The study presented at NeurIPS 2023’s Generative AI and Biology workshop focuses on converting 2D molecular structures into 3D conformations using a novel, scalable diffusion model on Riemannian Manifolds, achieving competitive results without assuming molecule structure.

Retraining customer churn prediction models is vital but challenging, especially when distinguishing the effects of interventions on customer behavior. Control groups, feedback surveys, and uplift modeling can address these biases, enabling more accurate predictions and focused retention strategies. Continual refinement and adaptation are key to future success.

A new integer-to-string conversion algorithm, called “LR printer,” outperforms the optimized standard algorithm by 25-38% for 32-bit and 40-58% for 64-bit integers. It’s beneficial for applications that generate large text files with numerous integers, affecting performance notably in data-heavy fields like Data Science and Machine Learning. The C++ implementation is available on GitHub.

The paper, presented at the NeurIPS 2023 ICBINB workshop, examines the use of pre-trained language models in text-to-image auto-regressive generation, finding them of limited utility and providing a twofold analysis related to cross-modality tokens.

Google researchers identified a method to retrieve parts of OpenAI’s ChatGPT training data by prompting repeated words, revealing sensitive information. Investing $200, they extracted over 10,000 examples. The findings raise security and privacy concerns amidst lawsuits accusing OpenAI of misusing private data for ChatGPT training.

Yann LeCun, Meta AI’s chief and deep learning pioneer, has expressed skepticism about the near-term development of artificial general intelligence (AGI) and quantum computing’s role in AI. He contrasts industry leaders by downplaying imminent AGI breakthroughs and doubts AI will match human intelligence soon. He also emphasizes the need for multimodal AI systems and democratizing…

The paper presents a study on using conditional generation from diffusion models for tasks in music production, such as audio continuation, inpainting, and regeneration, creating transitions between tracks, and transferring styles, by applying guidance during the sampling process at 44.1kHz stereo audio quality.

This article examines public transport systems in Budapest, Berlin, Stockholm, and Toronto using GTFS data and data science tools to analyze and visualize public transport patterns and insights for urban planning. The author addresses GTFS’s universality, noting city-specific manual validations, and explores topics like stop locations, departure times, spatial distributions, transport modes, and route shapes…

The Metal.jl Framework provides Julia users on macOS the ability to utilize the GPU for better performance in scientific computing and machine learning. It tackles macOS’s transition to M-series chips, offering solutions amidst compatibility challenges. Users can harness the GPU’s parallel processing via Metal.jl for tasks like matrix multiplication and machine learning with Flux, improving…