Assessing the effectiveness of Large Language Model (LLM) compression techniques is a vital challenge in AI. Traditional compression methods like quantization look to optimize LLM efficiency by reducing computational overhead and latency. But, the conventional accuracy metrics used in evaluations often overlook subtle changes in model behavior, including the occurrence of "flips" where right answers…
Large language models (LLMs) can revolutionize human-computer interaction but struggle with complex reasoning tasks, a situation prompting the need for a more streamlined and powerful approach. Current LLM-based agents perform well in straightforward scenarios but struggle with complex situations, emphasizing the need for improving these agents to tackle an array of intricate problems.
Researchers from Baichuan…
Groq, in partnership with Glaive, has recently introduced two state-of-the-art AI models for tool use: Llama-3-Groq-70B-Tool-Use and Llama-3-Groq-8B-Tool-Use. By outperforming all previous models, these innovations have achieved over 90% accuracy on the Berkeley Function Calling Leaderboard (BFCL) and are now open-sourced and available on GroqCloud Developer Hub and Hugging Face. The models leveraged ethically generated…
Sign language research is aimed at improving technology to better understand and interpret sign languages used by Deaf and hard-of-hearing communities globally. This involves creating extensive datasets, innovative machine-learning models, and refining tools for translation and identification for numerous applications. However, due to the lack of standardized written form for sign languages, there is a…
The Mistral AI team, together with NVIDIA, has launched Mistral NeMo, a state-of-the-art 12-billion parameter artificial intelligence model. Released under the Apache 2.0 license, this high-performance multilingual model can manage a context window of up to 128,000 tokens. The considerable context length is a significant evolution, allowing the model to process and understand massive amounts…
OpenAI has released its most cost-efficient miniature AI model, GPT-4o Mini, which is set to expand the scope of AI applications due to its affordable price and powerful capabilities. This model is substantially more cost-effective compared to its predecessors, such as GPT-3.5 Turbo, and is priced at 15 cents per million input tokens and 60…
Companies that build large language models, like those used in AI chatbots, routinely safeguard their systems using a process known as red-teaming. This involves human testers generating prompts designed to trigger unsafe or toxic responses from the bot, thus enabling creators to understand potential weaknesses and vulnerabilities. Despite the merits of this procedure, it often…
In the field of biomedicine, segmentation plays a crucial role in identifying and highlighting essential structures in medical images, such as organs or cells. In recent times, artificial intelligence (AI) models have shown promise in aiding clinicians by identifying pixels that may indicate disease or anomalies. However, there is a consensus that this method is…
Researchers from the Massachusetts Institute of Technology (MIT) are using machine learning to explore the concept of short-range order (SRO) in metallic alloys at atomic levels. The team believes that understanding SRO is key to creating high-performance alloys with unique properties but this has been a challenging area to explore. High-entropy alloys are of particular…
The Short-Range Order (SRO), the arrangement of atoms over small distances, plays a crucial role in materials’ properties, yet it has been understudied in metallic alloys. However, recent attention has been drawn to this concept as it is a contributing step towards developing high-performing alloys known as high-entropy alloys. Understanding how atoms self-arrange can pose…
Large language models (LLMs) like GPT-3 and Llama-2, encompassing billions of parameters, have dramatically advanced our capability to understand and generate human language. However, the considerable computational resources required to train and deploy these models presents a significant challenge, especially in resource-limited circumstances. The primary issue associated with the deployment of LLMs is their enormity,…
Spatiotemporal prediction, a significant focus of research in computer vision and artificial intelligence, holds broad applications in areas such as weather forecasting, robotics, and autonomous vehicles. It uses past and present data to form models for predicting future states. However, the lack of standardized frameworks for comparing different network architectures has presented a significant challenge.…