International Business Machines Corporation

Venkata Sitaramagiridharganesh Ganapavarapu

Roman Vaculin

Kanthi K Sarpatwar

Karthikeyan Shanmugam

Gi-Joon Nam

James Thomas Rayfield

Richard Harold Boivie

Nianjun Zhou

Enriquillo Valdez

Wesley M Gifford

Kyong Min Yeo

Saket Sathe

Jinwook Jung

Kanthi Sarpatwar

A computer-implemented method, a computer program product, and a computer system for adaptive retraining of an artificial intelligence model. A computer system computes drift magnitude scores for respective drift functions. A computer system computes an aggregated data drift score for a data drift, an aggregated concept drift score for a concept drift, and an aggregated model drift score for a model drift. A computer system computes an overall drift score, based on the aggregated data drift score, the aggregated concept drift score, the aggregated model drift score, a predetermined data drift threshold, a predetermined concept drift threshold, and a predetermined model drift threshold. A computer system determines whether retraining of the artificial intelligence model is required, based on the overall drift score. A computer system performs the retraining of the artificial intelligence model, in response to determining the retraining of the artificial intelligence model is required.

Adaptive retraining of an artificial intelligence model by detecting a data drift, a concept drift, and a model drift

A method, computer system, and a computer program product for managing digital assets is provided. The present invention may include receiving, in a second blockchain network, an authentication request to determine an authenticity of a new digital asset. The present invention may include determining, in the second blockchain network, the authenticity of the new digital asset. The present invention may include updating, in the second blockchain network, the authenticity of the new digital asset as authentic in a shared library. The present invention may include generating, in the second blockchain network, an update authenticity transaction, wherein the update authenticity transaction is sent to a first blockchain network.

Tracing and verifying a shared library using blockchain

An example operation may include one or more of generating, by a training participant client, a plurality of transaction proposals, each of the plurality of transaction proposals corresponding to a training iteration for machine learning model training related to stochastic gradient descent, the machine learning model training comprising a plurality of training iterations, the transaction proposals comprising a gradient calculation performed by the training participant client, transferring the plurality of transaction proposals to one or more endorser nodes or peers each comprising a verify gradient smart contract, executing, by each of the endorser nodes or peers, the verify gradient smart contract; and providing endorsements corresponding to the plurality of transaction proposals to the training participation client.

Efficient verification of machine learning applications

An example operation may include one or more of generating, by a plurality of training participant clients, gradient calculations for machine learning model training, each of the training participant clients comprising a training dataset, converting, by a training aggregator coupled to the plurality of training participant clients, the gradient calculations to a plurality of transaction proposals, receiving, by one or more endorser nodes or peers of a blockchain network, the plurality of transaction proposals, executing, by each of the endorser nodes or peers, a verify gradient smart contract, and providing endorsements corresponding to the plurality of transaction proposals to the training aggregator.

Aggregated machine learning verification for database

An example operation may include one or more of generating, by a training participant client comprising a training dataset, a plurality of transaction proposals that each correspond to a training iteration for machine learning model training related to stochastic gradient descent, the machine learning model training comprising a plurality of training iterations, the transaction proposals comprising a gradient calculation performed by the training participant client, a batch from the private dataset, a loss function, and an original model parameter, receiving, by one or more endorser nodes of peers of a blockchain network, the plurality of transaction proposals, and evaluating each transaction proposal.

An example operation includes one or more of computing, by a data owner node, updated gradients on a loss function based on a batch of private data and previous parameters of a machine learning model associated with a blockchain, encrypting, by the data owner node, update information, recording, by the data owner, the encrypted update information as a new transaction on the blockchain, and providing the update information for an audit.

Verification of stochastic gradient descent

A method verifies an authenticity, integrity, and provenance of outputs from steps in a process flow. One or more processor(s) validate one or more inputs to each step in a process flow by verifying at least one of a hash and a digital signature of each of the one or more inputs. The processor(s) then generate digital signatures that cover outputs of each step and the one or more inputs to each step, such that the digital signatures result in a chain of digital signatures that are used to verify an authenticity, an integrity and a provenance of outputs of the one or more steps in the process flow.

Electronically verifying a process flow

Aspects of the present disclosure describe techniques for detecting anomalous data in an encrypted data set. An example method generally includes receiving a data set of encrypted data points. A tree data structure having a number of levels is generated for the data set. Each level of the tree data structure generally corresponds to a feature of the encrypted plurality of features, and each node in the tree data structure at a given level represents a probability distribution of a likelihood that each data point is less than or greater than a split value determined for a given feature. An encrypted data point is received for analysis, and anomaly score is calculated based on a probability identified for each of the plurality of encrypted features. Based on determining that the calculated anomaly score exceeds a threshold value, the encrypted data point is identified as potentially anomalous.

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Peekskill, NY, United States of America

Venkata Sitaramagiridharganesh Ganapavarapu