X Development LLC

Matthew Sibigtroth

Matthew David Day

Bin Ni

Sarah Ann Laszlo

Johan Ulrich Lewin Jessen

Michael Patrick Bauerly

Nicholas John Foster

Ivan Grubisic

Preeti Murali Talwai

Jonathan Souliere

Johan Jessen

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying one or more regions of a brain of a biological organism that are predicted to be functionally-specialized for performing a task. In one aspect, a method comprises: obtaining data defining a synaptic connectivity graph representing synaptic connectivity between neurons in the brain of the biological organism; identifying a plurality of sub-graphs of the synaptic connectivity graph; determining, for each sub-graph of the plurality of sub-graphs, a performance measure characterizing a performance of a neural network having a neural network architecture that is specified by the sub-graph in accomplishing the task; and determining, based on the performance measures, that one or more sub-graphs of the plurality of sub-graphs correspond to regions of the brain of the biological organism that are predicted to be functionally-specialized for performing the task.

Neural functional localization using synaptic connectivity graphs

A method of providing decisions to user queries includes: receiving, by at least one processor, a query transmitted from a client device; identifying, from a query database, a first query model; applying, by the at least one processor, the identified first query model to multiple user experience data entries from a user experience database, to obtain at least one correlation resulting from the application of the first query model to the multiple user experience data entries; and identifying, by the at least one processor, an answer to the query from the at least one correlation.

Personalized decision engine

Perception housing for a robot

Techniques are provided to estimate of location or position of objects that are depicted in an image of a scene. Some implementations include obtaining an image of a scene; identifying an object within the image of the scene; obtaining a three-dimensional model that corresponds to the object that was identified within the image of the scene, the three-dimensional model being obtained from the database of three-dimensional models; determining, based on data from the three-dimensional model, an estimated depth of the object within the scene; generating or updating a three-dimensional representation of the scene based at least on the estimated depth of the object within the scene; and providing the three-dimensional representation of the scene, including at least a portion of the three-dimensional representation of the scene that was generated or updated based on the three-dimensional model of the object, to the scene analyzer.

Scene recognition using volumetric substitution of real world objects

After running a simulation on a biological cell, a simulation system displays a circular viewer for presenting simulation data. The circular viewer is a graphical element which contains a plurality of circular graphical elements, wherein each circular graphical element displays simulation data of one biological category ordered around the circular graphical element. Responsive to a user input, the circular viewer updates the circular graphical elements to visually indicate subsets of simulation data in each graphical element that are above a threshold differential from a baseline cell state of the biological cell. The circular viewer may additionally display connectors linking portions of simulation data from different circular graphical elements. Moreover, the circular viewer may update to display simulation data in the circular graphical elements over a plurality of time steps over which the simulation has occurred.

Whole cell circular delta viewer and navigator

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Richmond, CA, United States of America

Matthew Sibigtroth