X Development LLC

Skip Innovations, Inc.

Google Inc.

Georgios Evangelopoulos

Bin Ni

Philip Edwin Watson

Sarah Ann Laszlo

Olivia Hatalsky

Qianyu Zhang

Elliott J Rouse

Kathryn Jane Zealand

Benoit M Schillings

Owen Lewis

Grigory Bronevetsky

Benoit Schillings

Rohan Badlani

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an exosuit activity transition control structure. In some implementations, sensor data for a powered exosuit is received. The sensor data is classified depending on whether the sensor data is indicative of a transition between different types of activities of a wearer of the powered exosuit. The classification is provided to a control system for the powered exosuit. The powered exosuit is controlled based on the classification.

Exosuit activity transition control

Techniques are described herein for using artificial intelligence to 'learn,' statistically, a target programming style that is imposed in and/or evidenced by a code base. Once the target programming style is learned, it can be used for various purposes. In various implementations, one or more generative adversarial networks ('GANs'), each including a generator machine learning model and a discriminator machine learning model, may be trained to facilitate learning and application of target programming style(s). In some implementations, the discriminator(s) and/or generator(s) may operate on graphical input, and may take the form of graph neural networks (&#x201c;GNNs&#x201d;), graph attention neural networks (&#x201c;GANNs&#x201d;), graph convolutional networks (&#x201c;GCNs&#x201d;), etc., although this is not required.

Learning and using programming styles

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a neural network architecture for performing a machine learning task. In one aspect, a method comprises: obtaining data defining a synaptic connectivity graph representing synaptic connectivity between neurons in a brain of a biological organism; generating data defining a plurality of candidate graphs based on the synaptic connectivity graph; determining, for each candidate graph, a performance measure on a machine learning task of a neural network having a neural network architecture that is specified by the candidate graph; and selecting a final neural network architecture for performing the machine learning task based on the performance measures.

Neural architecture search based on synaptic connectivity graphs

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a reservoir computing neural network. In one aspect there is provided a reservoir computing neural network comprising: (i) a brain emulation sub-network, and (ii) a prediction sub-network. The brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input. The prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output. The values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusting during training of the reservoir computing neural network.

Reservoir computing neural networks based on synaptic connectivity graphs

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an artificial neural network architecture corresponding to a sub-graph of a synaptic connectivity graph. In one aspect, there is provided a method comprising: obtaining data defining a graph representing synaptic connectivity between neurons in a brain of a biological organism; determining, for each node in the graph, a respective set of one or more node features characterizing a structure of the graph relative to the node; identifying a sub-graph of the graph, comprising selecting a proper subset of the nodes in the graph for inclusion in the sub-graph based on the node features of the nodes in the graph; and determining an artificial neural network architecture corresponding to the sub-graph of the graph.

Predicting neuron types based on synaptic connectivity graphs

Implementations are described herein for generating embeddings of source code using both the language and graph domains, and leveraging combinations of these semantically-rich and structurally-informative embeddings for various purposes. In various implementations, tokens of a source code snippet may be applied as input across a sequence-processing machine learning model to generate a plurality of token embeddings. A graph may also be generated based on the source code snippet. A joint representation may be generated based on the graph and the incorporated token embeddings. The joint representation generated from the source code snippet may be compared to one or more other joint representations generated from one or more other source code snippets to make a determination about the source code snippet.

Generating and using joint representations of source code

Implementations are described herein for automatically identifying, recommending, and/or automatically effecting changes to a source code base based on updates previously made to other similar code bases. Intuitively, multiple prior 'migrations,' or mass updates, of complex software system code bases may be analyzed to identify changes that were made. More particularly, a particular portion or 'snippet' of source code&#x2014;which may include a whole source code file, a source code function, a portion of source code, or any other semantically-meaningful code unit&#x2014;may undergo a sequence of edits over time. Techniques described herein leverage this sequence of edits to predict a next edit of the source code snippet. These techniques have a wide variety of applications, including but not limited to automatically updating of source code, source code completion, recommending changes to source code, etc.

Growing community of inventors

Venice, CA, United States of America

Georgios Evangelopoulos