Abstract: A computer-implemented method may include generating an optimistically executable echo state network (ESN), including a reservoir, input layer, and output layer, each with its own group of neurons and synapses; training the optimistically executable ESN, including updating a weight of each output synapse; and performing inference calculation via the optimistically executable ESN. During the inference calculation, at least one neuron may determine, based on a sequence of synapses messages received at the neuron, whether the neuron processed the sequence of synapse messages out of order. If the neuron processed the sequence of synapse messages out of order, the neuron may reverse-compute at least one computation performed by the neuron and perform the at least one computation based on receipt of the sequence of synapse messages in a correct order as determined by the timestamp of each synapse message in the sequence of synapse messages.

Abstract: A computer-implemented method is disclosed. The method may include providing, in a computer-readable storage device, a dataspace. The dataspace may include a plurality of logical segments, and dataspace data stored in the plurality of logical segments. The method may include receiving, from a data source external to the dataspace, a data artifact. The data artifact may include a plurality of data elements. The method may include processing, at a plurality of data filters, the plurality of data elements. A data filter of the plurality of data filters may process a data element of the plurality of data elements, based on a structure of the plurality of the data elements, or a configuration of the data filter. The method may include generating, based on the processed plurality of data elements, processed data. The method may include sending the processed data to the dataspace.

Abstract: A computer-implemented method is disclosed. The method may include generating a first deep learning model configuration and calculating a first result metric for the first deep learning model configuration. The method may include selecting a first sample space based on the first deep learning model configuration. The method may include generating a second deep learning model configuration. The second deep learning model configuration may be within the first sample space. The method may include calculating a second result metric for the second deep learning model configuration. The method may include, in response to the second result metric exceeding the first result metric, selecting a second sample space. The second sample space may be based on the second deep learning model configuration. The method may include, in response to the second result metric not exceeding the first result metric, reducing the size of the first sample space.

Abstract: A computer-implemented method is disclosed. The method may include receiving, at a neuron of an artificial neural network, a sequence of synapse messages. Each synapse message may include a timestamp of the time the synapse message was sent. The method may include determining, based on the timestamp of each synapse message, whether the neuron processed the sequence of synapse messages out of order with respect to the timestamps. The method may include, in response to the neuron processing the sequence of synapse messages out of order, reverse-computing at least one computation performed by the neuron in response to processing the sequence of synapse messages out of order. The method may include performing the at least one computation based on receipt of the sequence in a correct order as determined by the timestamp of each synapse message in the sequence of synapse messages.