Abstract: The present disclosure relates to methods and systems for sharing with a plurality of users a chat session that uses large language models to provide responses for input messages received for the chat session. The methods and systems provide access to the chat session to the users and update the chat session in response to any changes made to the chat session by any of the users. The methods and systems allow the users to resume the chat session at a future time using the chat session history.

Abstract: The present disclosure relates to extracting entities from a collection of digital content items based on text from within the digital content items. For example, the present disclosure describes a customizable entity extraction system that utilizes a number of models to extract entities, rank entities, and classify certain entities using a combination of rule-based and machine learning approaches. In one or more embodiments, a customizable entity extraction system applies a set of rules to unstructured text of a collection of digital content items to extract and classify a set of entities in connection with a specific domain of interest.

Abstract: This disclosure presents a technique for enhancing the performance of models such as large multimodal models (LMMs) without retraining or fine-tuning. This technique includes an iterative refinement process implemented by three main components: a forward generator, a backward generator, and a discriminator. The forward generator translates a prompt into an output in a different modality, the backward generator translates this output back into the original modality, and the discriminator compares the prompt and the translated prompt to generate a hint for refining the prompt to reduce differences. This cycle continues until the original prompt and the translated prompt match, achieving cycle consistency. The solution offers several advantages, including improving model performance without the need for costly fine-tuning, training data, or expertise.

Abstract: The present disclosure relates to efficiently receiving and processing input tasks in a way that is scalable and which reduces both the quantity of tokens processed by a foundation model (e.g., an LLM) as well as the number of API calls that are made in processing the input tasks. A system batches a set of inputs to provide as a single batch of input(s) into an LLM. The system generates one or more permutations of the batched input(s) to determine outputs based on variable orders in which the input data is provided within the respective permutations of the batched inputs. The system further may eliminate one or more of the data inputs within the respective batches to facilitate smaller batched inputs without sacrificing accuracy in a set of outputs generated by the LLM responsive to the batch permutations.

Abstract: The present disclosure relates to methods and systems for sharing with a plurality of users a chat session that uses large language models to provide responses for input messages received for the chat session. The methods and systems provide access to the chat session to the users and update the chat session in response to any changes made to the chat session by any of the users. The methods and systems allow the users to resume the chat session at a future time using the chat session history.

Abstract: The present disclosure relates to methods and systems for automatically generating documents for a specific topic using large language models. The methods and systems receive an input query that identifies a topic for the document. The methods and systems automatically generate, using the large language models, a framework for the document with sections and subsections for the document. The methods and systems write the document, using the large language models, and provide references for the data sources used to obtain the data that the large language model used to write the document.

Abstract: The present disclosure relates to methods and systems for sharing with a plurality of users a chat session that uses large language models to provide responses for input messages received for the chat session. The methods and systems provide access to the chat session to the users and update the chat session in response to any changes made to the chat session by any of the users. The methods and systems allow the users to resume the chat session at a future time using the chat session history.

Abstract: The present disclosure relates to extracting key concepts from digital content items and determining associations between the key concepts and candidate terms for use in generating and presenting a correlation graph object based on the determined associations. For example, systems described herein involve determining frequency of co-occurrence between various key concepts and applying a classification model (e.g., a zero-shot classification model) to the key concepts and candidate terms to determine associations between the key concepts and candidate terms for a given domain of interest. The systems further involve generating a graph object and processing graph queries in a way that enables fast and efficient presentation of slices of the graph object that provide a visual depiction of key concepts and edges representing associations between pairs of the key concepts.

Abstract: The present disclosure relates to extracting entities from a collection of digital content items based on text from within the digital content items. For example, the present disclosure describes a customizable entity extraction system that utilizes a number of models to extract entities, rank entities, and classify certain entities using a combination of rule-based and machine learning approaches. In one or more embodiments, a customizable entity extraction system applies a set of rules to unstructured text of a collection of digital content items to extract and classify a set of entities in connection with a specific domain of interest.

Abstract: The present disclosure relates to extracting key concepts from digital content items and determining associations between the key concepts and candidate terms for use in generating and presenting a correlation graph object based on the determined associations. For example, systems described herein involve determining frequency of co-occurrence between various key concepts and applying a classification model (e.g., a zero-shot classification model) to the key concepts and candidate terms to determine associations between the key concepts and candidate terms for a given domain of interest. The systems further involve generating a graph object and processing graph queries in a way that enables fast and efficient presentation of slices of the graph object that provide a visual depiction of key concepts and edges representing associations between pairs of the key concepts.

Abstract: A computer-implemented method for generating a predictive output based on a predictive input includes generating a plurality of rule-based prediction scores by executing one or more prediction rules on the prediction input, wherein each prediction rule of the one or more prediction rules is associated with a rule condition and one or more predictive weights, each predictive weight of the one or more predictive weights is associated with a related prediction category of a plurality of prediction categories, and each prediction category of the plurality of prediction categories is associated with a rule-based prediction score; determining a rule-based prediction output based at least in part on the plurality of rule-based prediction scores; and providing the plurality of rule-based prediction scores and the rule-based prediction output to a machine learning engine, wherein the machine learning engine is configured to generate a machine-learning based prediction output based at least in part on the plurality of

Abstract: A tower bottom cooling device for a wind turbine includes a tower and a heat sink configured to cool a heat generating component arranged at a bottom of the tower; a main air duct is provided inside the tower, and the heat sink is disposed inside the main air duct; a first fan is arranged in the main air duct, and is configured to drive air in the main air duct to flow to cool the heat sink; and the main air duct and an external environment of the tower together form an open cycle.

Abstract: A tower bottom cooling device for a wind turbine includes a tower and a heat sink configured to cool a heat generating component arranged at a bottom of the tower; a main air duct is provided inside the tower, and the heat sink is disposed inside the main air duct; a first fan is arranged in the main air duct, and is configured to drive air in the main air duct to flow to cool the heat sink; and the main air duct and an external environment of the tower together form an open cycle.

Abstract: A system includes an infrared camera, an infrared light source, and a processor. The processor is programmed to receive, from the infrared camera, an image of a hand illuminated using the infrared light source, and send the image to a remote server to identify a user corresponding to the hand according to a vein pattern of the hand. An image of a hand illuminated using an infrared light source is received from an infrared camera. Region-of-interest segmentation is performed on the image to generate a segmented image of consistent hand location and orientation. Feature extraction is performed on the segmented image to generate a feature-extracted vein image. Matching of the feature-extracted vein image is performed against a database of feature-extracted vein images to identify a user identity corresponding to the hand.