Abstract: Systems and methods for inspecting or maintaining a structure, such as a utility pole or a metal tower), with an unmanned aerial vehicle (UAV) are disclosed. The UAV can be configured to fly to a target location on the structure and measure characteristics of the structure such as a component thickness. The UAV can precisely repeat the measurement at a later time to determine any change in component thickness.

Abstract: Systems and methods for inspecting or maintaining a structure, such as a utility pole or a metal tower), with an unmanned aerial vehicle (UAV) are disclosed. The UAV can be configured to fly to a target location on the structure and measure characteristics of the structure such as a component thickness. The UAV can precisely repeat the measurement at a later time to determine any change in component thickness.

Abstract: Systems and methods for inspecting or maintaining a structure, such as a utility pole or a metal tower), with an unmanned aerial vehicle (UAV) are disclosed. The UAV can be configured to fly to a target location on the structure and measure characteristics of the structure such as a component thickness. The UAV can precisely repeat the measurement at a later time to determine any change in component thickness.

Abstract: Systems, apparatuses, and methods for representing words or phrases, and using the representation to perform NLP and NLU tasks, where these tasks include sentiment analysis, question answering, and conference resolution. Embodiments introduce a type of deep contextualized word representation that models both complex characteristics of word use, and how these uses vary across linguistic contexts. The word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. These representations can be added to existing task models and significantly improve the state of the art across challenging NLP problems, including question answering, textual entailment and sentiment analysis.

Abstract: Systems, apparatuses, and methods for representing words or phrases, and using the representation to perform NLP and NLU tasks, where these tasks include sentiment analysis, question answering, and conference resolution. Embodiments introduce a type of deep contextualized word representation that models both complex characteristics of word use, and how these uses vary across linguistic contexts. The word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. These representations can be added to existing task models and significantly improve the state of the art across challenging NLP problems, including question answering, textual entailment and sentiment analysis.