A novel approach from the Allen Institute for AI enables data to be removed from an artificial intelligence model even after ...

The starting point of a FlexOlmo project is a so-called anchor AI model. Every organization that participates in the project ...

Training teaches the model to recognize patterns and make predictions based on data. Models learn from a labeled dataset and adjust internal parameters to maximize confidence in predictions.

Personally identifiable information has been found in DataComp CommonPool, one of the largest open-source data sets used to ...

1. Prepare the Data. The first step in training an AI model is preparing your data by collecting, cleaning, and preprocessing the information you will use to train the model.

When someone starts a new job, early training may involve shadowing a more experienced worker and observing what they do ...

Consider, for example, the need to expose an AI model to large amounts of data for training. When data may not yet exist or may lack comprehensiveness, synthetic data comes into the training equation.

Strict training data management is needed throughout the model-building process by controlling and mediating the influence of multiple stakeholders. The scope and definitions of the model need to ...

Ari Morcos, who’s worked in the AI industry for nearly a decade, wants to abstract away many of the data prep processes around AI model training — and he’s founded a startup to do just that.

Image recognition: Training data for an image recognition model might consist of thousands or millions of images labeled with the objects they contain (e.g., “cat,” “dog,” “car,” etc.).

While AI companies make efforts to anonymize your data, it’s something you may not feel comfortable with. In most cases, you can find a setting to turn the training off.