An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.

A method for retrieving data on a web page includes performing a simulated user operation on a target web page to generate a result web page, retrieving a source code of the result web page, creating a data table according to the source code, and performing a data cleaning operation with the data table to generate cleaned data and store the cleaned data in a database. Each temporary row of the data table is corresponding to a quotation plan.

[Problem] An API adapter of a wholesale service provided in a coordination execution apparatus of a wholesale service.

[Solution] An API adapter creation apparatus 100 is configured to accept, from a developing engineer, a setting of an execution order of a wholesale service API invoked by internal processing of an adapter API provided by an API adapter, a setting of a request parameter of the adapter API serving as an acquisition source of a request parameter of the wholesale service API, and a setting of a response parameter of the adapter API serving as a reflection destination of a response parameter of the wholesale service API. The API adapter creation apparatus 100 generates a source code based on the accepted settings.

Methods and apparatuses that apply a hierarchical-decomposition reinforcement learning technique to train one or more AI objects as concept nodes composed in a hierarchical graph incorporated into an AI model. The individual sub-tasks of a decomposed task may correspond to its own concept node in the hierarchical graph and are initially trained on how to complete their individual sub-task and then trained on how the all of the individual sub-tasks need to interact with each other in the complex task in order to deliver an end solution to the complex task. Next, during the training, using reward functions focused for solving each individual sub-task and then a separate one or more reward functions focused for solving the end solution of the complex task. In addition, where reasonably possible, conducting the training of the AI objects corresponding to the individual sub-tasks in the complex task, in parallel at the same time.

Provided herein in some embodiments is an artificial intelligence (“AI”) engine hosted on one or more remote servers configured to cooperate with one or more databases including one or more AI-engine modules and one or more server-side client-server interfaces. The one or more AI-engine modules include an instructor module and a learner module configured to train an AI model. An assembly code can be generated from a source code written in a pedagogical programming language describing a mental model of one or more concept modules to be learned by the AI model and curricula of one or more lessons for training the AI model. The one or more server-side client-server interfaces can be configured to enable client interactions from a local client such as submitting the source code for training the AI model and using the trained AI model for one or more predictions.

Techniques for generating a dataflow graph include generating a first dataflow graph with a plurality of first nodes representing first computer operations in processing data, with at least one of the first computer operations being a declarative operation that specifies one or more characteristics of one or more results of processing of data, and transforming the first dataflow graph into a second dataflow graph for processing data in accordance with the first computer operations, the second dataflow graph including a plurality of second nodes representing second computer operations, with at least one of the second nodes representing one or more imperative operations that implement the logic specified by the declarative operation, where the one or more imperative operations are unrepresented by the first nodes in the first dataflow graph.

Disclosed herein are system, method, and device embodiments for implementing a customizable animation experience. A multi-tenant service may associate an animation element with a visual component of an application, and generate a markup component including an animation parameter configured to customize the animation element within the application code. Further, the multi-tenant service may receive a request for the animation from an animation manager based on execution of the application code, and send the animation information to the animation manager. In some embodiments, the animation manager is configured to set the animation parameter to the animation information and present an animation associated with the animation element based on the animation parameter.

A computing system includes a processor, and a storage device holding instructions executable by the processor. The instructions are executable to receive a source code through an application programming interface (“API”) exposed to a graphical user interface (“GUI”). The GUI is configured to enable an author to define a proposed model with a pedagogical programming language, the proposed model including an input, one or more concept nodes, and an output. The GUI is further configured to enable the author to provide a program annotation indicating an execution behavior for the source code, to generate an assembly code from the source code with a compiler of an artificial intelligence (“AI”) engine configured to work with the GUI; and to build an executable, trained AI model including a neural-network layout having one or more layers derived from the assembly code.

A software rewriting device, configured to rewrite software of a moving body, includes: a rewriting processing unit configured to execute a rewriting process of rewriting the software; and a communication unit configured to communicate with an accommodation area management device managing an accommodation area for accommodating the moving body. When the moving body executes the rewriting process in the accommodation area, the communication unit notifies execution of the rewriting process to the accommodation area management device.

Certain aspects of the disclosure are directed to multi-level programming of a VoIP communications system. According to a specific example, a VoIP server is configured and arranged to identify, in response to received VoIP telephone calls from VoIP endpoint devices, a set of multi-level scripts written in a programming language that includes call flow commands and a message exchange protocol between the call control server and data sources. The VoIP server is further configured to execute the set of multi-level scripts to retrieve data from the data sources and control, in response to the data, call flow for the VoIP calls.