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A visual-language model called REVEAL, which utilizes a multi-source multi-modal "memory" to answer knowledge-intensive queries, overcoming the limitations of existing retrieval-augmented models and reducing the need for massive amounts of training data and parameter size.

Highlights of the new features and updates in Git 2.41

Enhancing mathematical cognition through guided processes

MLCommons is democratizing data with public datasets, focusing on the importance of data in machine learning and the need for better tools and standards for creating and improving datasets.

Implementing lightweight background jobs for a Python web application using Fly Machines

Algorithm for determining when a machine should follow a teacher or learn independently in uncertain real-world situations

A new tool facilitates the selection of the appropriate method to evaluate AI models, enabling users to interpret predictions accurately.

A remarkable journey from Laredo, Texas, to leading the MIT autonomous vehicle team and earning an MBA from MIT Sloan.

Training a model to improve mathematical reasoning by rewarding correct steps of reasoning instead of just the final answer, leading to better performance and aligning the model with human thinking.

Large sequence models for software development activities that use the process of software development as the source of training data, resulting in promising results for professional software developers and the potential for developing agents that can assist across the software development process.

A glimpse into the daily responsibilities and projects of a senior software engineer on the Photo Publishing Team at the New York Times.

Snowflake Snowpark enables organizations to take ML experiments into production by providing production-grade ML data pipelines, data preparation in Python, and leveraging Scikit-Learn for feature engineering and ML model training in Snowflake.