公开(公告)号：US20240135691A1

公开(公告)日：2024-04-25

申请号：US18493018

申请日：2023-10-23

Applicant: X Development LLC

Inventor： Avery Noam Cowan ,  Nikhil Suresh ,  Akshina Gupta ,  David Andre ,  Eliot Julien Cowan

IPC: G06V10/776 ,  G06V10/74

CPC classification number: G06V10/776 ,  G06V10/761

Abstract: Methods, systems, and apparatus for obtaining input features representative of a region of space, processing an input comprising the input features through the ML model to generate a prediction describing predicted features of the region of space, obtaining result features describing the region of space, determining a value of at least one evaluation metric that relates the predicted features and the result features, that at least one evaluation metric including one of a distance score, a pyramiding density error, and min-max intersection over union (IOU) score, and training the ML model responsive to the at least one evaluation metric. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

公开(公告)号：US11902858B2

公开(公告)日：2024-02-13

申请号：US17726647

申请日：2022-04-22

Applicant: X DEVELOPMENT LLC

Inventor： David Andre ,  Erich Karl Nachbar

IPC: H04W4/029 ,  H04B17/318 ,  H04W52/36 ,  G01S5/02

CPC classification number: H04W4/029 ,  G01S5/0284 ,  H04B17/318 ,  H04W52/367

Abstract: The technology enables locating asset tracking tags based on a ramped sequence of signals from one or more beacon tracking tags. The sequence includes at least one minimum power signal and at least one maximum power signal. Each signal in the sequence has a tag identifier and an initial signal strength value. Each beacon signal in the ramped sequence is associated with the time at which that beacon signal was received by a reader. Each beacon signal is also associated with a received signal strength at reception. A location of the beacon tracking tag is estimated according to the signals in the sequence based on the difference between the initial and received signal strengths. A position of the reader device is identified based on the beacon tag's location. An asset tracking tag location is identified based on the reader's location and packets received by the reader from the asset tag.

公开(公告)号：US20230117297A1

公开(公告)日：2023-04-20

申请号：US18047510

申请日：2022-10-18

Applicant: X Development LLC

Inventor： Ryan Butterfoss ,  David Andre ,  Salil Vijaykumar Pradhan ,  Rebecca Radkoff

IPC: G06F30/27 ,  G06Q10/06 ,  G06Q50/28

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatic generation of a supply chain simulation. The methods, systems, and apparatus include actions of obtaining supply chain data of a supply chain, generating a supply chain network graph that represents relationships between locations indicated by the supply chain data, determining classifications of the locations indicated by the supply chain data, determining agent rule models based on the supply chain data, and generating a supply chain simulation based on the supply chain network graph, the classifications of the locations, and the agent rule models.

公开(公告)号：US20230018088A1

公开(公告)日：2023-01-19

申请号：US17945376

申请日：2022-09-15

Applicant: X Development LLC

Inventor： Rishabh Singh ,  David Andre ,  Bin Ni ,  Owen Lewis

IPC: G06F8/71 ,  G06F40/20 ,  G06N20/00

Abstract: Implementations are described herein for using machine learning to perform various tasks related to migrating source code based on relatively few (“few shots”) demonstrations. In various implementations, an autoregressive language model may be conditioned based on demonstration tuple(s). In some implementations, a demonstration tuple may include a pre-migration version of a first source code snippet and a post-migration version of the first source code snippet. In other implementations, demonstration tuples may include other data, such as intermediate forms (e.g., natural language descriptions or pseudocode), input-output pairs demonstrating intended behavior, etc. The autoregressive language model may be trained on corpora of source code and natural language documentation on the subject of computer programming. A pre-migration version of a source code file may be processed based on the conditioned autoregressive language model, and a post-migration version may be generated based on output generated based on the conditioned autoregressive model.

公开(公告)号：US20220414419A1

公开(公告)日：2022-12-29

申请号：US17362446

申请日：2021-06-29

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  David Andre ,  Rednar Rosique Rodriquez

IPC: G06N3/00 ,  G06N3/08 ,  G06N3/04

Abstract: In one aspect, there is provided a method performed by one or more data processing apparatus for selecting actions to be performed by an agent interacting with an environment, the method including, at each of multiple time steps, receiving an observation characterizing a current state of the environment at the time step, providing an input including the observation to an action selection neural network having a brain emulation sub-network with an architecture that is based on synaptic connectivity between biological neurons in a brain of a biological organism, processing the input including the observation characterizing the current state of the environment at the time step using the action selection neural network having the brain emulation sub-network to generate an action selection output, and selecting an action to be performed by the agent at the time step based on the action selection output.

公开(公告)号：US20220202348A1

公开(公告)日：2022-06-30

申请号：US17139144

申请日：2020-12-31

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  David Andre ,  Doris Tang ,  Farooq Ahmad

IPC: A61B5/00 ,  G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing brain emulation neural networks on user devices. One of the methods includes obtaining, by a first component of a user device, a network input; processing, by the first component of the user device, the network input using an artificial neural network to generate a network output, wherein the artificial neural network has a network architecture that has been determined according to a synaptic connectivity graph, wherein the synaptic connectivity graph represents synaptic connectivity between neurons in a brain of a biological organism; and providing the network output for use by one or more second components of the user device.