Abstract: An information processing apparatus according to an embodiment of the present technology includes a first learning unit, a second learning unit, an evaluation unit, and an adjustment unit. The first learning unit causes a predetermined learning model to perform learning. The second learning unit causes a conversion model to perform learning, the conversion model converting an output of the predetermined learning model into a rule group described in a format that can be interpreted by a user. The evaluation unit acquires evaluation information obtained by evaluating the rule group in accordance with a predetermined standard. The adjustment unit adjusts learning processing of the predetermined learning model on the basis of the evaluation information.

Abstract: An information processing apparatus (1) includes a learning unit (32), a calculation unit (33), and a presentation unit (34). The learning unit (32) learns the first model based on predetermined new data acquired from a terminal device (100) possessed by the user and the second model based on joined data obtained by joining shared data stored in advance in the storage unit (4) as additional data with the new data. The calculation unit (33) calculates the improvement degree indicating the degree of improvement in the output precision of the second model to the output of the first model. The presentation unit (34) generates predetermined presentation information based on the improvement degree calculated by the calculation unit (33).

Abstract: There is provided an information processing method, an information processing device, and a program that facilitates a security measure for a machine learning model or an API for using the machine learning model, the information processing system including one or more information processing devices controls a user interface for performing a setting related to security of a machine learning model, and generates the machine learning model corresponding to content set via the user interface. The present technology can be applied to, for example, a system that generates and discloses, for example, a machine learning model or an API for using the machine learning model.

Abstract: An information processing apparatus (100) according to the present disclosure includes: a control unit (130) that acquires a past case including a past prediction target and an analysis data set used for predictive analysis for the prediction target, acquires data to be used for predictive analysis, extracts a prediction target in a case of performing the predictive analysis by using the data based on the data and the past case, and constructs, based on the data, a data set to be used for the predictive analysis for the extracted prediction target.

Abstract: The present technology relates to an information processing method, an information processing device, and a program capable of improving prediction accuracy of a prediction model. An information processing system including one or more information processing devices performs training of the prediction model on the basis of prediction data used for predictive analysis using the prediction model and learning data. Furthermore, the information processing system including one or more information processing devices performs the predictive analysis on the basis of the prediction model trained on the basis of the learning data and the prediction data, and the prediction data. The present technology can be applied to, for example, a system that performs the predictive analysis for various services.

Abstract: An information processing device including: an input unit to which a first data set including a plurality of pieces of data is input; a determination unit that determines processing applied when a prediction model based on a second data set similar to the first data set is generated; and a prediction model generation unit that generates a prediction model based on the first data set by applying the processing determined by the determination unit to the first data set.

Abstract: To make it possible to more suitably suppress increasing medical expenses. There is provided a medical information processing system including: an acquisition section (110 and 122); and a calculation section (122). The acquisition section (110 and 122) acquires examination information and system information. The examination information pertains to an examination from which an examination deliverable regarding medical care is generated. The system information pertains to each of a plurality of estimation systems. The plurality of estimation systems each estimates a symptom of a subject on the basis of the examination deliverable. The calculation section (122) calculates a use priority of each of the plurality of estimation systems on the basis of the examination information and the system information.

Abstract: There is provided a medical information processing system that includes: an acquisition unit (110) that acquires an examination arrangement request that includes examination type information that indicates a type of medical-related examination, and user information regarding a user; a presentation unit (123) that presents, to the user, an examination resource capable of performing the examination, based on the examination arrangement request and the user information; a management unit (222) that manages a result of the examination performed on the user in association with the user information; and an output unit (224) that outputs the result based on input performed by the user.

Abstract: A configuration is realized that inputs terminal-acquired information of a mobile terminal in a vehicle to a learning model, estimates a driver's driving behavior, and performs processes such as calculating a score on the basis of an estimation result and giving a notice. Terminal-acquired information such as acceleration information acquired by the mobile terminal in the vehicle is input, and a process of estimating a driving behavior of the driver of the vehicle is performed. A driving behavior estimate of the driver and estimation reliability of the driving behavior estimate are calculated on the basis of the terminal-acquired information by applying a learning model.

Abstract: The present disclosure relates to an information processing apparatus, an information processing method, and a program that allow improvement of a learning data set to be facilitated. A prediction analysis section calculates an evaluation value for an evaluation data set used to evaluate a prediction model, for a predetermined number of data samples in a learning data set used for training of the prediction model, and on the basis of the evaluation value for all the data samples in the learning data set and gradients of the data samples, an advice generation section generates presentation information for presenting advice related to at least one of the data samples in the learning data set or feature amounts of the data samples. A technique according to the present disclosure can be applied to prediction of a contract price of a previously used condominium, for example.

Abstract: There is provided an information processing apparatus as a mechanism capable of more appropriately specifying reasons of prediction by a prediction model, the information processing apparatus including a control unit configured to extract a characteristic amount set from characteristic amounts included in a plurality of pieces of input data input to a prediction model configured by a non-linear model, in which an absolute value of a degree of contribution of the extracted characteristic amount set to a prediction result by the prediction model is equal to or greater than a first threshold, and an absolute value of a degree of contribution of a characteristic amount set obtained by excluding arbitrary one of the characteristic amounts from the extracted characteristic amount set to a prediction result by the prediction model is equal to or less than a second threshold.

Abstract: There is provided an information processing apparatus and an information processing method allowing variations of scenes of various events to be realized in a simulator environment simulating the real world. A reward providing unit provides rewards to a first agent and a second agent taking action in the simulator environment and learning an action decision rule according to the reward for the action. The first agent is provided with the reward in accordance with a prescribed reward definition. The second agent is provided with the reward in accordance with an opposing reward definition opposing the prescribed reward definition, the opposing reward definition causing a resultant reward to be increased in a case where the second agent acts to bring about a situation where the reward for the first agent is reduced and causing a resultant reward to be reduced in a case the reward for the first agent is increased.

Abstract: There is provided a learning apparatus and a learning method that allow a reinforcement learning model to be easily corrected on the basis of user input. A display control section causes a display section to display reinforcement learning model information regarding a reinforcement learning model. A correcting section corrects the reinforcement learning model on the basis of user input to the reinforcement learning model information. The present disclosure can be applied to, for example, a personal computer (PC) and the like that correct a reinforcement learning model on the basis of input from a user and perform reinforcement learning of a movement policy of an agent using the corrected reinforcement learning model.

Abstract: The present invention is directed to a new joint estimation framework employing MAP estimation based on pixel-based latent variables for tissue types. The method combines the geometrical information described by latent MRF, statistical relation between tissue types and P-C coefficients, and Poisson noise models of PCD data, and makes possible the continuous Baysian estimation from detected photon counts. The proposed method has better accuracy and RMSE than the method using FBP and thresholding. The joint estimation framework has the potential to further improve the accuracy by introducing more information about tissues in human body, e.g., the location, size, and number of tissues, or limited variation of neighboring tissues, which will be easily formulated by pixel-based latent variables.

Abstract: The present invention is directed to a new joint estimation framework employing MAP estimation based on pixel-based latent variables for tissue types. The method combines the geometrical information described by latent MRF, statistical relation between tissue types and P-C coefficients, and Poisson noise models of PCD data, and makes possible the continuous Baysian estimation from detected photon counts. The proposed method has better accuracy and RMSE than the method using FBP and thresholding. The joint estimation framework has the potential to further improve the accuracy by introducing more information about tissues in human body, e.g., the location, size, and number of tissues, or limited variation of neighboring tissues, which will be easily formulated by pixel-based latent variables.