Ghostbuster is a new method for detecting AI-generated text. It addresses the problem of large language models, like ChatGPT, being used for ghostwriting assignments and producing text with factual errors. Ghostbuster works by finding the probability of generating each token in a document under several weaker language models. It doesn’t need to know the specific…

The article discusses Self-RAG, a method that improves upon the standard Retrieval Augmented Generation (RAG) architecture. Self-RAG uses fine-tuned language models to determine the relevance of a context and generates special tokens accordingly. It outperforms other models in various tasks and does not change the underlying language model. However, there is room for improvement in…

The text discusses the similarities between time series and natural language processing (NLP) in the context of deep learning for sequential data. Both time series and text data have a sequential structure and exhibit long-range dependencies. The text explores different tasks for analyzing sequential data, such as time series forecasting and text generation. It also…

The article discusses insights from successful content creators on the topics of what content to post, which platforms to use, how often to post, and how to create a lot of content. Consistency and volume are emphasized, with recommendations for posting regularly on Twitter, LinkedIn, and Instagram. Repurposing long-form content is suggested as a strategy…

Researchers from Google Quantum AI have addressed a critical challenge in quantum computing by introducing a new quantum operation called Data Qubit Leakage Removal (DQLR). DQLR targets leakage states in data qubits, efficiently converting them into computational states and significantly reducing average leakage. The study also demonstrates improved Quantum Error Correction (QEC) execution. This breakthrough…

Researchers from the University of Oxford and Xi’an Jiaotong University have developed a machine learning model that can assist with atomic-scale simulation of phase-change materials (PCMs). The model can generate high-fidelity simulations, allowing for a better understanding of PCM-based devices. The researchers trained the model using quantum-mechanical data and demonstrated its speed and precision in…

Researchers from S-Lab at Nanyang Technological University, Singapore, have introduced OtterHD-8B, a versatile high-resolution multimodal model that can accurately interpret visual inputs of varying dimensions. The researchers also developed MagnifierBench, an evaluation framework for assessing the model’s ability to discern fine details and spatial relationships. OtterHD-8B demonstrates superior performance and adaptability in tasks such as…

Google DeepMind has developed an AI model called GraphCast that can predict weather conditions up to 10 days in advance, outperforming current models in accuracy and speed. The model accurately predicted the landfall of Hurricane Lee in Nova Scotia nine days in advance, compared to traditional models’ six days. GraphCast is based on historical weather…

The text explores recent research findings that uncover the inner workings of MoE (Mixture of Experts) models during training. For more details, refer to the full article on Towards Data Science.

The author shares their thoughts on the second week of the #30DayMapChallange, a daily social challenge where participants create thematic maps. The challenge focuses on designing maps and encourages creativity.