Abstract: Gaming systems and methods of operation thereof that provide a keno game employing one or more bonus ball drawn as the trigger(s) for one or more secondary events.

Abstract: Methods and systems include determining that a query is relevant to information that is unknown to a pre-trained language model. Outputs from adapter layers are added to outputs of respective transformer layers of the language model to infuse the language model with the information, such that the language model generates a response to the query that accounts for the information that is unknown to the pre-trained language model. An action is performed based on the response.

Abstract: Techniques for executing system actions during conversations between a human user and an autonomous conversational system are disclosed. A first generative language model processes user messages to determine user intent, while a dialog management model analyzes the intent and conversation context to identify required system actions. The system executes actions by retrieving parameters from context, performing database queries or API calls to obtain response data, and storing results in conversation context variables. A second generative language model generates natural language responses using the action results. The system maintains conversation context including message history, action results, and state information, validates action execution, and initiates human agent handoff when needed. The system improves over time by detecting poor performance, gathering problematic conversations, and retraining using updated configurations.

Abstract: The present invention relates to a detection assembly (102) for detecting passing sports timing transponders (14) in a sports event, comprising: an antenna (106) and a calculation unit (104) connected to the antenna; and a channel element (22) for positioning the antenna and the calculation unit on an underlying surface and for protecting the antenna and the calculation unit from external forces, wherein said channel element is connectable to a preceding channel element (108) and a following channel element (110) to form a line; said calculation unit is connectable to a first neighboring calculation unit (112) via a first cable (114) extending into the preceding channel element and to a second neighboring calculation unit (116) via a second cable (118) extending into the following channel element; and said calculation unit includes a voltage detection circuitry (124) for detecting whether power is provided to the calculation unit via the first cable or via the second cable.

Abstract: A method, system and devices are provided for unidirectional transfer of data from a sender device (100) to a receiver device (200). At the sender device, respective parts of the data are encoded to generate respective 2D barcodes, and the 2D barcodes are sequentially displayed on a display (142). At the receiver device, an image sensor (222) is used to capture the sequentially displayed 2D barcodes to obtain a series of captured images, and in each or a subset of the series of captured images, detect a position of a respective 2D barcode in the captured image, scan the 2D barcode at the identified position to obtain barcode data, and decode the barcode data to, together with the barcode data obtained from other captured images, obtain a received version of the transmitted data.

Abstract: The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data acquired continuously by a plurality of types of sensors deployed on the vehicle are first received, where the plurality of types of sensor data provide information about surrounding of the vehicle. Based on at least one model, one or more items are tracked from a first of the plurality of types of sensor data acquired by one or more of a first type of the plurality of types of sensors, wherein the one or more items appear in the surrounding of the vehicle. At least some of the one or more items are then automatically labeled on-the-fly via either cross modality validation or cross temporal validation of the one or more items and are used to locally adapt, on-the-fly, the at least one model in the vehicle.

Abstract: Techniques that enable users to generate an artificial intelligence (AI) integration configured to perform a task utilizing one or more machine learning models are described herein. An AI studio provides a user with options for creating a configuration object associated with an AI integration via a user interface. The AI studio may receive a request, from a user, to generate a prompt, the prompt defining an instruction to one or more machine learning model(s) (e.g., a large language model) to perform a task or generate an output based in part on input received from a user. A user may be presented with an option to select a type of transformer and deployment configuration to associate with the AI integration, the deployment configuration indicating an enterprise system and a deployment location within the enterprise system.

Abstract: A generative model, e.g. a large language model (LLM), may be accessed by users over a network. A user might experience latency in the response from the generative model. To address the technical problem of latency, in some embodiments, the latency of the response from a first generative model is measured. If the latency falls within a particular range, then a switch to a second generative model is performed. In some embodiments, if the first generative model is not yet finished providing the response, then the partially-completed response from the first generative model is not deleted. Instead, the second generative model provides the remaining portion of the response so that the switch appears transparent and seamless to the user, and does not require restarting the generation process, thereby avoiding or mitigating the loss of already generated output and hence saving computer resources.

Abstract: A method for fill level determination, which can include receiving a set training set, training a neural network, selecting reference images, and/or determining a container fill level. A system for fill level determination, which can include a computing system, one or more containers, and/or one or more content sensors.

Abstract: Generating a multi-dimensional video using a multi-dimensional video generative model for, including, but not limited to, at least one of static portrait animation, video reconstruction, or motion editing. The method including providing data into the multi-dimensionally aware generator of the multi-dimensional video generative model, and generating the multi-dimensional video from the data by the multi-dimensionally aware generator.

Abstract: A method for improving a performance for an instruction-following language model including the steps of determining a degree of bias of neurons with respect to an instruction label, selecting one or more biased neuron based on the degree of bias, and removing an influence of the biased neuron.

Abstract: A system may display a set of images to a user, the set of images includes a plurality of synthetic images output by a generative adversarial network (GAN) includes a generator and a discriminator, and a plurality of non-synthetic images, detect a user response to the set of images, the user response includes at least a gaze of the user relative to the set of images, and train the GAN based at least on the user response, including tuning the generator based on the gaze of the user.

Abstract: A computing platform may train, using historical information indicating a plurality of regimes for LLM outputs, an LLM steward model, which may configure the LLM steward model to generate LLM validation information indicating classifications of LLM outputs as acceptable/tolerable/non-acceptable. The computing platform may input, into an LLM, an LLM prompt, which may cause the LLM to generate an LLM output. The computing platform may input the LLM output into the LLM steward model, which may cause the LLM steward model to output the LLM validation information. Based on outputting LLM validation information indicating that the LLM output is acceptable/tolerable, the computing platform may send the LLM output to a user device for presentation. Based on outputting LLM validation information indicating that the LLM output is non-acceptable, the computing platform may update the LLM output to conform with a corresponding subset of the plurality of regimes.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks using a preference function that compares a measure of quality between network outputs. According to one aspect there is provided a method for training a target neural comprising: at each of a plurality of training steps, receiving one or more network inputs; for each of the network inputs, processing the network input using the target neural network to generate a first network output, processing the network input using an alternative neural network for the training step to generate a second network output, and applying a preference function to the first and second network outputs to generate a preference score comparing the first and second network outputs; and updating the target neural network weights using an objective function that encourages the first network outputs to be preferred over the second network outputs.

Abstract: A data fusion method and apparatus, and a device and a storage medium are provided. The method includes: acquiring, from a first data center, a plurality of pieces of first data to be compared that are within a preset time period, and acquiring, from a second data center, a plurality of pieces of second data to be compared that are within the preset time period; then, determining the same data unique identifier in the plurality of pieces of first data to be compared and the plurality of pieces of second data to be compared, and taking the same data unique identifier as a first data identifier; and acquiring first data to be compared that corresponds to the first data identifier, and acquiring second data to be compared that corresponds to the first data identifier.

Abstract: A quantum system can include a reconfigurable array of quantum sensors; and one or more processors configured to perform operations, the operations including: configuring the quantum system including the reconfigurable array of quantum sensors; obtaining a signal from a quantum sensor exposed to a signal for a period of time; transducing at least one state of the quantum sensor into quantum memory; encoding the at least one transduced state using an error-correcting code; processing the encoded states by quantum operations to extract at least one measurement value; and processing the extracted at least one measurement value to determine one or more classical values.

Abstract: There is described a quantum controller for interfacing between a host computer and a quantum processing unit (QPU) having a plurality of qubits. The quantum controller comprises signal processing hardware configured for transforming instructions from the host computer into control signals readable by the QPU, the signal processing hardware comprising programmable logic and signal conversion circuits; hardware accelerator components dedicated to tasks offloaded from the programmable logic, the hardware accelerator components comprising at least one processor different from the QPU; and a carrying substrate on which the signal processing hardware and the hardware accelerator components are coupled, the carrying substrate providing power and signal routing to the signal processing hardware and the hardware accelerator components.

Abstract: Methods, systems and computer program products for distributed machine learning are provided. Such methods, systems and products may comprise, or may comprise instructions operable to configure one or more processors to perform, a set of acts. Such acts may comprise a plurality of nodes performing a set of training round activities, and a server performing a set of network topology design activities. The network topology design activities may comprise the server generating update data based on data from the plurality of nodes. The plurality of nodes may use that data to update control values used to exchange and combine machine learning models. After the set of training round activities and the set of network topology design activities have been repeated one or more times, the plurality of nodes may send machine learning models to the server, and the server may use them to create an aggregated machine learning model.

Abstract: Systems, methods, and computer-readable storage media for combining machine learning models which respectfully use public and private data using a third machine learning model. Upon training a public data machine learning model and a private data machine learning model, the system trains a public and private data machine learning model using a combination of: (1) historical public data machine learning predictions output by the public data machine learning model, and (2) historical private data machine learning predictions output by the private data machine learning model. The system then executes the public and private data machine learning models, resulting in a public data machine learning prediction and a private data machine learning prediction, then executes the public and private data machine learning model using the public data machine learning prediction and the private data machine learning prediction as inputs, resulting in a final prediction.

Abstract: A scanning window is used to scan an image frame of a sensor when doing object detection. In one approach, positions within the image frame are stored in memory. Each position corresponds to an object detection at that position for a prior frame of data. A first area of the image frame is determined based on the stored positions. When starting to analyze a new frame of data, the first area is scanned to detect at least one object. After scanning within the first area, at least one other area of the new image frame is scanned.