DPLM-2: A Multimodal Protein Language Model Integrating Sequence and Structural Data

Amazon SageMaker Studio offers a managed environment for developing, training, and deploying ML models, with the ability to run notebooks as scheduled jobs. SageMaker Pipelines now includes notebook jobs as a step, enabling data scientists to…

Practical Solutions for Memory Efficiency in Large Language Models Understanding the Challenge Large language models (LLMs) excel at complex language tasks but face memory issues due to storing contextual information. Efficient Memory Management Reduce memory usage…

Understanding LLM Inference Challenges Large Language Model (LLM) inference requires a lot of memory and computing power. To solve this, we use model parallelism strategies that share workloads across multiple GPUs. This helps reduce memory issues…

Researchers have introduced the GF-7 Building dataset, a comprehensive collection of high-resolution satellite images covering an extensive area of 573.17 km² in China. This dataset features 170,015 buildings, providing a balanced representation of urban and rural…

Anthropic researchers found that introducing backdoor vulnerabilities into AI models could make them unremovable. They experimented with triggers causing models to generate unsafe code, and found that reinforcement and fine-tuning did not make them safer. Adversarial…

Understanding AI and Machine Learning Artificial intelligence (AI) and machine learning (ML) focus on creating models that learn from data to perform tasks such as language processing, image recognition, and predictions. A key area of AI…

Introduction Artificial Intelligence (AI) is no longer a futuristic concept; it’s a reality that businesses are increasingly integrating into their operations. As companies face unprecedented challenges in a rapidly evolving market, leveraging AI can provide innovative…

Practical Solutions for Relational Table Learning with Large Language Models (LLMs) Challenges in Real-World Application of LLMs Large language models (LLMs) have shown remarkable text understanding and generation capabilities in artificial intelligence. However, their application to…

Generating Time Series Data: Importance and Challenges Generating time series data is crucial for various applications such as data augmentation and creating synthetic datasets. However, when dealing with multiple categories, this task becomes complex due to…

A new study led by Hugging Face indicates considerable energy and carbon footprint in AI tasks, with image generation as the most intensive, equivalent to driving 4.1 miles. Text generation is less intensive. Research suggests choosing…

The text discusses the challenges faced while running Principal Component Analysis (PCA) in PySpark to rank diamonds using machine learning. Despite the excellent documentation, the process of working with machine learning in Spark is not user-friendly.…

Microsoft has introduced Copilot, an AI assistant integrated across Windows 11, Microsoft 365, Edge, and Bing. It aims to provide support while maintaining privacy and security, using web context and intelligence with user data. Copilot offers…

Practical Solutions and Value of HARP in Multi-Agent Reinforcement Learning Introduction to MARL and Its Challenges Multi-agent reinforcement learning (MARL) focuses on systems where multiple agents collaborate to tackle tasks beyond individual capabilities. It is crucial…

Understanding Retrieval-Augmented Generation (RAG) Retrieval-augmented generation (RAG) is a significant improvement in how large language models (LLMs) perform tasks by using relevant external information. This method combines information retrieval with generative modeling, making it useful for…

OpenAI’s Guide to Building LLM Agents for Business Applications OpenAI’s Guide to Building LLM Agents for Business Applications Introduction OpenAI has released a comprehensive guide titled A Practical Guide to Building Agents, aimed at engineering and…

Getir, established in 2015, is a leading ultrafast grocery delivery company with a multinational presence. Utilizing Amazon SageMaker and AWS Batch, they reduced model training time by 90% and improved operational efficiency. Their data science team…

Impact of Large Language Models on Academic Writing Large language models (LLMs), such as ChatGPT, are increasingly used in scholarly literature, raising concerns about authenticity and originality. Detecting changes in writing style and vocabulary in biomedical…

The Skywork-13B family of large language models (LLMs) addresses the need for transparent and commercially available LLMs. Researchers at Kunlun Technology developed Skywork-13B-Base and Skywork-13BChat, providing detailed information about the training process and data composition. They…