microsoft introduces SliceGPT, a post-training sparsification scheme for large language models (LLMs). It reduces the embedding dimension, leading to faster inference without extra code optimization. The method utilizes computational invariance in transformer networks and has been shown to outperform SparseGPT, offering significant speedups across various models and tasks.

A groundbreaking development in AI and machine learning presents intelligent agents that adapt and evolve by integrating past experiences into diverse tasks. The ICE strategy, developed by researchers, shifts agent development paradigms by enhancing task execution efficiency, reducing computational resources, and improving adaptability. This innovative approach holds great potential for the future of AI technology.

MambaTab is a novel machine learning method developed by researchers at the University of Kentucky to process tabular data. It leverages a structured state-space model to streamline data handling, demonstrating superior efficiency and scalability compared to existing models. MambaTab’s potential to simplify analytics and democratize advanced techniques marks a significant advancement in data analysis.

Researchers have developed a new, sleek 3D surface imaging system with simpler optics that can recognize faces just as effectively as existing smartphone systems. This advancement could replace cumbersome facial recognition technology currently in use for unlocking devices and accessing accounts.

Generative AI, particularly Large Language Models (LLMs), has shown remarkable progress in language processing tasks but has struggled to significantly impact molecule optimization in drug discovery. A new model, DrugAssist, developed by Tencent AI Lab and Hunan University, exhibits impressive human-interaction capabilities and achieved promising results in multi-property optimization, showcasing great potential for enhancing the…

New York University researchers trained an AI system using 60 hours of first-person video recordings from children aged 6 months to 2 years. The AI employed self-supervised learning to understand actions and changes like a child. The study’s findings suggest AI can efficiently learn from limited, targeted data, challenging conventional AI training methods.

Researchers work to optimize large language models (LLMs) like GPT-3, which demand substantial GPU memory. Existing quantization techniques have limitations, but a new system design, TC-FPx, and FP6-LLM provide a breakthrough. FP6-LLM significantly enhances LLM performance, allowing single-GPU inference of complex models with higher throughput, representing a major advancement in AI deployment. For more details,…

Auto-regressive decoding in large language models (LLMs) is time-consuming and costly. Speculative sampling methods aim to solve this issue by speeding up the process, with EAGLE being a notable new framework. It operates at the feature level and demonstrates faster and more accurate draft accuracy compared to other systems. EAGLE improves LLM throughput and can…

Nightshade, a tool from the University of Chicago, gained over 250,000 downloads within five days of its release. It combats unauthorized use of artwork by AI models by poisoning them at the pixel level, rendering them unable to replicate images accurately. The team is overwhelmed by its success, with potential future integration and cloud hosting.