Advancements in large language models (LLMs) have greatly elevated natural language processing applications by delivering exceptional results in tasks like translation, question answering, and text summarization. However, LLMs grapple with a significant challenge, which is their slow inference speed that restricts their utility in real-time applications. This problem mainly arises due to memory bandwidth bottlenecks…
As the AI technology landscape advances, free online platforms to test large language models (LLMs) are proliferating. These 'playgrounds' offer developers, researchers, and enthusiasts a valuable resource to experiment with various models without needing extensive setup or investment.
LLMs, the cornerstone of contemporary AI applications, can be complex and resource-intensive, making them often inaccessible for individual…
In the field of artificial intelligence, the evaluation of Large Language Models (LLMs) poses significant challenges; particularly with regard to data adequacy and the quality of a model’s free-text output. One common solution is to use a singular large LLM, like GPT-4, to evaluate the results of other LLMs. However, this methodology has drawbacks, including…
Edge Artificial Intelligence (Edge AI) is a novel approach to implementing AI algorithms and models on local devices, such as sensors or IoT devices at the network's edge. The technology permits immediate data processing and analysis, reducing the reliance on cloud infrastructure. As a result, devices can make intelligent decisions autonomously and quickly, eliminating the…
In an era dominated by data-driven decision-making, businesses, researchers, and developers constantly require specific information from various online sources. This information, used for tasks like analyzing trends and monitoring competitors, is traditionally collected using web scraping tools. The trouble is that these tools require a sound understanding of programming and web technologies, can deliver errors,…
Large Language Models (LLMs) represent a significant advancement across several application domains, delivering remarkable results in a variety of tasks. Despite these benefits, the massive size of LLMs renders substantial computational costs, making them challenging to adapt to specific downstream tasks, particularly on hardware systems with limited computational capabilities.
With billions of parameters, these models…
In the age of rapidly growing data volume, charts have become vital tools for visualizing data in diverse fields ranging from business to academia. As a result, the need for automated chart comprehension has become increasingly important and received significant attention. While advancements in Multimodal Large Language Models (MLLMs) have shown promise in understanding images…
Generative artificial intelligence's (AI) ability to create new text, images, videos, and other media represents a huge technological advancement. However, there's a downside: generative AI may unwittingly infrive on copyrights by using existing creative works as raw material without the original author's consent. This poses serious economic and legal challenges for content creators and creative…
Large language models (LLMs) are increasingly in use, which is leading to new cybersecurity risks. The risks stem from their main characteristics: enhanced capability for code creation, deployment for real-time code generation, automated execution within code interpreters, and integration into applications handling unprotected data. It brings about the need for a strong approach to cybersecurity…
Artificial Intelligence (AI) is significantly transforming the healthcare industry, addressing challenges in areas such as diagnostics and treatment planning. Large Language Models (LLMs) are emerging as a revolutionary tool in this sector, capable of deciphering and understanding complex health data. However, the intricate nature of medical data and the need for accuracy and efficiency in…