Data labeling software

Data labeling software

Data labeling is the process of identifying raw data and adding informative labels to provide context so that a machine learning model can learn from it.

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Erin Scherfner
Erin Scherfner edited on 20 Dec, 2020
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Data labeling, also referred to as data annotation, is required for a variety of use cases including computer vision, natural language processing, and speech recognition. The goal of data labeling is to provide data that is "marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict."For example, in the use case of computer vision for autonomous vehicles, labeled data might include tagged street signs, pedestrians, or other vehicles. While some unsupervised machine learning models (ex., anomaly detection models) do not rely on annotated data, supervised or semi-supervised "human in the loop (HITL)" models are utilized for a variety of commercalcommercial applications, ranging from autonomous vehicles to facial recognition.

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  • Customer Service (natural language proccessingprocessing application)
Erin Scherfner
Erin Scherfner edited on 19 Dec, 2020
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Synthetically-generated datasets can also be used to train machine learning models, particularly in computer vision. Synthetic data may augment real datasets to cover areas of the data distribution that are not sufficiently represented in order to alleviate dataset bias. Synthetic data may also be useful when real data is impossible or prohibitively difficult to acquire due to privacy or legal issues. Synthetic data has been used to train Google’s Waymo in the form of driving simulations. Facebook was reported to use synthetic data to train algorithms to detect bullying language.

Erin Scherfner
Erin Scherfner edited on 19 Dec, 2020
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Data labeling, also referred to as data annotation, is required for a variety of use cases including computer vision, natural language processing, and speech recognition. The goal of data labeling is to provide data that is "marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict."For example, in the use case of computer vision for autonomous vehicles, labeled data might include tagged street signs, pedestrians, or other vehicles. The process of data labeling can be completed as a time-intensive manual process or it can be automated to various degrees using software. While some unsupervised machine learning models (ex., anomaly detection models) do not rely on annotated data, supervised or semi-supervised "human in the loop (HITL)" models are utilized for a variety of commercal applications, ranging from autonomous vehicles to facial recognition.

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The process of data labeling can be completed as a time-intensive manual process or it can be automated to various degrees using software. Data labeling as a service arises out of the need for companies in a variety of industries to develop large sets of training data for artificial intelligence or machine learning models. In 2019, The Economist referred to “tagged” or labeled data as the “feedstock” for machine learning algorithms.Data labeling can take up to 25% of total of the time required to complete a machine learning project.

Erin Scherfner
Erin Scherfner edited on 19 Dec, 2020
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Data labeling as a service arises out of the need for companies in a variety of industries to develop large sets of training data for artificial intelligence andor machine learning models. In 2019, for example, The Economist referred to “tagged” or labeled data as the “feedstock” for machine learning algorithms.Data labeling can take up to 25% of total of the time required to complete a machine learning project.

Erin Scherfner
Erin Scherfner edited on 19 Dec, 2020
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Data labeling, also referred to as data annotation, is required for a variety of use cases including computer vision, natural language processing, and speech recognition. The goal of data labeling is to provide data that is "marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict."For example, in the use case of computer vision for autonomous vehicles, labeled data might include tagged street signs, pedestrians, or other vehicles. The process of data labeling can be completed as a time-intensive manual process (as with "human in the loop (HITL)" supervised or semi-supervised machine learning) or it can be automated to various degrees using software. While some unsupervised machine learning models (ex., anomaly detection models) do not rely on annotated data, supervised or semi-supervised "human in the loop (HITL)" models are often necessary utilized for a variety of commercal applications.

Erin Scherfner
Erin Scherfner edited on 19 Dec, 2020
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Article

Data labeling, also referred to as data training or data preparation solutions, is required for a variety of use cases including computer vision, natural language processing, and speech recognition. It can be completed as a time-intensive manual process or be automated by software.

Data labeling, also referred to as data annotation, is required for a variety of use cases including computer vision, natural language processing, and speech recognition. The goal of data labeling is to provide data that is "marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict."For example, in the use case of computer vision for autonomous vehicles, labeled data might include tagged street signs, pedestrians, or other vehicles. The process of data labeling can be completed as a time-intensive manual process (as with "human in the loop (HITL)" supervised or semi-supervised machine learning) or it can be automated to various degrees. While some unsupervised machine learning models (ex., anomaly detection models) do not rely on annotated data, supervised or semi-supervised "human in the loop (HITL)" models are often necessary for a variety of commercal applications.

...

Data labeling as a service arises out of the need to develop large sets of training data for artificial intelligence and machine learning models. In 2019, for example, The EconomistThe Economist referred to “tagged” or labeled data as the “feedstock” for machine learning algorithms.Data labeling can take up to 25% of total of the time required to complete a machine learning project.

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  • Customer ServiceCustomer Service (natural language proccessing application)
  • Precision AgriculturePrecision Agriculture (computer vision application)
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Synthetically generatedSynthetically-generated datasets can also be used to train machine learning models, particularly in computer vision. Synthetic data may augment real datasets to cover areas of the data distribution that are not sufficiently represented in order to alleviate dataset bias. Synthetic data may also be useful when real data is impossible or prohibitively difficult to acquire due to privacy or legal issues. Synthetic data has been used to train Google’s Waymo in the form of driving simulations. Facebook was reported to use synthetic data to train algorithms to detect bullying language.

Erin Scherfner
Erin Scherfner edited on 19 Dec, 2020
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Data labeling, also referred to as data training, data annotation, or data preparation solutions, is required for a variety of use cases including computer vision, natural language processing, and speech recognition. It can be completed as a time-intensive manual process or be automated by software.

Erin Scherfner
Erin Scherfner edited on 19 Dec, 2020
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  • Natural Language Processing
  • Computer Vision
  • Customer Service (natural language proccessing application)
  • Precision Agriculture (computer vision application)
  • Micromobility (computer vision application)
Erin Scherfner
Erin Scherfner edited on 19 Dec, 2020
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Other applications
  • Natural Language Processing
  • Computer Vision
Edits on 19 Dec, 2020
Erin Scherfner
Erin Scherfner edited on 19 Dec, 2020
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Data labeling as a service arises out of the need to develop large sets of training data for artificial intelligence and machine learning models. In 2019, for example, The Economist referred to “tagged” or labeled data as the “feedstock” for machine learning algorithmsalgorithms.Data labeling can take up to 25% of total of the time required to complete a machine learning project.

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Erin Scherfner
Erin Scherfner edited on 19 Dec, 2020
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Data labeling, also referred to as data training, data annotation, or data preparation solutions, is required for a variety of use cases including computer vision, natural language processing, and speech recognition. It can be completed as a time-intensive manual process or be automated by software.

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Model operations and monitoring

A market has also emerged adjacent to the data labeling market that aims to reduce bias in large datasets and the models that they subsequently produce. This is part of the Ethical AI movement, which encourages the proactive embedding of diversity and inclusion principles into the AI lifecycle and ensure transparency of artificial intelligence systems and models.

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More recently, the use of synthetic data has supplemented the data labeling process. Synthetic data is “generated through computer programs, instead of being composed through the documentation of real-world events”).

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Synthetically generated datasets can also be used to train machine learning models, particularly in computer vision. Synthetic data mymay augment real datasets to cover partsareas of the data distribution that are not sufficiently represented in order to alleviate dataset bias. Synthetic data may also be useful when real data is impossible or prohibitively difficult to acquire due to privacy or legal issues. Synthetic data has been used to train Google’s Waymo in the form of driving simulations. Facebook was reported to use synthetic data to train algorithms to detect bullying language.

Model operations and monitoring

A market has also emerged, adjacent to the data labeling market, that aims to ensure proper oversight over models and reduce bias in large datasets. This is part of the Ethical AI movement, which encourages the proactive embedding of diversity and inclusion principles into the AI lifecycle and aims to ensure transparency of AI systems.

Erin Scherfner
Erin Scherfner approved a suggestion from Golden's AI on 18 Dec, 2020
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Uber acquired Mighty AI on June 25, 2019 in an effort to improve its self-driving algorithms. Scale.AI's customers include many other self-driving and general transport companies, including WaymoWaymo, Lyft, Zoox, Cruise, and the Toyota Research Institute. Waymo, Argo AI, and Lyft have also open sourced their self-driving datasets. A "high-quality" vehicle dataset includes:

Erin Scherfner
Erin Scherfner approved a suggestion from Golden's AI on 18 Dec, 2020
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Uber acquired Mighty AI on June 25, 2019 in an effort to improve its self-driving algorithms. Scale.AI's customers include many other self-driving and general transport companies, including Waymo, Lyft, ZooxZoox, Cruise, and the Toyota Research Institute. Waymo, Argo AI, and Lyft have also open sourced their self-driving datasets. A "high-quality" vehicle dataset includes:

Erin Scherfner
Erin Scherfner approved a suggestion from Golden's AI on 18 Dec, 2020
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Uber acquired Mighty AI on June 25, 2019 in an effort to improve its self-driving algorithms. Scale.AI's customers include many other self-driving and general transport companies, including Waymo, Lyft, Zoox, Cruise, and the Toyota Research InstituteToyota Research Institute. Waymo, Argo AI, and Lyft have also open sourced their self-driving datasets. A "high-quality" vehicle dataset includes:

Erin Scherfner
Erin Scherfner edited on 18 Dec, 2020
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Erin Scherfner
Erin Scherfner approved a suggestion from Golden's AI on 18 Dec, 2020
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More recently, the use of synthetic data has supplemented the data labeling process. Synthetic data is “generated through computer programscomputer programs, instead of being composed through the documentation of real-world events”).

Erin Scherfner
Erin Scherfner approved a suggestion from Golden's AI on 18 Dec, 2020
Edits made to:
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Data labeling is required for a variety of use cases including computer visioncomputer vision, natural language processing, and speech recognition. It can be completed as a time-intensive manual process or be automated by software.

Erin Scherfner
Erin Scherfner approved a suggestion from Golden's AI on 18 Dec, 2020
Edits made to:
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Article

Synthetically generated datasets can also be used to train machine learning models, particularly in computer vision. Synthetic data my augment real datasets to cover parts of the data distribution that are not sufficiently represented to alleviate dataset bias. Synthetic data may also be useful when real data is impossible or prohibitively difficult to acquire due to privacy or legal issues. Synthetic data has been used to train Google’s Waymo in the form of driving simulations. FacebookFacebook was reported to use synthetic data to train algorithms to detect bullying language.

Erin Scherfner
Erin Scherfner approved a suggestion from Golden's AI on 18 Dec, 2020
Edits made to:
Article (+6/-6 characters)
Article

Synthetically generated datasets can also be used to train machine learning models, particularly in computer vision. Synthetic data my augment real datasets to cover parts of the data distribution that are not sufficiently represented to alleviate dataset bias. Synthetic data may also be useful when real data is impossible or prohibitively difficult to acquire due to privacy or legal issues. Synthetic data has been used to train GoogleGoogle’s Waymo in the form of driving simulations. Facebook was reported to use synthetic data to train algorithms to detect bullying language.

Erin Scherfner
Erin Scherfner approved a suggestion from Golden's AI on 18 Dec, 2020
Edits made to:
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Data labeling as a service arises out of the need to develop large sets of training data for artificial intelligence and machine learningmachine learning models. In 2019, for example, The Economist referred to “tagged” or labeled data as the “feedstock” for machine learning algorithms.

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