​[[{“value”:” Anomaly detection has gained traction in various fields such as surveillance, medical analysis, and network security. Typically approached as a one-class classification problem, autoencoder (AE) models are commonly used. However, AEs tend to reconstruct anomalies too well, reducing discrimination between normal and abnormal data.… Read More »Enhancing Anomaly Detection with Adaptive Noise: A Pseudo Anomaly Approach Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Home Table of Contents Harnessing Power at the Edge: An Introduction to Local Large Language Models Introduction to Large Language Models (LLMs) What Are Large Language Models? Historical Context and Technological Evolution The Development of OpenAI’s Generative Pre-Trained Transformers Key Training Methodologies Broad Spectrum… Read More »Harnessing Power at the Edge: An Introduction to Local Large Language Models Aditya Sharma PyImageSearch

​[[{“value”:” The domain of large language model (LLM) quantization has garnered attention due to its potential to make powerful AI technologies more accessible, especially in environments where computational resources are scarce. By reducing the computational load required to run these models, quantization ensures that advanced… Read More »Intel Releases a Low-bit Quantized Open LLM Leaderboard for Evaluating Language Model Performance through 10 Key Benchmarks Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Vision Transformers (ViT) and Convolutional Neural Networks (CNN) have emerged as key players in image processing in the competitive landscape of machine learning technologies. Their development marks a significant epoch in the ongoing evolution of artificial intelligence. Let’s delve into the intricacies of both… Read More »Vision Transformers (ViTs) vs Convolutional Neural Networks (CNNs) in AI Image Processing Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Molecular representation learning is an essential field focusing on understanding and predicting molecular properties through advanced computational models. It plays a significant role in drug discovery and material science, providing insights by analyzing molecular structures. The fundamental challenge in molecular representation learning involves efficiently… Read More »This AI Research Introduces SubGDiff: Utilizing Diffusion Model to Improve Molecular Representation Learning Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Data centers need an upgraded dashboard to guide their journey to greater energy efficiency, one that shows progress running real-world applications. The formula for energy efficiency is simple: work done divided by energy used. Applying it to data centers calls for unpacking some details.… Read More »Dial It In: Data Centers Need New Metric for Energy Efficiency Jeremy Rodriguez – Archives Page 1 | NVIDIA Blog

​[[{“value”:” Language models, a subset of artificial intelligence, focus on interpreting and generating human-like text. These models are integral to various applications, ranging from automated chatbots to advanced predictive text and language translation services. The ongoing challenge in this field is enhancing these models’ efficiency… Read More »Alignment Lab AI Releases ‘Buzz Dataset’: The Largest Supervised Fine-Tuning Open-Sourced Dataset Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) like GPT-3 and ChatGPT exhibit exceptional capabilities in complex reasoning tasks such as mathematical problem-solving and code generation, far surpassing standard supervised machine learning techniques. The key to unlocking these advanced reasoning abilities lies in the chain of thought (CoT),… Read More »How ‘Chain of Thought’ Makes Transformers Smarter Vineet Kumar Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Autoregressive language models (ALMs) have proven their capability in machine translation, text generation, etc. However, these models pose challenges, including computational complexity and GPU memory usage. Despite great success in various applications, there is an urgent need to find a cost-effective way to serve… Read More »FastGen: Cutting GPU Memory Costs Without Compromising on LLM Quality Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Quantization, a method integral to computational linguistics, is essential for managing the vast computational demands of deploying large language models (LLMs). It simplifies data, thereby facilitating quicker computations and more efficient model performance. However, deploying LLMs is inherently complex due to their colossal size… Read More »QoQ and QServe: A New Frontier in Model Quantization Transforming Large Language Model Deployment Sana Hassan Artificial Intelligence Category – MarkTechPost