Methods for evaluating sensor data to predict when the sensor should be recalibrated are described. The methods include a model that utilizes current wellbore data as input for the recalibration prediction.

An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.

Methods and apparatus for determining whether a media presentation device is in an on state or an off state are disclosed. A disclosed example method comprises determining contribution values from at least one of a signal measured from a sensing device or an output signal accessed from the presentation device, wherein the contribution values are indicative of a state of a presentation device. Summing, via a logic circuit, a first plurality of the contribution values corresponding to a first measurement cycle to generate a first intermediate fuzzy score for the first measurement cycle. Storing the first intermediate fuzzy score in a buffer including a plurality of intermediate fuzzy scores corresponding to respective measurement cycles. Combining, via the logic circuit, the intermediate fuzzy scores corresponding to a first time period to form a final fuzzy score. When the final fuzzy score satisfies a threshold, setting the state of the presentation device as on and enabling crediting of media presented by the presentation device.

According to an embodiment, a natural language processing artificial intelligence network and data security system determines an emotions model for one or more users from electronic natural language interactions of the users. The system includes a natural language processing decoder to determine textual features from the electronic natural language interactions that may be indicative of emotional states of the users. They system includes an emotions model encoder that generates an emotions model based on the emotional states of the users in the electronic natural language interactions retrieved from the data storage. The system also includes an artificial intelligence network and data security subsystem. The artificial intelligence network and data security subsystem may use the emotions model as a primitive for artificial intelligence based tasks including computer system security, network security, data security, proactive monitoring and preventive actions, that are moderated using the context provided by the emotional state of a user.

An apparatus for applying dynamic quantization of a neural network is described herein. The apparatus includes a scaling unit and a quantizing unit. The scaling unit is to calculate an initial desired scale factors of a plurality of inputs, weights and a bias and apply the input scale factor to a summation node. Also, the scaling unit is to determine a scale factor for a multiplication node based on the desired scale factors of the inputs and select a scale factor for an activation function and an output node. The quantizing unit is to dynamically requantize the neural network by traversing a graph of the neural network.

A machine learning hardware accelerator architecture and associated techniques are disclosed. The architecture features multiple memory banks of very wide SRAM that may be concurrently accessed by a large number of parallel operational units. Each operational unit supports an instruction set specific to machine learning, including optimizations for performing tensor operations and convolutions. Optimized addressing, an optimized shift reader and variations on a multicast network that permutes and copies data and associates with an operational unit that support those operations are also disclosed.

An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.

A device may identify a set of features associated with the unknown object. The device may determine, based on inputting the set of features into a threat prediction model associated with a set of security functions, a set of predicted threat scores. The device may determine, based on the set of predicted threat scores, a set of predicted utility values. The device may determine a set of costs corresponding to the set of security functions. The device may determine a set of predicted efficiencies, associated with the set of security functions, based on the set of predicted utility values and the set of costs. The device may identify, based on the set of predicted efficiencies, a particular security function, and may cause the particular security function to be executed on the unknown object. The device may determine whether another security function is to be executed on the unknown object.

An automated method of detecting patterns corresponding to a plurality of real world business measures corresponding to a plurality of business processes, assessing the next instance of such measures and related business attributes, and describing the next best action to optimize business outcomes based upon a plurality of control parameters. The system operates by continuously abstracting input data from a process agnostic data system (PADS) that links real-world things, activities and processes, into a process agnostic measure store (PAMS) configured to accept measures data without limitation as to a specific process or a plurality of processes. The machine self-learning system can then automatically project a business outcome, suggest most relevant attributes that can impact the said outcome, and suggest actions to change such outcome(s).

Provided is a dispenser mapping apparatus for multi-analysis and an operation method thereof according to an embodiment. The method may include receiving analysis informationthe analysis information including information relating to at least one of a plurality of samples, a detection gene for each of the plurality of samples, and a primer corresponding to the detection gene which are subject to analysisdividing a reaction plate into a plurality of areas and sequentially arranging the plurality of samples and the plurality of primers on the plurality of areas of the reaction plate.