100 results about "Artificial general intelligence" patented technology

Aspects of the disclosure generally relate to computing devices and may be generally directed to devices, systems, methods, and/or applications for learning the operation of a computing device or software application, storing this knowledge in a knowledgebase, neural network, or other repository, and enabling autonomous operation of the computing device or software application with partial, minimal, or no user input.

User authentication apparatus controlling access to systems, inputs owner's login name and password and then extracts the owner's timing vectors from keystroke characteristics with which the owner forms a training set. A semantic network uses multiple links to indicate that different pattern components of user's behavioral access create different kinds of relationships and “symbolic representations”. A neural network is trained by using each of the owner's timing vectors in the training set as an input. When a user inputs the owner's login name and password, it's checked and the user's timing vector is extracted to type the user's password if checked and demoted in confidence level if otherwise. The user's timing vector is applied to neural network and difference between the input/output is compared with a predetermined threshold; and if the difference is greater than the threshold, is prohibited. Preferably this is aided by response time to personal questions.

The invention provides an artificial intelligence-based object pushing method and apparatus. The method comprises the steps of inputting historical click behaviors of a target user to a built user model for performing learning to obtain multi-dimensional preference eigenvectors of a target user; obtaining eigenvectors of all to-be-pushed objects; inputting the eigenvectors of the to-be-pushed objects and the multi-dimensional preference eigenvectors to a trained deep neural network model for performing prediction to obtain prediction probabilities of the to-be-pushed objects; and pushing the to-be-pushed objects to the target user according to the obtained prediction probabilities. According to the method and the apparatus, the preferences of the target user are obtained through the user model built by a neural network, and the probability of possibly purchasing to-be-pushed group orders by the target user is obtained based on the deep neural network and the preferences of the target user, so that the pushing is more effective; and preference features are selected through training by the user model, so that a large amount of manpower does not need to be consumed for selection and the pushing efficiency is improved.

Exemplary embodiments of the present disclosure are directed towards an automated cooking system comprising of: a an intelligence layer is connected to databases, the databases configured to store various information and the intelligence layer configured to collect and analyze the technical data obtained from modules, a device layer comprises an automated cooking device and a cooking assistance module, cooking assistance module and automated cooking device are configured to retrieve information from databases and a feedback received from cooking assistance module is used to update cooking process, and computing device comprises voice assistant and health monitoring device are connected to the automated cooking device over network, the automated cooking device comprises a curry vessel and an ingredient rack, rice vessel positioned on first induction cooktop and curry vessel positioned on second induction cooktop, first induction cooktop configured for cooking rice and the second induction cooktop configured for curry making.

A computer implemented method for use in conjunction with a computing device, system, network, and cloud with touch screen two dimension display or augmented/mixed reality three dimension display comprising: obtaining, analyzing and detecting user blood and saliva chemistry data and mapping the blood and saliva data into a database associated with a specific user, applying the data with optimization equations and mapping equations to food chemistry such that a user may order food and beverage from a food/beverage distribution point or have food/beverage delivered to the user which has been specifically optimized for their specific blood characteristic target ranges. The method and system uses recursive techniques and neural networks to learn how to optimize food and beverage nutrient efficiency into the users blood chemistry.

A method for an AI time machine to accept sequential input tasks from at least one user, manage tasks, and execute tasks simultaneously or sequentially. Tasks specified by a user can be accomplished in the virtual world or in the real world and includes extracting digital data from electronic devices or manipulation of objects in the real world. The AI time machine's data structures, comprising: at least one dynamic robot to train the AI time machine; a main program with two modes: training mode and standard mode; external technologies, comprising: universal artificial intelligence programs, human level robots, psychic robots, super intelligent robots, the AI time machine, dynamic robots, a signalless technology, atom manipulators, ghost machines, a universal CPU, an autonomous prediction internet, and a 4-d computer; a videogame environment for virtual characters to do and store work; a prediction internet; a universal brain to store dynamic robot pathways or virtual character pathways, said universal brain, comprising: a real world brain, a virtual world brain, and a time machine world brain; a timeline of Earth that records predicted knowledge of Earth's past, current and future; a future United States government system; and a long-term memory. The present invention further serves as a universal AI to control at least one of the following: a machine, a hierarchical team of machines, a universal machine and a transforming machine.

The invention relates to a brain-like computing system which comprises an arithmetic/logical operation and control unit, a brain-like co-processing unit, a storage unit, an external interface and a bus for connecting the units and the external interface. The arithmetic/logical operation and control unit is used for programming and configuring the brain-like co-processing unit, executing arithmeticoperation or logical operation and controlling operation and data exchange of other units through a bus. The brain-like co-processing unit has an artificial neural network processing function and a pulse neural network processing function, and is used for executing artificial neural network calculation and pulse neural network calculation according to the arithmetic/logical operation and the instruction of the control unit, and storing the calculation result in the storage unit. According to the method, high calculation efficiency can be achieved when different tasks in universal artificial intelligence calculation are processed, real-time correspondence to the tasks is achieved through low-delay continuous execution of the calculation tasks, and meanwhile the calculation operation energyconsumption of the whole system is reduced.

The invention discloses a novel calibration method and a system for gas sensors and particulate matter sensors. The method comprises the steps of acquiring cross-over experiment data of parameters with influence on measurement accuracy of the sensors, and inputting a large number of laboratory data of different parameters to an established calibration model, so as to obtain linear and nonlinear calibration parameters of different sensors. The calibration model adopts a universal artificial-intelligence big data algorithm platform in view of different parameters, linear/nonlinear cross corresponding curves, containing different parameters, of temperature, humidity, wind direction, wind speed, atmospheric pressure, interference gas and sensor element aging of the sensors can be automatically extracted and obtained from experimental tank cross-over experiment parameter data through establishing relatively-deep artificial neural network or intelligent gene algorithm models, and random complicated nonlinear calibration model can be mapped, so that the robustness and adaptability are very high.

A method of creating human level artificial intelligence in machines and computer software is presented here, as well as methods to simulate human reasoning, thought and behavior. The present invention serves as a universal artificial intelligence program that will store, retrieve, analyze, assimilate, predict the future and modify information in a manner and fashion which is similar to human beings and which will provide users with a software application that will serve as the main intelligence of one or a multitude of computer based programs, software applications, machines or compilation of machinery.

The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence interaction method and an artificial intelligence interaction system. The artificial intelligence interaction method includes acquiring text information according to input of users; analyzing the text information to obtain reply information; synthesizing reply voice information according to the reply information; adjusting actions of virtual images in 3D (three-dimensional) models according to the reply information; displaying the virtual images by holographic imaging technologies and synchronously playing the reply voice information. The artificial intelligence interaction method and the artificial intelligence interaction system in an embodiment of the invention have the advantages that feedback in multiple dimensions such as vision and hearing can be provided for users, accordingly, natural interaction can be carried out, and interaction information is abundant.

Disclosed is a method and a system a method and a system for auto learning, artificial intelligence (AI) applications development, and execution. Various applications or operations may be associated with training environment-agnostic AI models, automated AI app application performance monitoring, fault, quality and performance remediation through prediction of failures or suboptimal performance, privacy and secure AI training and inference mechanism for data and AI model sharing between untrusted parties, and building auto learning applications that can automatically learn and improve.

The invention discloses an artificial intelligence-based prompt information recommendation method and apparatus. The method comprises the steps of obtaining text contents of a current conversation, and obtaining entity information and intention information of the text contents; generating a current state of a current environment according to the entity information and the intention information; generating multiple pieces of action information corresponding to the current state according to the current state and a pre-established reinforcement learning model; generating multiple pieces of to-be-filled entity contents corresponding to the action information according to the action information and a search result of the text contents; and filling preset speaking art templates corresponding to the action information with the to-be-filled entity contents corresponding to the action information so as to generate multiple pieces of prompt information, and providing the prompt information for a user. According to the method, quality indexes of interaction in all rounds are considered, so that the overall satisfaction of the conversation can be improved to a final index, the satisfaction of the user in the conversation can be improved, and the user experience in the overall conversation is enhanced.

The invention discloses a fault diagnosis method of a spacecraft based on a back propagation BP neural network, which mainly solves the problem that prior knowledge is highly dependent in the prior art. The invention utilizes spacecraft environmental data to train back propagation BP neural network to fit probability data of spacecraft abnormal event occurrence. The implementation scheme is as follows: Back Propagation BP neural network model is determined, the training set data and test set data of back propagation BP neural network are obtained, the parameters of back propagation BP neural network is initialized, the BP neural network is trained, the trained BP neural network is optimized, the optimized BP neural network is used to diagnose the new spacecraft data. The invention fully utilizes the adaptive ability and the learning ability of the BP neural network, can well fit the probability data of the occurrence of the abnormal event of the spacecraft, reduces the dependence on the prior knowledge, and can be used in the field of wireless network communication.

A heterogeneous classifier based on actual reservoir and well data is developed to qualitatively classify oil well producer performance, and based on the classification drill a new well into a producing reservoir or adjust fluid flows in an existing well. The data includes perforation interval(s), completion type, and how far or close the perforated zones are located relative to the free water level or gas cap. The data also include geological data, such as major geological bodies like regional faults and fractures. The features may be prioritized before classification. The classifier utilizes four different techniques to apply pattern recognition on reservoir simulation vector data to classify the wells. Three of the classification techniques are supervised learning methods: Bayesian classification, dynamic time warping and Neural Network. The fourth classification is an unsupervised method, clustering, to automate well grouping into similar categories.

The invention relates to an employee state monitoring system based on artificial intelligence, belongs to the technical field of state monitoring, and solves the problems that in the prior art, the subjectivity is high, the real-time performance is poor, the judgment result accuracy is low, and the safety production requirement cannot be met. The system comprises multifunctional sensors arranged on a helmet and a wrist strap, and an information processor arranged on safety work clothes worn by an employee. The multifunctional sensor is used for collecting field working environment audio and video information and personal physiological data information of the employee and transmitting the information to the information processor; And the information processor is used for identifying the working state and the real-time physiological health condition of the employee according to the on-site working environment audio and video information and the personal physiological data information ofthe employee so as to judge whether the pre-post physiological state of the employee meets the post requirement or not or whether the physiological state of the employee in work is qualified for the current work or not, and displaying a judgment result. The system can automatically and accurately analyze the physiological and psychological conditions of the employee in real time.

The invention provides an artificial intelligence-based method for formulating an application reporting scheme for college entrance examination. The method is characterized by comprising the followingsteps: (1) collecting data; (2) cleaning the data; (3) inputting the examinee information; (4) analyzing the data; (5) intelligently selecting; (6) evaluating applications; (7) simulating the archivecasting process. According to the invention, a system required by the method comprises a server, a database management software, a database management system, an operation environment, a windows web,and a software development language such as C#, asp.net, and PHP. According to the invention, a scheme can be intelligently provided, and a neural network technology is adopted. The information of 13dimensions is automatically matched based on the scheme and a clear reason is given. Meanwhile, the advantages of accurate content, accurate matching of predicted scores, accurate matching of personal intentions, accurate matching of policies, accurate matching of rules and accurate matching of collision conditions are realized. As a result, an optimal scheme is given. That means, an optimal school, an optimal professional and an optimal scheme are achieved.

The invention relates to an artificial intelligence processor security enhancement system and method based on key path encryption. The artificial intelligence processor security enhancement system iscomposed of an instruction security unit, a data security unit and a universal artificial intelligence processor architecture. On the basis of a universal artificial intelligence processor architecture, an instruction security unit and a data security unit are added to ensure the security of instructions and weights of a neural network model and protect the security of intermediate data processedby an artificial intelligence processor. According to the technology, a private key is transmitted through an asymmetric encryption algorithm to decrypt an encrypted instruction and a weight, the integrity of a neural network model instruction can be verified through a bypass chain type verification method, and the instruction transmission performance of an artificial intelligence processor is notaffected. The system adopts encryption algorithms (e.g., advanced encryption algorithms such as AES-CTR mode) to encrypte a feature map of a critical path, so that the privacy of the feature map is protected, side channel information leakage of an artificial intelligence processor is reduced, and an attacker cannot infer the structure of the neural network model through an access mode of an off-chip DRAM. The method has wide practical value and application prospect.

The invention discloses an artificial intelligence student tutoring system, and relates to the field of artificial intelligence. The system comprises a user, a client, an artificial intelligence server, a knowledge base used for storing knowledge-type data information, and a user base used for storing user data information. The artificial intelligence server is arranged between the client and theknowledge base, a student's parent unit and a teacher unit are established in the artificial intelligence server, and the student's parent unit provides an intelligent answering, testing and materialdownloading platform for students; when there is no answer to a question in the knowledge base, the system sends the question to a related subject teacher in a message mode, and then the teacher helpsto answer the question and stores the answer in the knowledge base. By using the system, the learning interest and learning efficiency of students are improved, the contact between students, teachersand parents is enhanced, and parents can conveniently learn about the learning conditions of students.

The invention relates to the field of automobile intelligent design, in particular to a driving assistance system based on artificial intelligence, which comprises a vehicle condition monitoring unit,an auxiliary control unit, an artificial intelligence unit, a vehicle surrounding environment monitoring unit and an acousto-optic reminder unit. The auxiliary control unit is used for controlling the speed of the vehicle, adjusting the distance from the previous vehicle, changing lanes, overtaking and parking. The artificial intelligence unit is used for processing the received state information, and sends the processed state information to the auxiliary control unit and the acousto-optic reminder unit. The driving assistance system based on artificial intelligence is provided with an artificial intelligence unit, integrates artificial intelligence technology. The information monitored by the vehicle condition monitoring unit and the surrounding environment monitoring unit of the vehicleis analyzed and processed by the artificial intelligence unit, and the vehicle is directly controlled after the analysis and processing is completed, thereby eliminating the driver's reaction, so that the safety is higher.

The invention discloses a method for realizing general artificial intelligence. According to the method, only the similarity relation, the time relation and the space relation between the things needto be extracted, and a relation network between the things can be established under the optimization of a memory and forgetting mechanism. Information such as instinctive motivation, income and loss evaluation is also used as input information and is kept in memory. A machine only needs to arrange relations in memory, reorganize the memory by using the relations, select responses by using an income and loss evaluation system, and realize the responses through simulation, so that general artificial intelligence similar to human beings can be established.

The invention discloses a college aspiration fill-out decision-making method and system based on artificial intelligence. According to the method and the system, on the basis of the artificial intelligence and big data, through intelligent judgment, major and college selection relatively suitable for own features is provided for an examinee. According to the system, college query, decision-makingsupport and the like can be provided when aspiration fill-out is carried out, the examinee is helped to make a scientific decision, an information platform for the aspiration fill-out of the examineeis provided, and the aspiration fill-out scientificity and high efficiency are improved. The college aspiration fill-out decision-making system comprises a college recommendation module, a self-cognition module, a college performance prediction module, a student portrait module, a occupational interest evaluation system module, a college data module, a college admission data module, an AI data module and a self-learning system module based on industries.