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.
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.
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.
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.
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.
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.
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.
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.
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.
Generative AI and large language models dominated enterprise trends this year, with companies like Amdocs, Dropbox, and SAP building customized applications using RAG and LLMs. Open-source pretrained models are set to revolutionize businesses' operational strategies, while off-the-shelf AI and microservices make it easier for developers to create complex applications.
NVIDIA celebrates milestone with 500 RTX games and applications, revolutionizing gaming graphics and performance. Ray tracing and DLSS technologies have transformed visual fidelity and boosted performance in titles like Cyberpunk 2077 and Minecraft RTX.
The article explores common data clustering techniques, with a focus on spectral clustering. Using k-means to compute cluster labels from eigenvectors is found to be the best approach, despite variations and complexities.
The article discusses the launch of ChatGPT and the rise in popularity of generative AI. It highlights the creation of a web UI called Chat Studio to interact with foundation models in Amazon SageMaker JumpStart, including Llama 2 and Stable Diffusion. This solution allows users to quickly experience conversational AI and enhance the user experience with media integration.
Large language models (LLMs) like GPT NeoX and Pythia are gaining popularity, with billions of parameters and impressive performance. Training these models on AWS Trainium is cost-effective and efficient, thanks to optimizations like rotational positional embedding (ROPE) and partial rotation techniques.
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.