Abstract: Controlling a heating process is provided. An image of a raw food item is captured. Using a generative model, synthesized images of the cooked food are generated at different levels of doneness based on the raw image. A selection of one of the synthesized cooked images is received. The food item is cooked to the levels of doneness corresponding to the one of the synthesized cooked images.

Abstract: A method (110) for operating a cooking appliance (10) includes receiving data (16) from an image sensor (14) operably associated with a food-receiving area (12) of the cooking appliance (10), the data (16) comprising an image (24) of a food product (F), determining whether the image (24) of the food product (F) corresponds with one of a plurality of known food product types accessible by the cooking appliance (10) based on an analysis of the image (24) of the food product using an identification model (31), and in response to the image (24) of the food product (F) not corresponding with any one of the plurality of known food product types, designating the image (24) of the food product (F) as a new food product type and causing the new food product type to be added to the plurality of known food product types accessible by the cooking appliance without retraining the identification model (31).

Abstract: Controlling a heating process is provided. An image of a raw food item is captured. Using a generative model, synthesized images of the cooked food are generated at different levels of doneness based on the raw image. A selection of one of the synthesized cooked images is received. The food item is cooked to the levels of doneness corresponding to the one of the synthesized cooked images.

Abstract: A method is provided. The method is executed by an autocomplete prediction engine implemented as a computer program within a computing environment. The autocomplete prediction engine executes automated communication mining on a communication. The method includes processing the communication to extract intents and entities related to each intent. The method includes providing the intents and the entities into forms using a language model to provide a conversational or natural language understanding of the communication.

Abstract: Embodiments are directed to a machine learning engine that determines training documents and validation documents from a plurality of documents. The machine learning engine may determine attributes associated with the documents. In response to receiving a request to predict attribute values of a selected document the machine learning engine may train a plurality of ML models to predict the attribute values based on the training documents and the attributes and associate the trained ML models with an accuracy score. The machine learning engine may determine candidate ML models from the trained ML models based on the training accuracy scores. The machine learning engine may evaluate and rank the candidate ML models based on the request and the validation documents. The machine learning engine may generate confirmed ML models based on the ranked candidate ML models such that the confirmed ML models may answer the request.

Abstract: A path collision avoidance method and system may include obtaining a three-dimensional point cloud of an at least partially enclosed space, obtaining a voxelized model of a vehicle/robot, and outputting a visual representation of navigation of the vehicle/robot within the at least partially enclosed space based on the three-dimensional point cloud of the at least partially enclosed space and the voxelized model of the vehicle/robot.

Abstract: Controlling a heating process is provided. An image of a raw food item is captured. Using a generative model, synthesized images of the cooked food are generated at different levels of doneness based on the raw image. A selection of one of the synthesized cooked images is received. The food item is cooked to the levels of doneness corresponding to the one of the synthesized cooked images.

Abstract: Embodiments are directed to a machine learning engine that determines training documents and validation documents from a plurality of documents. The machine learning engine may determine attributes associated with the documents. In response to receiving a request to predict attribute values of a selected document the machine learning engine may train a plurality of ML models to predict the attribute values based on the training documents and the attributes and associate the trained ML models with an accuracy score. The machine learning engine may determine candidate ML models from the trained ML models based on the training accuracy scores. The machine learning engine may evaluate and rank the candidate ML models based on the request and the validation documents. The machine learning engine may generate confirmed ML models based on the ranked candidate ML models such that the confirmed ML models may answer the request.

Abstract: Embodiments are directed to a machine learning engine that determines training documents and validation documents from a plurality of documents. The machine learning engine may determine attributes associated with the documents. In response to receiving a request to predict attribute values of a selected document the machine learning engine may train a plurality of ML models to predict the attribute values based on the training documents and the attributes and associate the trained ML models with an accuracy score. The machine learning engine may determine candidate ML models from the trained ML models based on the training accuracy scores. The machine learning engine may evaluate and rank the candidate ML models based on the request and the validation documents. The machine learning engine may generate confirmed ML models based on the ranked candidate ML models such that the confirmed ML models may answer the request.

Abstract: Embodiments are directed to a machine learning engine that determines training documents and validation documents from a plurality of documents. The machine learning engine may determine attributes associated with the documents. In response to receiving a request to predict attribute values of a selected document the machine learning engine may train a plurality of ML models to predict the attribute values based on the training documents and the attributes and associate the trained ML models with an accuracy score. The machine learning engine may determine candidate ML models from the trained ML models based on the training accuracy scores. The machine learning engine may evaluate and rank the candidate ML models based on the request and the validation documents. The machine learning engine may generate confirmed ML models based on the ranked candidate ML models such that the confirmed ML models may answer the request.