What’s next for AI in 2024

Researchers from Stanford University and UNC Chapel Hill have developed a method to enhance the factual accuracy of large language models (LLMs) without human labeling. They fine-tune the LLMs by leveraging innovations in natural language processing…

Challenges in Traditional Text-to-Speech (TTS) Systems Traditional text-to-speech systems face significant challenges, such as: Complex Models: Many require intricate elements like duration modeling and phoneme alignment. Slow Convergence: Previous models struggled with speed and robustness. Alignment…

Transforming Speech Recognition with Universal-2 Introduction to ASR Technology In recent years, Automatic Speech Recognition (ASR) technology has become essential in various industries, including healthcare and customer support. However, accurately transcribing speech in different languages, accents,…

Summary: A report by MacroPolo shows how China’s AI talent pool has grown, with more researchers staying in China. The US still leads in attracting talent, but China is catching up. The report also highlights the…

Introduction In today’s fast-changing digital world, the demand for accessible and efficient language models is clear. While traditional large-scale models have significantly improved natural language understanding and generation, they are often too expensive and complex for…

Practical Solutions and Value of Google’s New Whale Bioacoustics Model Overview Whale species have diverse vocalizations, making it challenging to classify them automatically. Google’s new model helps estimate population sizes, track changes, and aid conservation efforts.…

The latest research from Brown University reveals that using low-resource languages (LRL) like Zulu or Scots Gaelic can cause GPT-4, an AI model, to produce unsafe responses, despite its alignment guardrails. When prompted in these languages,…

Researchers from Duke University and the Air Force Research Laboratory have introduced a new approach called Policy Stitching (PS) to tackle challenges in using reinforcement learning (RL) for teaching robots new skills. PS enables the combination…

Generalist AI systems have made significant progress in computer vision and natural language processing, benefitting various applications. However, the lack of physical and spatial reasoning in these systems limits their full potential. Google DeepMind’s BootsTAP method…

GRIT, a new AI methodology developed by researchers, merges generative and embedding capabilities in language models, unifying diverse language tasks within a single, efficient framework. It eliminates the need for task-specific models, outperforming existing models and…

A growing interest exists in technology that can convert textual descriptions into lifelike videos by animating images. Existing methods focus on generating static images and subsequently animating them, but may require improvement for quality and consistency,…

Bioplanner, a recent research introduced by researchers from multiple institutions, addresses the challenge of automating the generation of accurate protocols for scientific experiments. It focuses on enhancing long-term planning abilities of language models, specifically targeting biology…

Understanding Instruction-Following Pruning (IFPruning) What are Large Language Models (LLMs)? LLMs are powerful tools used for tasks like language processing, math calculations, and programming. However, they need a lot of computing power, making them less efficient.…

A group of researchers from UC Berkeley, Stanford, and King Abdulaziz City for Science and Technology has proposed a programmatic framework called RULES to evaluate the rule-following ability of large language models (LLMs). RULES consists of…

Contextual AI’s RAG 2.0 introduces cutting-edge Contextual Language Models (CLMs) setting a new benchmark in AI performance. CLMs excel in understanding and generating human-like text, offering profound implications for businesses and the AI research community. However,…

Challenges in Visual Language Models (VLMs) Modern VLMs face difficulties with complex visual reasoning tasks, where simply understanding an image is not enough. Recent improvements in text-based reasoning have not been matched in the visual domain.…

Introduction to EvalPlanner The rapid growth of Large Language Models (LLMs) has enhanced their ability to create detailed responses, but evaluating these responses fairly and efficiently is still a challenge. Human evaluation is often too costly…

Optimizing Graph Neural Network Training with DiskGNN: A Leap Toward Efficient Large-Scale Learning Introduction Graph Neural Networks (GNNs) are essential for processing complex data from domains like e-commerce and social networks. However, as graph data scales,…