Abstract: A system for optimizing results of processed assets for provision to software applications based on determined sequences of operation is disclosed, the system having a cloud-based engine and a plurality of models that are each usable by the engine to provide artificial intelligence in connection with software applications. Multiple software extensions are executed by the engine in accordance with a respective model for at least one of the general-purpose software applications. Input data are processed as a function of at least one of the models and a set of the plurality of extensions, wherein a given sequence of executing the set of extensions on one of the inputs impacts results provided by the engine. The engine is configured by executing each of the extensions in different sequences to grade a respective degree of inference and selecting an optimum sequence, which is communicated to the general-purpose software application.

Abstract: 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.

Abstract: Methods and computer readable media for facilitating training of a recognition model. An embodiment includes generating media items based on information associated with a representation of a graphic, the information including content other than the graphic, content based on at least one transformation parameter set, and content comprising the graphic integrated with the other content, then using a recognition model to process the media items to generate predictions related to recognition of the graphic for the media items, the generated predictions including an indication of a predicted location of the graphic in a first media item.

Abstract: 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.

Abstract: A system for optimizing results of processed assets for provision to software applications based on determined sequences of operation is disclosed, the system having a cloud-based engine and a plurality of models that are each usable by the engine to provide artificial intelligence in connection with software applications. Multiple software extensions are executed by the engine in accordance with a respective model for at least one of the general-purpose software applications. Input data are processed as a function of at least one of the models and a set of the plurality of extensions, wherein a given sequence of executing the set of extensions on one of the inputs impacts results provided by the engine. The engine is configured by executing each of the extensions in different sequences to grade a respective degree of inference and selecting an optimum sequence, which is communicated to the general-purpose software application.

Abstract: In certain embodiments, training of a prediction model (e.g., recognition or other prediction model) may be facilitated via a training set based on one or more logos or other graphics. In some embodiments, graphics information associated with a logo or graphic (e.g., to be recognized via a recognition model) may be obtained. Media items (e.g., images, videos, etc.) may be generated based on the graphics information, where each of the media items includes (i) content other than the logo and (ii) a given representation of the logo integrated with the other content. In some embodiments, the media items may be processed via the recognition model to generate predictions (related to recognition of the logo or graphic for the media items). The recognition model may be updated based on (i) the generated predictions and (ii) corresponding reference indications (related to recognition of the logo for the media items).

Abstract: A system for optimizing results of processed assets for provision to software applications based on determined sequences of operation is disclosed, the system having a cloud-based engine and a plurality of models that are each usable by the engine to provide artificial intelligence in connection with software applications. Multiple software extensions are executed by the engine in accordance with a respective model for at least one of the general-purpose software applications. Input data are processed as a function of at least one of the models and a set of the plurality of extensions, wherein a given sequence of executing the set of extensions on one of the inputs impacts results provided by the engine. The engine is configured by executing each of the extensions in different sequences to grade a respective degree of inference and selecting an optimum sequence, which is communicated to the general-purpose software application.

Abstract: In certain embodiments, content items may be obtained, where each of the content items may include multiple data types. Machine learning models may be caused to be trained based on the content items to map data in a vector space by providing at least a first portion of each of the content items as input to at least one of the machine learning models and providing at least a second portion of each of the content items as input to at least another one of the machine learning models. A search request for results may be obtained, where the search request includes search parameters. One or more locations within the vector space may be predicted (e.g., by one or more of the machine learning models) based on the search parameters. Information (indicating content items mapped to or proximate the predicted locations) may be provided as a request response.

Abstract: In some embodiments, a service platform that facilitates artificial intelligence model and data collection and collection may be provided. Input/output information derived from machine learning models may be obtained via the service platform. The input/output information may indicate (i) first items provided as input to at least one model of the machine learning models, (ii) first prediction outputs derived from the at least one model's processing of the first items, (iii) second items provided as input to at least another model of the machine learning models, (iv) second prediction outputs derived from the at least one other model's processing of the second items, and (v) other inputs and outputs. The input/output information may be provided via the service platform to update a first machine learning model. The first machine learning model may be updated based on the input/output information being provided as input to the first machine learning model.

Abstract: Keyboard-based search of local and/or connected digital media items may be facilitated. A digital media item search interface may be presented in the same view as a messaging interface. The digital media item search interface may receive input such as from an on-screen keyboard to facilitate editing of user-provided search queries and submission of the user-provided search queries. The digital media item search interface may present digital media item tags relating to context information based on input received by the digital media item search query field. The digital media item search interface may present visual previews of local and/or connected digital media items corresponding to digital media item tags presented in the digital media item tag field. The digital media item search interface may receive user selections of individual digital media items to be communicated to one or more other users via the messaging interface.

Abstract: A previously trained classification model associated with the machine learning system is configured to process an input to generate i) a first prediction that represents a characteristic associated with the input, and ii) a representation of accuracy associated with the prediction. A retraining subsystem is configured to receive the input, the first prediction, and the representation of accuracy. The retraining subsystem processes the input to generate a prediction representing a characteristic. A sufficiency of certainty of the first prediction is determined based on at least the input, the first prediction, the measure of accuracy, and the second prediction. Based at least on the determined sufficiency the retraining subsystem causes the machine learning system to be automatically retrained, be retrained using the input with active learning or not retrained.

Abstract: In some embodiments, a service platform that facilitates artificial intelligence model and data collection and collection may be provided. Input/output information derived from machine learning models may be obtained via the service platform. The input/output information may indicate (i) first items provided as input to at least one model of the machine learning models, (ii) first prediction outputs derived from the at least one model's processing of the first items, (iii) second items provided as input to at least another model of the machine learning models, (iv) second prediction outputs derived from the at least one other model's processing of the second items, and (v) other inputs and outputs. The input/output information may be provided via the service platform to update a first machine learning model. The first machine learning model may be updated based on the input/output information being provided as input to the first machine learning model.

Abstract: Keyboard-based search of local and/or connected digital media items may be facilitated. A digital media item search interface may be presented in the same view as a messaging interface. The digital media item search interface may receive input such as from an on-screen keyboard to facilitate editing of user-provided search queries and submission of the user-provided search queries. The digital media item search interface may present digital media item tags relating to context information based on input received by the digital media item search query field. The digital media item search interface may present visual previews of local and/or connected digital media items corresponding to digital media item tags presented in the digital media item tag field. The digital media item search interface may receive user selections of individual digital media items to be communicated to one or more other users via the messaging interface.

Abstract: A previously trained classification model associated with the machine learning system is configured to process an input to generate i) a first prediction that represents a characteristic associated with the input, and ii) a representation of accuracy associated with the prediction. A retraining subsystem is configured to receive the input, the first prediction, and the representation of accuracy. The retraining subsystem processes the input to generate a prediction representing a characteristic. A sufficiency of certainty of the first prediction is determined based on at least the input, the first prediction, the measure of accuracy, and the second prediction. Based at least on the determined sufficiency the retraining subsystem causes the machine learning system to be automatically retrained, be retrained using the input with active learning or not retrained.

Abstract: 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.

Abstract: In some embodiments, a given client computing platform may include a client-side machine learning model configured to facilitate deep neural network operations on structured data. The operations may be performed by a client application installed on the given client computing platform. The client application may locally access the client-side machine learning model in order to perform the operations. Deep neural network operations on structured data may be performed at one or more servers. Sharing of model states may be facilitated between the cloud machine learning model and the client-side machine learning model. The cloud machine learning model may be improved, at one or more servers, based on usage of the application and user interactions with the given client computing platform.

Abstract: 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.

Abstract: Off-line deep neural network operations on client computing platforms may be enabled by cooperative machine learning across multiple client computing platforms and the cloud. A given client computing platform may include a client-side machine learning model configured to facilitate deep neural network operations on structured data. The operations may be performed by a client application installed on the given client computing platform. The client application may locally access the client-side machine learning model in order to perform the operations. Deep neural network operations on structured data may be performed at one or more servers. Sharing of model states may be facilitated between the cloud machine learning model and the client-side machine learning model. The cloud machine learning model may be improved, at one or more servers, based on usage of the application and user interactions with the given client computing platform.

Abstract: 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.

Abstract: In certain embodiments, training of a prediction model (e.g., recognition or other prediction model) may be facilitated via a training set based on one or more logos or other graphics. In some embodiments, graphics information associated with a logo or graphic (e.g., to be recognized via a recognition model) may be obtained. Media items (e.g., images, videos, etc.) may be generated based on the graphics information, where each of the media items includes (i) content other than the logo and (ii) a given representation of the logo integrated with the other content. In some embodiments, the media items may be processed via the recognition model to generate predictions (related to recognition of the logo or graphic for the media items). The recognition model may be updated based on (i) the generated predictions and (ii) corresponding reference indications (related to recognition of the logo for the media items).