公开(公告)号：US20250028995A1

公开(公告)日：2025-01-23

申请号：US18224889

申请日：2023-07-21

Applicant: X Development LLC

Inventor： Rishabh Singh ,  David Andre ,  Garrett Raymond Honke ,  Falak Shah ,  Nisarg Vyas ,  Jayendra Parmar ,  Brian M. Rosen ,  Shaili Trivedi

IPC: G06N20/00

Abstract: Disclosed implementations relate to adding “bottleneck” models to machine learning pipelines that already apply domain models to translate and/or transfer representations of high-level semantic concepts between domains. In various implementations, an initial representation in a first domain of a transition from an initial state of an environment to a goal state of the environment may be processed based on a pre-trained first domain encoder to generate a first embedding that semantically represents the transition. The first embedding may be processed based on one or more bottleneck models to generate a second embedding with fewer dimensions than the first embedding. In various implementations, the second embedding may be processed in various ways to train one or more of the bottleneck model(s) based on various different auxiliary loss functions.

公开(公告)号：US20230359789A1

公开(公告)日：2023-11-09

申请号：US18142472

申请日：2023-05-02

Applicant: X Development LLC

Inventor： David Andre ,  Rishabh Singh ,  Rebecca Radkoff ,  Yu-Ann Madan ,  Nisarg Vyas ,  Jayendra Parmar ,  Falak Shah ,  Shaili Trivedi

IPC: G06F30/27 ,  G10L15/183

CPC classification number: G06F30/27 ,  G10L15/183

Abstract: As opposed to a rigid approach, implementations disclosed herein utilize a flexible approach in automatically determining an action set to utilize in attempting performance of a task that is requested by natural language input of a user. The approach is flexible at least in that embedding technique(s) and/or action model(s), that are utilized in generating action set(s) from which the action set to utilize is determined, are at least selectively varied. Put another way, implementations leverage a framework via which different embedding technique(s) and/or different action model(s) can at least selectively be utilized in generating different candidate action sets for given NL input of a user. Further, one of those action sets can be selected for actual use in attempting real-world performance of a given task reflected by the given NL input. The selection can be based on a suitability metric for the selected action set and/or other considerations.