Best Practices for Contact Centers for 2024

AI agents are evolving from backend automators to interactive, collaborative components in modern applications. The challenge lies in creating agents that not only respond to users but also guide workflows proactively. Developers often face difficulties in…

Slovakian startup CulturePulse is working with the UN to use AI to gain a better understanding of the Israeli-Palestinian conflict. The company uses large datasets and machine learning to build digital twins of audiences and believes…

Generative deep learning models have transformed NLP, CV, speech processing, and TTS. Large language models demonstrate versatility in NLP, while pre-trained models excel in CV tasks. Amazon AGI’s BASE TTS, trained on extensive speech data, improves…

Practical Solutions for Modeling Magnetic Hysteresis Challenges in AI for Magnetic Devices Accurately modeling magnetic hysteresis is crucial for optimizing the performance of electric machines and actuators. Traditional methods struggle to generalize to novel magnetic fields,…

Understanding Direct Q-Function Optimization (DQO) Aligning large language models (LLMs) with human preferences is crucial in AI research. Traditional reinforcement learning (RL) methods, like Proximal Policy Optimization (PPO), often require a lot of online sampling, leading…

This text emphasizes the importance of continuous learning and growth in one’s career. It introduces several articles that cover various technical topics, such as generative AI, principle component analysis, image classification, linear algebra, support vector machines,…

Growing Need for Fine-Tuning LLMs The demand for fine-tuning Large Language Models (LLMs) to keep them updated with new information is increasing. Companies like OpenAI and Google provide APIs for customizing LLMs, but their effectiveness for…

Advancing Large Language Models (LLMs) with Critic-CoT Framework Enhancing AI Reasoning and Self-Critique Capabilities for Improved Performance Artificial intelligence is rapidly progressing, focusing on improving reasoning capabilities in large language models (LLMs). To ensure AI systems…

Researchers have developed RoboHive, a platform for robot learning, to address the challenges in this field. RoboHive serves as a benchmarking and research tool, offering various learning paradigms and hardware integration. Its key features include a…

Practical Solutions for Energy-Efficient Large Language Model (LLM) Inference Enhancing Energy Efficiency Large Language Models (LLMs) require powerful GPUs to handle data quickly, but this consumes a lot of energy. To address this, DynamoLLM optimizes energy…

Understanding AI Chatbots and Their Human-Like Interactions AI chatbots simulate emotions and human-like conversations, leading users to believe they truly understand them. This can create significant risks, such as users over-relying on AI, sharing sensitive information,…

Chemists at MIT have developed a machine learning model that can predict transition states in chemical reactions. Traditional quantum methods take hours or days to calculate a single state, but this model only takes a few…

Transforming Natural Language Processing with SmolLM2 Recent advancements in large language models (LLMs) like GPT-4 and Meta’s LLaMA have changed how we handle natural language tasks. However, these large models have some drawbacks, especially regarding their…

Introduction to Sentiment Analysis In this tutorial, we will explore how to perform sentiment analysis on text data using IBM’s open-source Granite 3B model integrated with Hugging Face Transformers. Sentiment analysis is a crucial natural language…

The “LONG AGENT” approach revolutionizes text analysis by enabling language models to efficiently navigate lengthy documents with up to 128,000 tokens. Developed by a team at Fudan University, its multi-agent architecture allows granular analysis and has…

This study introduces the LAW framework, combining language, agent, and world models to enhance machine reasoning and planning. It addresses limitations in current language models by integrating human-like reasoning elements and real-world context. The framework demonstrates…

Recent developments in Multi-Modal (MM) pre-training have led to the creation of sophisticated MM-LLMs (MultiModal Large Language Models) by integrating Large Language Models (LLMs) with additional modalities. Models like GPT-4(Vision) and Gemini demonstrate remarkable capabilities in…

Practical AI Solutions for Data Platforms Introduction Data generation is at an all-time high, presenting both opportunities and challenges for businesses. Data platforms are essential for handling and analyzing the vast volume of data, enabling companies…

OpenAI, initially a non-profit, shifted to a for-profit structure in 2019, straying from its effective altruism mission. Effective altruism seeks to maximize positive impacts while long-termism focuses on reducing existential risks. OpenAI’s commercial expansion created a…