A treatment model that is a first neural network is trained to optimize a treatment loss function based on a treatment variable t using a plurality of observation vectors by regressing t on x.sup.(1),z. The trained treatment model is executed to compute an estimated treatment variable value {circumflex over (t)}.sub.i for each observation vector. An outcome model that is a second neural network is trained to optimize an outcome loss function by regressing y on x.sup.(2) and an estimated treatment variable t. The trained outcome model is executed to compute an estimated first unknown function value {circumflex over (α)}(x.sub.i.sup.(2)) and an estimated second unknown function value {circumflex over (β)}(x.sub.i.sup.(2)) for each observation vector. An influence function value is computed for a parameter of interest using {circumflex over (α)}(x.sub.i.sup.(2)) and {circumflex over (β)}(x.sub.i.sup.(2)). A value is computed for the predefined parameter of interest using the computed influence function value.

The present disclosure provides a method and an apparatus for analyzing an intention based on artificial intelligence. The method includes: receiving a query; acquiring a keyword of an intention of the query according to a preset strategy; acquiring a qualifier of the intention of the query according to the keyword based on a syntax tree; and determining the intention of the query according to the keyword and the qualifier.

The description relates to artificial intelligence assisted wearables, such as backpacks. An example backpack may include sensors, such as a microphone and a camera. The backpack may receive a contextual voice command from a user. The contextual voice command may include a non-explicit reference to an object in an environment. The backpack may use the sensors to sense the environment, use an artificial intelligence engine to identify the object in the environment, and use a digital assistant to perform a contextual task in response to the contextual voice command. The contextual task may relate to the object in the environment. The backpack may output a response to the contextual voice command to the user.

The description relates to artificial intelligence assisted wearables, such as backpacks. An example backpack may include sensors, such as a microphone and a camera. The backpack may receive a contextual voice command from a user. The contextual voice command may include a non-explicit reference to an object in an environment. The backpack may use the sensors to sense the environment, use an artificial intelligence engine to identify the object in the environment, and use a digital assistant to perform a contextual task in response to the contextual voice command. The contextual task may relate to the object in the environment. The backpack may output a response to the contextual voice command to the user.

System for Knowledge Creation with the Living Machine for the Manufacture of Living Knowledge where Living Trust is being built and advanced.

An integrated intelligent system includes a first intelligent electronic device, a second intelligent electronic device, a transferable intelligent control device (TICD) and a cross product bus. The first intelligent electronic device performs a first function and the second intelligent electronic device performs a second function. The cross product bus couples the first intelligent electronic device to the transferable intelligent control device. The TICD partially controls behaviors of the intelligent electronic device by sending commands over the cross product bus to the first intelligent electronic device and the TICD partially controls behaviors of the second intelligent electronic device to perform the second function. The TICD is first attached to the first intelligent electronic device to partially control the behaviors of the first electronic device, then detached from the first electronic device, and then attached to the second intelligent electronic device to perform the second function.

Disclosed are systems and methods for decoding chord information from brain activity. General chord decoding protocols involves using computational operations for the extraction of neural codes, the development of the decoding model, and the deployment of the trained model.

A slicer in a material drop ejecting three-dimensional (3D) object printer identifies the positions and local densities for a plurality of infill lines within a perimeter to be formed within a layer of an object to be formed by the printer. The local density of each infill line is filtered and a control law is applied to the filtered local density to identify an error in the local density compared to a target density. This process is performed iteratively until the error is within a predetermined tolerance range about the target local density. The error is used to generate machine ready instructions to operate the 3D object printer to achieve the target density for the infill lines.

In establishment of a general-purpose artificial intelligence system, the machine simulates similarity, repeatability and adjacency of information, and stores the demand, award/penalty and emotion symbols together with related low-level features as a preset relation network. When the machine encounters input information, the machine iteratively identifies low-level features in the input information and stores them in a memory according to a simultaneous storage method. With the low-level features as nodes and the similarity, repeatability and adjacency relations between nodes as connection relations, the machine establish relations between the low-level features and the demand, award/penalty and emotion symbols to extend the relation network. The machine uses the low-level features and searches for related low-level features through a chain associative activation process, searches for imitable experiences through segmented simulation, reassembles the experiences according to a principle of benefit-seeking and harm-avoiding to form an optimal response path.

A computer implemented technique for developing a behaviour tool to estimate a behaviour of a particular person in response to a given situation, comprises maintaining, in a storage device, digital memories and a record of associations between the digital memories, where a given digital memory is generated in response to a given event associated with the particular person and is determined from analysis of multiple items of data associated with the given event, including at least items of personal data derived from signals gathered from a plurality of sensors used to monitor the particular person. Processing circuitry is then employed to analyse the digital memories and the record of associations between the digital memories, in order to generate a digital twin of the particular person comprising one or more personalised cognitive models, each personalised cognitive model being arranged to emulate an associated cognitive skill of the particular person. At least one of the one or more personalised cognitive models forming the digital twin is then output for incorporation within the behaviour tool so as to cause the estimated behaviour of the particular person in response to the given situation to be influenced, at least in part, by model output data generated by the at least one of the one or more personalised cognitive models.