This article explores outlier detection algorithms in machine learning and their application to Major League Baseball's 2023 batting statistics. The four algorithms compared are Elliptic Envelope, Local Outlier Factor, One-Class Support Vector Machine with Stochastic Gradient Descent, and Isolation Forest. The goal is to gain insight into their behavior and limitations in order to determine whi...
This article explores the complexities of counting fish passing through large hydroelectric dams and the challenges of coordinating human-in-the-loop dataset production. It highlights the importance of complying with regulations set by the Federal Energy Regulatory Commission and the potential impact of hydroelectric dams on fish populations.
This article explains how to benchmark using the criterion crate and how to benchmark across different compiler settings, providing insights on performance effects and comparisons across CPUs. The range-set-blaze crate is used as an example to measure SIMD settings, optimization levels, and various input lengths.
This article explores the logic behind the fundamental algorithm used in gradient descent, focusing on the exponential moving average. It discusses the motivation behind the method, its formula, and provides a mathematical interpretation of its weight distribution.
Amazon announces the integration of Amazon DocumentDB with Amazon SageMaker Canvas, enabling users to build ML models without coding. This integration allows businesses to analyze unstructured data stored in Amazon DocumentDB and generate predictions without relying on data engineering and data science teams.
Boosting data ingestion in the range-set-blaze Crate by 7x by delegating calculations to little crabs. Rule 7: Use Criterion benchmarking to pick an algorithm and discover that LANES should (almost) always be 32 or 64.
Talent.com collaborates with AWS to develop a job recommendation engine using deep learning, processing 5 million daily records in less than 1 hour. The system includes feature engineering, deep learning model architecture design, hyperparameter optimization, and model evaluation, all run using Python.
Amazon Comprehend offers pre-trained and custom APIs for natural-language processing. They have developed a pre-labeling tool that automatically annotates documents using existing tabular entity data, reducing the manual work needed to train accurate custom entity recognition models.
Text-to-image generation is a rapidly growing field of AI, with Stable Diffusion allowing users to create high-quality images in seconds. The use of Retrieval Augmented Generation (RAG) enhances prompts for Stable Diffusion models, enabling users to create their own AI assistant for prompt generation.
OpenAI's ChatGPT, a groundbreaking AI language model, sparked excitement with its impressive abilities, including excelling in exams and playing chess. However, skeptics argue that true intelligence should not be confused with memorization, leading to scientific studies exploring the distinction and making the case against AGI.
ICL, a multinational manufacturing and mining corporation, developed in-house capabilities using machine learning and computer vision to automatically monitor their mining equipment. With support from the AWS Prototyping program, they were able to build a framework on AWS using Amazon SageMaker to extract vision from 30 cameras, with the potential to scale to thousands.
Dive into the world of artificial intelligence â build a deep reinforcement learning gym from scratch. Gain hands-on experience and develop your own gym to train an agent to solve a simple problem, setting the foundation for more complex environments and systems.
Amazon SageMaker Studio now offers a fully managed Code Editor based on Code-OSS, along with JupyterLab and RStudio, allowing ML developers to customize and scale their IDEs using flexible workspaces called Spaces. These Spaces provide persistent storage and runtime configurations, improving workflow efficiency and allowing for seamless integration of generative AI tools.
Dropbox faces backlash after enabling a default setting that shares user data with OpenAI for AI-powered search, but assures data is only shared when actively used and is deleted within 30 days. CEO Drew Houston apologizes for customer confusion and emphasizes that no customer data is automatically sent to third-party AI services.
This article explores the importance of classical computation in the context of artificial intelligence, highlighting its provable correctness, strong generalization, and interpretability compared to the limitations of deep neural networks. It argues that developing AI systems with these classical computation skills is crucial for building generally-intelligent agents.