Artificial Intelligence
The text provides a comprehensive overview of Gaussian splatting, a new trend in 3D representation. It discusses its representation of 3D scenes using 3D points and Gaussian functions, its image formation model & rendering, optimization, and limitations. The method is compared to NeRF and highlighted for its speed, simplicity, and interpretability. For more information, please…
Researchers from Northeastern University, Hangzhou Dianzi University, Stability AI, and Google Research have introduced HOI-Diff, a novel solution for generating realistic 3D human-object interactions guided by textual prompts. It utilizes a modular design and innovative correction mechanisms, showcasing superior performance compared to conventional methods. The approach’s effectiveness is demonstrated in diverse interactions and objects, positioning…
Generative Large Language Models (LLMs) have shown outstanding performance in various tasks. An effective LLM inference system, PowerInfer, designed for local deployments using a single consumer-grade GPU, significantly boosts LLM inference speed, achieving up to 11.69 times faster performance than the current system. This introduces a potential solution for advanced language model execution on desktop…
The text discusses the utilization of modern data warehousing and machine learning models to predict user churn in online apps. It emphasizes the importance of retention as a business metric and the benefits of using machine learning for user churn prediction. The approach involves dataset preparation, SQL-based model training, and leveraging BigQuery ML for model…
ChatGPT for Data Analysis is a comprehensive tutorial on leveraging ChatGPT for data analysis. The AI tool acts as a junior data analyst by interpreting plain English queries and conducting complex data analysis. The tutorial illustrates using ChatGPT to analyze transaction data for a fitness company, providing valuable insights and visualizations.
The article discusses the importance of project prioritization in the analytics world. It emphasizes considering impact, risks, and time constraints to make better decisions. The analogy of being a venture capitalist in choosing where to invest time and energy in different projects is used to drive this point home.
Google’s software engineers, Dan Kondratyuk and David Ross, have developed VideoPoet, an advanced AI tool for video generation. It integrates various capabilities into a single large language model (LLM), allowing seamless and coherent video creation. VideoPoet excels in animating still images, editing videos, and generating longer videos while demonstrating impressive evaluation results.
Summary: The article provides a comprehensive tutorial on building a graph convolutional network (GCN) for molecular property prediction using PyTorch. It covers creating molecular graphs, developing the GCN model, and training the network. The tutorial discusses the need for graph neural networks in chemistry and physics and provides code snippets for implementation. It emphasizes the…
The text discusses using Python, MIDI, and Matplotlib to analyze music and help beginners find the right instrument to learn piano. It explores extracting musical notes from MIDI files, visualizing note distribution using Matplotlib, and understanding the range of keys needed for different music pieces. The tutorial aims to aid beginners in data science and…
Summary: The blog post “Inside GPT — II: The Core Mechanics of Prompt Engineering” explains the mechanics of prompt engineering in language models like GPT-2. It discusses the impact of prompt choice on text generation, explores decoding strategies like greedy search and beam search, and mentions the use of n-gram penalty to improve the coherence of generated…
The tutorial provides comprehensive guidance on an analytics use case, detailing the process of analyzing semi-structured data with Spark SQL and utilizing Docker to set up the environment. It covers data engineering, data retrieval from an API, storage in MinIO, data transformation using PySpark, and data analysis with Spark SQL. The tutorial offers practical insights…
Chevrolet dealership in Watsonville, California removed its sales chatbot after being tricked into offering steep discounts. Interactions revealed limitations in letting chatbots close deals, as users negotiated for deals including a 2020 Chevrolet Trax LT for $17,300 with extras, a VIP test drive, and more. The dealership has since addressed the chatbot issues.
Researchers from Genentech and Stanford University have developed an Iterative Perturb-seq Procedure leveraging machine learning for efficient design of perturbation experiments. The method facilitates the engineering of cells, sheds light on gene regulation, and predicts the results of perturbations. It also addresses the issue of active learning in a budget context for Perturb-seq data, demonstrating…
Deep Neural Networks (DNNs) are a potent form of artificial neural networks, proficient in modeling intricate patterns within data. Researchers at Cornell University, Sony Research, and Qualcomm delve into the challenge of enhancing operational efficiency in Machine Learning models for large-scale Big Data streams. They introduce a NAS framework to optimize early exits, aiming to…
The challenge of translating textual prompts into intricate 3D wire art has led to traditional methods focusing on geometric optimization. However, a research team has introduced DreamWire, utilizing differentiable 2D Bezier curve rendering and minimum spacing tree regularization to enhance multi-view wire art synthesis. This pioneering method empowers users to bring imaginative wire sculptures to…
Researchers at MIT have developed an innovative approach using deep learning to identify potential new antibiotics. The program was trained on extensive datasets to determine effective antibiotics without harming human cells, providing transparency in its decision-making. This method led to the discovery of novel families of molecules with potential antibacterial properties, offering hope in combating…
Google’s Gemini model represents a significant advancement in AI and ML, rivaling OpenAI’s GPT models in performance. However, detailed evaluation results are not widely available. A recent study by researchers from Carnegie Mellon University and BerriAI has delved into Gemini’s language production capabilities. The study compares Gemini and GPT models across various tasks, highlighting their…
Recent AI advancements have focused on optimizing large language models (LLMs) to address challenges like size, computational demands, and energy requirements. MIT researchers propose a novel technique called ‘contextual pruning’ to develop efficient Mini-GPTs tailored to specific domains. This approach aims to maintain performance while significantly reducing size and resource requirements, opening new possibilities for…
The LoRA approach presents a parameter-efficient method for fine-tuning large pre-trained models. By decomposing the update matrix during fine-tuning, LoRA effectively reduces computational overhead. The method involves representing the change in weights using lower-rank matrices, reducing trainable parameters and offering benefits like reduced memory usage and faster training. The approach has broad applicability across different…
Data science goes beyond math and programming, aiming to solve problems. To discover the right problem, data scientists should ask 5 crucial questions: “What problem are you trying to solve?” “Why…?” “What’s your dream outcome?” “What have you tried so far?” and “Why me?” Mastering these questions is essential for effective client communication and problem…