International Business Machines Corporation

Thomas S Hubregtsen

Anne Elizabeth Gattiker

Samuel Jonathan Thomas

Sujatha Kashyap

Minh Ngoc Binh Nguyen

Kaipeng Li

Bishop Chapman Brock

Frank Liu

Inseok Hwang

Jinho Lee

Christopher McCall Durham

H Peter Hofstee

An example operation may include one or more of receiving an example in a blockchain network, distributing the example to a plurality of endorsing peers of the blockchain network, performing, by one or more of the endorsing peers, automated analysis of the example to determine an inference for the example, determining if there is a consensus of inference amongst the plurality of endorsing peers, and committing the example to a blockchain of the blockchain network when there is a consensus of inference.

Verified permissioned blockchains

Examples of techniques for constructing, evaluating, and improving a search string for retrieving images are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method includes receiving, by a processing device, a plurality of images as search results returned based at least in part on a search string for an item in the form of a tuple including an item class, an action and an actor. The method further includes determining, by the processing device, whether the search string is effective at indicating a common item use based on image similarity. The method further includes, based at least in part on determining that the search string is ineffective at indicating the item use, generating, by the processing device, an alternative search string.

Constructing, evaluating, and improving a search string for retrieving images indicating item use

Authenticity of Artificial Intelligence (AI) results may be verified by creating, for an AI system, from a plurality of original inputs to form a plurality of original inference results, a plurality of original signatures of representative elements of an internal state of the AI system constructed from each individual original inference result of the plurality of original inference results. During deployment of the AI system, a matching of a plurality of deployment time inference results with a plurality of deployment time signatures, to the plurality of original signatures and the plurality of original inference results, may be verified.

Deployment verification of authenticity of machine learning results

Examples of techniques for constructing, evaluating, and improving a search string for retrieving images are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method includes receiving, by a processing device, a plurality of images returned as results to an executed search string in the form of a tuple including an item class, an action and an actor. The method further includes generating, by the processing device, a collection similarity score by comparing at least one of the plurality of images to at least one other of the plurality of images. The method further includes determining, by the processing device, whether the collection similarity score exceeds a collection threshold. The method further includes, based at least in part on determining that the similarity score does not exceed the collection threshold, indicating that the search string is ineffective at indicating the item use.

Examples of techniques for constructing, evaluating, and improving a search string for retrieving images are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method includes receiving, by a processing device, an item identifier. The method further includes retrieving, by the processing device, an item description based at least in part on the item identifier. The method further includes identifying, by the processing device, a tuple indicating a common item use based at least in part on the item description. The method further includes constructing, by the processing device, a search string based at least in part on the tuple. The method further includes retrieving, by the processing device, at least one image based at least in part on the search string.

Aspects of the invention include receiving data that includes a group of n symbols to be written to a plurality of storage units. The group of symbols is mapped into a codeword of n+k symbols, each assigned to one of the storage units. The codeword is configured to allow at least one of the n symbols to be reconstructed using a subset of the n+k symbols. At least one of the n+k symbols is assigned to one of the storage units based at least in part on content of the at least one of the n+k symbols. Writing each of the n+k symbols to its assigned storage unit in the plurality of storage units is initiated. The writing includes optimizing storage capacity of the assigned storage unit based at least in part on determining that the symbol has the same content as another symbol previously stored in the storage unit.

System combining efficient reliable storage and deduplication

Examples of techniques for constructing, evaluating, and improving a search string for retrieving images are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method includes constructing, by a processing device, a search string based at least in part on a tuple including an item class, an action, and an actor. The method further includes retrieving, by the processing device, a plurality of images based at least in part on the search string for an item. The method further includes evaluating, by the processing device, the retrieved plurality of images based on a similarity to determine whether the search string is effective at indicating a common item use. The method further includes, based at least in part on determining that the search string is ineffective at indicating the item use, generating, by the processing device, an alternative search string.

An apparatus comprising a memory and a processor coupled to the memory. The processor receives input from a user, processes a first portion of the input via more than one service module while receiving a second portion of the input to determine a first speculative result, wherein processing the first portion of the input comprises executing at least one service module coupled to a corresponding speculation buffer, processes a second portion of the input via the more than one service module to determine a second speculative result, wherein processing the second portion of the input comprises executing the at least one service module coupled to the corresponding speculation buffer, processes the input via the more than one service module to determine a final output, wherein processing the input comprises executing the at least one service module coupled to the corresponding speculation buffer, and outputs the final output to the user.

Growing community of inventors

Hassfurt, Germany

Thomas S Hubregtsen