Clarifai, Inc.

Marshall Jones

Matthew Donald Zeiler

Daniel Kantor

Yanan Jian

Christopher David Fox

John Rogers

Keith Ito

In some embodiments, user-selectable/connectable model representations may be provided via a user interface to facilitate artificial intelligence development. The model representations may comprises first and second machine learning model (ML) representations corresponding to first and second ML models, and non-ML model representations corresponding to non-ML models. Based on user input indicating selection of the first and second ML model representations and a non-ML model representation corresponding to a non-ML model, at least a portion of a software application may be generated such that the software application comprises (i) an instance of the first ML model, an instance of the second ML model, and an instance of the non-ML model and (ii) an input/output data path between the instance of the first ML model and at least one other instance, the at least one other instance comprising the instance of the second ML model or the instance of the non-ML model.

Artificial intelligence development via user-selectable/connectable model representations

In some embodiments, reduction of computational resource usage related to image labeling and/or segmentation may be facilitated. In some embodiments, a collection of images may be used to train one or more prediction models. Based on a presentation of an image on a user interface, an indication of a target quantity of superpixels for the image may be obtained. The image may be provided to a first prediction model to cause the prediction model to predict a quantity of superpixels for the image. The target quantity of superpixels may be provided to the first model to update the first model's configurations based on (i) the predicted quantity and (ii) the target quantity. A set of superpixels may be generated for the image based on the target quantity, and segmentation information related to the superpixels set may be provided to a second prediction model to update the second model's configurations.

Model-based image labeling and/or segmentation

In certain implementations, a user request to add a new concept may be received. A set of media item recommendations may be caused to be loaded on a user interface for presentation to a user responsive to the user request to add the new concept. The media item recommendation set may include a set of recommendations loaded on an on-screen portion of the user interface and a set of recommendations loaded on an off-screen portion of the user interface. The on-screen user interface portion is visible to the user at a first time. The off-screen user interface portion is not being visible to the user at the first time. A user selection of one or more recommendations of the on-screen recommendation set is received. The off-screen recommendation set may be caused to be updated on the user interface during the presentation of the media item recommendation set based on the user recommendation selection.

Systems and methods for updating recommendations on a user interface in real-time based on user selection of recommendations provided via the user interface

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New York, NY, United States of America

Marshall Jones