Goldman Sachs &co. LLC

Other

Extendabrain Corporation

Paul Burchard

Ulrich Drepper

Anthony Daoud

Vladimir M Zakharov

Dominic Dotterrer

Rahul Sharma

Andrew F Phillips

Francis Giannaros

Fabiano Romeiro

Raj Mahajan

Marco Argenti

Lachlan Maxwell

Brandon Oren Amir

Sahir Pathan

Kunal Baxi

An oracle that is fast enough to publish data to the blockchain in a timely manner while remaining decentralized and robust to the failure of any one part. A blockchain node may receive data to introduce to the blockchain from the oracle, determine whether the data was provided by a primary party or parties designated as an oracle by a DAO. If so, the node may process the data from the one or more primary parties and introduce the processed data to the blockchain.

Fast robust oracles via decentralized autonomous organizations

A method includes obtaining first and second data sets to be reconciled and, using matching rules, identifying discrepancies between the data sets. The matching rules include at least one permutation key, where each permutation key identifies a subset of data to be grouped together in one of the data sets. Identifying the discrepancies includes attempting to match one or more first characteristics associated with the grouped subset of data in one of the data sets to one or more second characteristics associated with another of the data sets. The matching rules could involve multiple matching characteristics, and the matching rules could be generated using a metric to select the matching characteristics of the matching rules. The metric could be based on a combination of a number of matched data items and a number of matched groups of data items.

System and method for reconciliation of data in multiple systems using permutation matching

A method for compliance with Know Your Customer (KYC) and other regulations includes a pseudonymous globally unique identifier stored on a blockchain that associates a pseudonymous first party address with a globally unique identifier representing the vetted identity of the owner of the address. The method also includes a trusted third party issuing a verifiable credential for a first pseudonymous party to a proposed transaction to a second pseudonymous party to the transaction.

Pseudonymous transactions on blockchains compliant with know your customer regulations and reporting requirements

A method is provided for solving a computational problem that is reducible to a problem of counting solutions to an associated decision problem. The method includes, using a quantum computer, estimating a number of the solutions to the decision problem by determining if there is at least one solution to the decision problem that lies in a pseudo-random set. The method also includes outputting or using the estimated number of the solutions to the decision problem as a solution to the computational problem. Determining if there is at least one solution to the decision problem that lies in the pseudo-random set could include determining if there is a sequence of solutions to the decision problem that, taken together, lies in the pseudo-random set.

System and method for performing fast computations using quantum counting and pseudo-random sets

A smart contract including one or more rules is defined and stored on a blockchain in conjunction with a pool of one or more tokens owned by the smart contract. A plurality of conflicting claims to the pool of one or more tokens is received and provided to the blockchain. An indication that a trigger condition is met is received from the blockchain. The trigger condition to determined using the one or more rules. Information indicating a distribution of the one or more tokens to resolve the plurality of conflicting claims is also received. The information is generated using the one or more rules in response to the trigger condition being met.

Blockchain with joint claims on tokens

An empirical approach to providing differential privacy includes applying a common statistical query to a set of databases to produce sample values, both with and without any particular entity's data. The probability density is empirically estimated by sorting the sample values to generate an empirical cumulative distribution function. The cumulative distribution function is differenced across approximately the square root of the number of sample points to get an empirical density function. The statistical query is empirically (&#x3b5;,&#x3b4;)-private if the empirical densities with and without any particular individual differ by a factor of no more than exp(&#x3b5;), with the exception of a set for which the densities exceed that bound by a total of no more than &#x3b4;.

Empirically providing data privacy with reduced noise

An apparatus includes multiple computing cores, where each computing core is configured to perform one or more processing operations and generate input data. The apparatus also includes multiple coprocessors associated with each computing core, where each coprocessor is configured to receive the input data from at least one of the computing cores, process the input data, and generate output data. The apparatus further includes multiple reducer circuits, where each reducer circuit is configured to receive the output data from each of the coprocessors of an associated computing core, apply one or more functions to the output data, and provide one or more results to the associated computing core. In addition, the apparatus includes multiple communication links communicatively coupling the computing cores and the coprocessors associated with the computing cores.

General-purpose parallel computing architecture

A method includes using a computational network to learn and predict time-series data. The computational network includes one or more layers, each having an encoder and a decoder. The encoder of each layer multiplicatively combines (i) current feed-forward information from a lower layer or a computational network input and (ii) past feedback information from a higher layer or that layer. The encoder of each layer generates current feed-forward information for the higher layer or that layer. The decoder of each layer multiplicatively combines (i) current feedback information from the higher layer or that layer and (ii) at least one of the current feed-forward information from the lower layer or the computational network input or past feed-forward information from the lower layer or the computational network input. The decoder of each layer generates current feedback information for the lower layer or a computational network output.

Systems and methods for learning and predicting time-series data using deep multiplicative networks

An apparatus includes at least one processor configured to obtain a graph having vertices that represent items and edges that represent relationships between the items. The at least one processor is also configured to identify pairwise relatedness values associated with pairs of vertices. Each pairwise relatedness value is determined as a measure of diffusion on a space in the graph with a first vertex acting as a diffusion source and a boundary acting as a diffusion sink such that a diffusion density at a second vertex defines the pairwise relatedness value associated with the first and second vertices. The at least one processor is further configured to use the pairwise relatedness values as a measure of how the items associated with the pairs of vertices in the graph are related to one another. A boundary condition defines the boundary on the space in the graph around the diffusion source.

Determining relatedness of data using graphs to support machine learning, natural language parsing, search engine, or other functions

A method includes performing, with at least one processing device, natural language understanding by iteratively (i) generating a semantic word and clause representation and (ii) generating a syntax. The generation of the semantic word and clause representation and the generation of the syntax occur iteratively such that (i) semantics are calculated from syntax by aggregating weights of syntactically-labeled context in which words or clauses appear and (ii) syntax is calculated from semantics by grouping common pairs of words or clauses with similar semantic relations, thereby producing a self-consistent coupled notion of syntax and semantics.

System and method for coupled detection of syntax and semantics for natural language understanding and generation

A method is provided for solving a computational problem that is reducible to a problem of counting solutions to an associated decision problem. The method includes estimating a number of the solutions to the decision problem using a quantum computer by determining if there is at least one simultaneous solution to both (i) the decision problem and (ii) an associated hashing problem. The method also includes increasing a precision of the estimated number of the solutions to the decision problem using the quantum computer by determining if there are multiple solutions to the decision problem that are simultaneously solutions to the associated hashing problem. The method further includes outputting or using the estimated number of the solutions to the decision problem as a solution to the computational problem.

System and method for performing fast computations using quantum counting based on simultaneous solutions to decision problem and associated hashing problem

A method for solving a computational problem reducible to a problem of summing probabilities over all solutions to a decision problem includes using a quantum computer to identify a coarse estimate of a sum of the probabilities over all solutions to the decision problem. The method also includes using the quantum computer to identify a finer estimate of the sum. The finer estimate is determined using a quantum amplitude estimation algorithm in which a number m in a number register controls a number R&#xd7;m of quantum amplitude amplification iterations to be applied to a solution space register (where R is a specified multiple) and a quantum Fourier transform is applied to the number register. The method further includes using the coarse estimate to de-alias the finer estimate over all solutions. In addition, the method includes outputting a solution to the computational problem determined using the de-aliased finer estimate.

Systems and methods for performing counting and summing using a quantum computer

A method includes using a computational network to learn and predict time-series data. The computational network is configured to receive the time-series data and perform transformation-invariant encoding of the time-series data. The computational network includes one or more encoding layers. The method also includes feeding back future predictions of the time-series data through inertial adjustments of transformations. The inertial adjustments preserve the invariants in the computational network. The computational network could further include one or more pooling layers each configured to reduce dimensionality of data, where the one or more pooling layers provide the transformation invariance for the encoding.

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Jersey City, NJ, United States of America

Paul Burchard