Time series forecasting is crucial for predicting future values, but faces challenges like seasonality and manual tuning. Amazon SageMaker AutoMLV2 simplifies the process with automation, from data preparation to model deployment.
Pre-training large language models (LLMs) with torchtitan library accelerates Meta Llama 3-like models, showcasing FSDP2 and FP8 support. Amazon SageMaker Model Training reduces time and cost, offering high-performing ML compute infrastructure.
Blake Montgomery takes over as the new writer of TechScape, discussing a middle school's tech ban and opting out of AI training. Stay updated with the latest tech news by signing up for the newsletter.
Researchers from MIT, CMU, and Lehigh collaborate on DARPA-funded METALS program to optimize multi-material structures for aerospace applications, including rocket engines. Project merges classical mechanics with AI design tech for compositionally graded alloys, enabling leap-ahead performance in structural components.
Implementing logistic regression using JavaScript to predict sex based on age, state, income, and political leaning. Training with batch gradient descent yields a model with 75% accuracy on test data.
Nearest Neighbor Regressor simplifies predicting continuous values using KD Trees and Ball Trees efficiently. A visual guide with code examples for beginners, focusing on construction and computation.
AI chatbots can steer people away from conspiracy beliefs, according to Thomas Costello's study. The power of artificial intelligence to promote truth is explored in this intriguing research.
Solving LinkedIn Queens game using backtracking & linear equations for quick solutions in less than 0.1 seconds. Linear equations provide a faster alternative to backtracking for solving the game's constraints.
FormulaFeatures is a tool for creating interpretable models by automatically engineering concise, highly predictive features. It aims to improve the accuracy and interpretability of models like decision trees, enhancing visibility into predictions.
Enhance RAG workflow by chunking data for optimal results with GPT-4 models. Short, focused inputs yield better responses, balancing performance and efficiency.
New AI agents excel in problem solving by reasoning and tool-driven decision making, showcasing impressive abilities beyond conversational tasks. Expressions of reasoning through evaluation and planning, as well as tool use, are key components in creating powerful AI solutions, with some models surpassing human accuracy on various benchmarks.
Dozens of neo-Nazis are shifting from Telegram to SimpleX Chat, backed by Twitter founder Jack Dorsey. Extremist groups fear Telegram's privacy policies, seeking anonymity on the secretive app.
Universities embrace generative AI for academic use, avoiding plagiarism. Student uses ChatGPT to anticipate interview questions accurately.
Agents simulating human actions paired with Amazon Titan models create personalized, multimodal fashion experiences. Customers can interact in natural language, receive outfit recommendations, and generate visual inspirations seamlessly.
Retrieval augmented generation (RAG) combines information retrieval & language generation for accurate responses. RAG techniques are diverse, tailored to specific challenges & use cases, evolving beyond a one-size-fits-all approach.