Abstract: A technique is directed toward controlling placement of workloads of an application within an application environment. The technique involves, while a first placement of workloads of the application is in a first deployment of resources within the application environment, generating a set of resource deployment changes that accommodates a predicted change in demand on the application. The technique further involves adjusting the first deployment of resources within the application environment to form a second deployment of resources within the application environment, the second deployment of resources being different from the first deployment of resources. The technique further involves providing a second placement of workloads of the application in the second deployment of resources to accommodate the predicted change in demand on the application, the second placement of workloads being different from the first placement of workloads.

Abstract: Methods, systems, and computer program products for multi-objective driven refactoring of a monolith application using reinforcement learning are provided herein. A computer-implemented method includes obtaining multiple code modules of a monolith application and a plurality of conflicting metrics for determining a set of microservices for the monolith application; performing a reinforcement learning-based clustering process that iteratively generates a plurality of clusters comprising the code modules based at least in part on feedback provided for the plurality of conflicting metrics at each iteration; generating candidate microservices for the monolith application, wherein each candidate microservice corresponds to a different one of the plurality of clusters; and outputting the generated candidate microservices to at least one of a system and a user.

Abstract: Methods, systems, and computer program products for factchecking artificial intelligence models using blockchain are provided herein. A computer-implemented method includes obtaining at least one artificial intelligence model and at least one set of data related to the at least one artificial intelligence model; determining a set of characteristics based at least in part on the at least one artificial intelligence model and the at least one set of data; selecting one of a plurality of networks based at least in part on a target deployment of the at least one artificial intelligence model to verify the set of characteristics; generating a report based at least in part on verifying the set of characteristics using the selected network, wherein the report establishes a threshold level of trust for the at least one artificial intelligence model; and storing the report on a blockchain.

Abstract: An approach to real-time multi-agent visual question answering in a decentralized environment. The approach can include initializing a plurality of agents based on data associated with an image and a question about the image. The approach can include identifying a first portion of the plurality of agents to construct a grid of agents based on similarity between the first portion of agents. The approach can include determining a second portion of agents of the first portion of agents with edges between agents allowing sharing of agent concepts and agent parameters. The approach can include generate training trajectories of the grid of agents based on executing a collaborative rollout. Additionally, the approach can include optimizing agent policies of the grid of agents.

Abstract: An example operation may include one or more of receiving, by a monitoring peer, data collection requirements from a model builder node, querying, by the monitoring peer, a blockchain of a plurality of blockchains for data sets that match the data collection requirements, providing, by the monitoring peer, the data sets to the model builder node to be tested, receiving, by the monitoring peer, a performance feedback on the data sets from the model builder node; and updating, by the monitoring peer, a smart contract associated with the data sets on the blockchain based on the performance feedback.

Abstract: An example operation may include one or more of recording an option associated with an event in a block of a blockchain, receiving a request to consume the option from a client based on a future outcome with respect to the event, receiving, via chaincode, outcome data that is associated with the event, and in response to detecting that the future outcome has occurred based on the outcome data, activating the request to consume the option.

Abstract: An example operation may include one or more of receiving, by a monitoring peer, data collection requirements from a model builder node, querying, by the monitoring peer, a blockchain of a plurality of blockchains for data sets that match the data collection requirements, providing, by the monitoring peer, the data sets to the model builder node to be tested, receiving, by the monitoring peer, a performance feedback on the data sets from the model builder node; and updating, by the monitoring peer, a smart contract associated with the data sets on the blockchain based on the performance feedback.