Evaluating the effectiveness of Large Language Model (LLM) compression techniques is a crucial challenge in AI. Compression methods like quantization aim to optimize LLM efficiency by reducing computational costs and latency. However, traditional evaluation practices focus primarily on accuracy metrics, which fail to capture changes in model behavior, such as the phenomenon of “flips” where…
For recruiters, finding the right candidates—whether they’re applying inward or outbound—is a laborious and time-consuming process. The results are longer hiring processes, lost chances, and less-than-ideal hiring choices. Meet Serra: an AI-powered candidate search engine that helps recruiters locate the right inbound and outbound applicants. If a recruiter is looking for top talent, Serra can…
Data engineering is crucial in today’s digital landscape as organizations increasingly rely on data-driven insights for decision-making. Learning data engineering ensures proficiency in designing robust data pipelines, optimizing data storage, and ensuring data quality. This skill is essential for efficiently managing and extracting value from large volumes of data, enabling businesses to stay competitive and…
Open-source software powers a vast array of technologies we use daily, from web browsers to operating systems, and creates a community of developers to promote innovations. Maintaining open-source projects requires repetitive tasks like bug triage and code review can consume a lot of time. Traditionally, open source software projects rely heavily on volunteer developers which…
Large Language Models (LLMs) have made significant strides in artificial intelligence, but their ability to process complex structured data, particularly graphs, remains challenging. In our interconnected world, a substantial portion of real-world data inherently possesses a graph structure, including the Web, e-commerce systems, and knowledge graphs. Many of these involve textual graphs, making them suitable…
Large language models (LLMs) are central to advancements in artificial intelligence, focusing on enhancing the models’ ability to follow detailed instructions. This area of research encompasses methods to improve the quality and complexity of datasets used for training LLMs, ultimately leading to more sophisticated and versatile AI systems. The importance of these improvements cannot be…
Spatiotemporal prediction is a critical area of research in computer vision and artificial intelligence. It leverages historical data to predict future events. This technology has significant implications across various fields, such as meteorology, robotics, and autonomous vehicles. It aims to develop accurate models to forecast future states from past and present data, impacting applications from…
Large language models (LLMs) such as GPT-3 and Llama-2 have made significant strides in understanding and generating human language. These models boast billions of parameters, allowing them to perform complex tasks accurately. However, the substantial computational resources required for training and deploying these models present significant challenges, particularly in resource-limited environments. Addressing these challenges is…
In the rapidly developing field of audio synthesis, Nvidia has recently introduced BigVGAN v2. This neural vocoder breaks previous records for audio creation speed, quality, and adaptability by converting Mel spectrograms into high-fidelity waveforms. This team has thoroughly examined the main enhancements and ideas that set BigVGAN v2 apart. One of BigVGAN v2’s most notable…
Today, in a really interesting Reddit post, we saw someone comparing 9.9 vs 9.11 on various AI Chatbot Models (Llama 3 vs Claude vs Gpt 4o vs. Gemini). So, we tried asking these models, and we found these interesting findings We asked Llama 3:‘Is 9.11 larger than 9.9?’The answer was ‘Yes,’ and of course that’s…