Transaction-enabled systems and methods for identifying and acquiring machine resources on a forward resource market are disclosed. An example system may include a controller having a resource requirement circuit to determine an amount of a resource required for a machine to service a task requirement, a forward resource market circuit to access a forward resource market, a resource market circuit to access a resource market, and a resource distribution circuit to execute a transaction of the resource on at least one of the resource market or the forward resource market in response to the determined amount of the resource required.

Systems and methods for prediction of forward market renewable energy credit from automated agent behavioral data are disclosed. An example transaction-enabling system may include a forward market circuit to access a forward energy credit market, and a market forecasting circuit to automatically generate a forecast for a forward market price of an energy credit in the forward energy credit market, based in part on an automated agent behavior collected from an automated agent behavioral data source. The example system may further include wherein the energy credit comprises a renewable energy credit from a renewable energy system, and a smart contract circuit to sell the renewable energy credit or purchase the renewable energy credit on the forward energy credit market in response to the forecasted forward market price.

Transaction-enabled methods for providing provable access to a distributed ledger with a tokenized instruction set for biological production processes are described. A method may include accessing a distributed ledger comprising an instruction set for a biological production process and tokenizing the instruction set. The method may further include interpreting an instruction set access request and, in response to the access request, providing a provable access to the instruction set. The method may further include providing commands to a production tool of the biological production process in response to the instruction set access request and recording the transaction on the distributed ledger.

Systems and transaction-enabled methods for providing provable access to a distributed ledger with serverless code logic are disclosed. A transaction-enabling system may include a controller configured to access a distributed ledger comprising serverless code logic, tokenize the serverless code logic, interpret an access request for the serverless code logic and, in response to the access request, provide a provable access to the serverless code logic.

A motorcycle helmet includes a data processor configured to facilitate communication between a rider wearing the helmet and a motorcycle, the motorcycle and the helmet communicating location and orientation of the motorcycle. An augmented reality system with a display is disposed to facilitate presenting an augmentation of content in an environment of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation of the motorcycle At least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider and the motorcycle.

An example transaction-enabled system may include a smart contract wrapper to access a distributed ledger comprising intellectual property (IP) licensing terms corresponding to IP assets, wherein the IP licensing terms include an apportionment of royalties among owning entities in the distributed ledger. The smart contract wrapper may interpret an IP description value and an IP addition request, and, in response to the IP addition request and the IP description value, to add the apportionment of royalties corresponding to the IP description value. At least one of the plurality of IP assets comprises an instruction set and an operation on the distributed ledger provides provable access to the instruction set. A royalty apportionment wrapper apportions royalties from at least one royalty generating element to owning entities in response to the IP licensing terms.

The present disclosure describes transaction-enabling systems and methods for enabling machine resource transactions. A system can include a machine having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; and a controller. The controller can include a resource requirement circuit to determine an amount of a resource for the machine to service task requirement, a resource market circuit to access a resource market, and a resource distribution circuit to execute a transaction of the resource on the resource market in response to the determined amount of the resource.

Systems and methods for forward market price prediction and sale of energy storage capacity are disclosed. An example transaction-enabling system may include a fleet of machines having an aggregate energy storage capacity; and a controller, comprising: an external data circuit structured to monitor an external data source and collect data from the external data source; an expert system circuit structured to predict a forward market price for energy storage capacity based on the collected data and the aggregate energy storage capacity; and a smart contract circuit structured to automatically sell at least a subset of the aggregate energy storage capacity on a forward market for energy storage capacity in response to the predicted forward market price.

Systems, methods, and devices for pruning a convolutional neural network (CNN). A subset of layers of the CNN is chosen, and for each layer of the subset of layers, how salient each filter in the layer is to an output of the CNN is determined, a subset of the filters in the layer is determined based on the salience of each filter in the layer, and the subset of filters in the layer is pruned. In some implementations, the layers of the subset of layers of the CNN are non-contiguous. In some implementations, the subset of layers includes odd numbered layers of the CNN and excludes even numbered layers of the CNN. In some implementations, the subset of layers includes even numbered layers of the CNN and excludes odd numbered layers of the CNN.

Implementations are described herein for training and applying machine learning models to digital images capturing plants, and to other data indicative of attributes of individual plants captured in the digital images, to recognize individual plants in distinction from other individual plants. In various implementations, a digital image that captures a first plant of a plurality of plants may be applied, along with additional data indicative of an additional attribute of the first plant observed when the digital image was taken, as input across a machine learning model to generate output. Based on the output, an association may be stored in memory, e.g., of a database, between the digital image that captures the first plant and one or more previously-captured digital images of the first plant.