\"Human-in-the-loop (HITL) is a branch of AI that leverages both human and machine intelligence to create machine learning models. In a traditional human-in-the-loop approach, people are involved in a virtuous circle where they train, tune, and test a particular algorithm. Generally, it works like this:\"
\n\"Label data. This gives a model high quality (and high quantities of) training data. A machine learning algorithm learns to make decisions from this data.\"
\n\"Tune the model. This can happen in several different ways, but commonly, humans will score data to account for overfitting, to teach a classifier about edge cases, or new categories in the model’s purview.\"
\n\"Test and validate a model by scoring its outputs, especially in places where an algorithm is unconfident about a judgment or overly confident about an incorrect decision.\"
\n\"Those actions comprises a continuous feedback loop. Human-in-the-loop machine learning means taking each of these training, tuning, and testing tasks and feeding them back into the algorithm so it gets smarter, more confident, and more accurate. This can be especially effective when the model selects what it needs to learn next–known as active learning–and you send that data to human annotators for training.\"
\nhttps://appen.com/blog/human-in-the-loop/
\n\"Active learning generally refers to the humans handling low confidence units and feeding those back into the model. Human-in-the-loop is broader, encompassing active learning approaches as well as the creation of data sets through human labeling. Additionally, HitL can sometimes (though rarely) refer to people simply validating (or invalidating) an output without feeding those judgments back to the model.\"
\nhttps://appen.com/blog/human-in-the-loop/
\n\"HITL refers to systems that allow humans to give direct feedback to a model for predictions below a certain level of confidence.\"
\n\"In practice, you need to determine what level of confidence is acceptable for the process: If it is ok to have wrong predictions \"slipping through\", you can set threshold rather low – which, in turn, requires much less manual intervention through human labor. In other cases, you want to be sure that the system only records \"correct\" predictions.\"
\n\"Why can't we just use better algorithms to achieve higher confidence?\"
\n\"The field of AI has seen great technological advances . The fundamental problem of this is that training data is hard to get by as it requires human expertise. And while there are many public datasets available, they generally don't exist for your specific problems. Hence, they have to be created.\"
\n\"In order not to spend 3 years building a dataset, it is possible to already start training a model and using it sooner. In many cases, this already leads to considerable productivity gains.\"
\nhttps://levity.ai/blog/human-in-the-loop
\n\"In supervised learning, experts use labeled data sets to train algorithms to produce appropriate functions. These can then help to map new examples. Doing this will allow the algorithm to correctly determine functions for unlabeled data.\"
\n\"In unsupervised learning, unlabeled datasets are fed to the algorithms. Thus, they need to learn on their own to find a structure in the unlabeled data and memorize it accordingly. This falls under the human-in-the-loop deep learning approach.\"
\nhttps://levity.ai/blog/human-in-the-loop
\n\"Where there is a lack of data, machine learning models are not very useful.\"
\n\"Healthcare as an example. A 2018 Stanford study found that a human-in-the-loop AI model works better than AI on its own or human doctors on their own.\"
\n\"These systems can help improve accuracy while maintaining human-level standards of work. This can apply to many industries and change the way people work all across the world!\"
\nhttps://levity.ai/blog/human-in-the-loop
\nAuthors: Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, Liang He
\narXiv:2108.00941 [cs.LG]
\n\"Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for computers in the pipeline with the help of machine-based approaches.\"
\n\" In this paper, the authors surveyed existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) the work of improving model performance from data processing, (2) the work of improving model performance through interventional model training, and (3) the design of the system independent human-in-the-loop. \"
\n\"That survey intends to provide a high-level summarization for human-in-the-loop and motivates interested readers to consider approaches for designing effective human-in-the-loop solutions.\"
\nhttps://arxiv.org/abs/2108.00941
\n\"The AI lifecycle moves from proof of concept to proof of scale, then to model in production. This is a cyclical process, and people play an important role. Humans in the loop (HITL) inspect, validate, and make changes to algorithms to improve outcomes. They also collect, label, and conduct quality control (QC) on data. The benefits of humans in the loop begin with model development and extend across the AI lifecycle, from proof of concept to model in production.\"
\n\"Poor utilization of people in the AI lifecycle is costly and yields low-quality data and poor model performance. It is best to consider workforce early in the AI lifecycle, and before model development begins, to achieve higher quality outcomes.\"
\nhttps://www.cloudfactory.com/human-in-the-loop
\n\"Human-in-the-loop or HITL is defined as a model that requires human interaction. HITL is associated with modeling and simulation (M&S) in the live, virtual, and constructive taxonomy. HITL models may conform to human factors requirements as in the case of a mockup.\"
\n\"In this type of simulation a human is always part of the simulation and consequently influences the outcome in such a way that is difficult if not impossible to reproduce exactly. HITL also readily allows for the identification of problems and requirements that may not be easily identified by other means of simulation.\"
\n\"HITL is often referred to as interactive simulation, which is a special kind of physical simulation in which physical simulations include human operators, such as in a flight or a driving simulator.\"
\nhttps://en.wikipedia.org/wiki/Human-in-the-loop
\nFine-grained categorization with humans in the loop
\nEmbedding human experts’ knowledge into computational models
Machine teaching
Interpretable fine-grained models
https://www.kaggle.com/c/herbarium-2022-fgvc9/discussion/307777
", "messages": [ { "id": 1692199, "postDate": "2022-02-15T22:22:21.417Z", "content": "\"Human-in-the-loop (HITL) is a branch of AI that leverages both human and machine intelligence to create machine learning models. In a traditional human-in-the-loop approach, people are involved in a virtuous circle where they train, tune, and test a particular algorithm. Generally, it works like this:\"
\n\"Label data. This gives a model high quality (and high quantities of) training data. A machine learning algorithm learns to make decisions from this data.\"
\n\"Tune the model. This can happen in several different ways, but commonly, humans will score data to account for overfitting, to teach a classifier about edge cases, or new categories in the model’s purview.\"
\n\"Test and validate a model by scoring its outputs, especially in places where an algorithm is unconfident about a judgment or overly confident about an incorrect decision.\"
\n\"Those actions comprises a continuous feedback loop. Human-in-the-loop machine learning means taking each of these training, tuning, and testing tasks and feeding them back into the algorithm so it gets smarter, more confident, and more accurate. This can be especially effective when the model selects what it needs to learn next–known as active learning–and you send that data to human annotators for training.\"
\nhttps://appen.com/blog/human-in-the-loop/
\n\"Active learning generally refers to the humans handling low confidence units and feeding those back into the model. Human-in-the-loop is broader, encompassing active learning approaches as well as the creation of data sets through human labeling. Additionally, HitL can sometimes (though rarely) refer to people simply validating (or invalidating) an output without feeding those judgments back to the model.\"
\nhttps://appen.com/blog/human-in-the-loop/
\n\"HITL refers to systems that allow humans to give direct feedback to a model for predictions below a certain level of confidence.\"
\n\"In practice, you need to determine what level of confidence is acceptable for the process: If it is ok to have wrong predictions \"slipping through\", you can set threshold rather low – which, in turn, requires much less manual intervention through human labor. In other cases, you want to be sure that the system only records \"correct\" predictions.\"
\n\"Why can't we just use better algorithms to achieve higher confidence?\"
\n\"The field of AI has seen great technological advances . The fundamental problem of this is that training data is hard to get by as it requires human expertise. And while there are many public datasets available, they generally don't exist for your specific problems. Hence, they have to be created.\"
\n\"In order not to spend 3 years building a dataset, it is possible to already start training a model and using it sooner. In many cases, this already leads to considerable productivity gains.\"
\nhttps://levity.ai/blog/human-in-the-loop
\n\"In supervised learning, experts use labeled data sets to train algorithms to produce appropriate functions. These can then help to map new examples. Doing this will allow the algorithm to correctly determine functions for unlabeled data.\"
\n\"In unsupervised learning, unlabeled datasets are fed to the algorithms. Thus, they need to learn on their own to find a structure in the unlabeled data and memorize it accordingly. This falls under the human-in-the-loop deep learning approach.\"
\nhttps://levity.ai/blog/human-in-the-loop
\n\"Where there is a lack of data, machine learning models are not very useful.\"
\n\"Healthcare as an example. A 2018 Stanford study found that a human-in-the-loop AI model works better than AI on its own or human doctors on their own.\"
\n\"These systems can help improve accuracy while maintaining human-level standards of work. This can apply to many industries and change the way people work all across the world!\"
\nhttps://levity.ai/blog/human-in-the-loop
\nAuthors: Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, Liang He
\narXiv:2108.00941 [cs.LG]
\n\"Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for computers in the pipeline with the help of machine-based approaches.\"
\n\" In this paper, the authors surveyed existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) the work of improving model performance from data processing, (2) the work of improving model performance through interventional model training, and (3) the design of the system independent human-in-the-loop. \"
\n\"That survey intends to provide a high-level summarization for human-in-the-loop and motivates interested readers to consider approaches for designing effective human-in-the-loop solutions.\"
\nhttps://arxiv.org/abs/2108.00941
\n\"The AI lifecycle moves from proof of concept to proof of scale, then to model in production. This is a cyclical process, and people play an important role. Humans in the loop (HITL) inspect, validate, and make changes to algorithms to improve outcomes. They also collect, label, and conduct quality control (QC) on data. The benefits of humans in the loop begin with model development and extend across the AI lifecycle, from proof of concept to model in production.\"
\n\"Poor utilization of people in the AI lifecycle is costly and yields low-quality data and poor model performance. It is best to consider workforce early in the AI lifecycle, and before model development begins, to achieve higher quality outcomes.\"
\nhttps://www.cloudfactory.com/human-in-the-loop
\n\"Human-in-the-loop or HITL is defined as a model that requires human interaction. HITL is associated with modeling and simulation (M&S) in the live, virtual, and constructive taxonomy. HITL models may conform to human factors requirements as in the case of a mockup.\"
\n\"In this type of simulation a human is always part of the simulation and consequently influences the outcome in such a way that is difficult if not impossible to reproduce exactly. HITL also readily allows for the identification of problems and requirements that may not be easily identified by other means of simulation.\"
\n\"HITL is often referred to as interactive simulation, which is a special kind of physical simulation in which physical simulations include human operators, such as in a flight or a driving simulator.\"
\nhttps://en.wikipedia.org/wiki/Human-in-the-loop
\nFine-grained categorization with humans in the loop
\nEmbedding human experts’ knowledge into computational models
Machine teaching
Interpretable fine-grained models
https://www.kaggle.com/c/herbarium-2022-fgvc9/discussion/307777
", "rawMarkdown": "#Human-in-the-Loop and Machine Learning (Appen Blog)\n\n\"Human-in-the-loop (HITL) is a branch of AI that leverages both human and machine intelligence to create machine learning models. In a traditional human-in-the-loop approach, people are involved in a virtuous circle where they train, tune, and test a particular algorithm. Generally, it works like this:\"\n\n\"Label data. This gives a model high quality (and high quantities of) training data. A machine learning algorithm learns to make decisions from this data.\"\n\n\"Tune the model. This can happen in several different ways, but commonly, humans will score data to account for overfitting, to teach a classifier about edge cases, or new categories in the model’s purview.\"\n\n\"Test and validate a model by scoring its outputs, especially in places where an algorithm is unconfident about a judgment or overly confident about an incorrect decision.\"\n\n\"Those actions comprises a continuous feedback loop. Human-in-the-loop machine learning means taking each of these training, tuning, and testing tasks and feeding them back into the algorithm so it gets smarter, more confident, and more accurate. This can be especially effective when the model selects what it needs to learn next–known as active learning–and you send that data to human annotators for training.\"\n\nhttps://appen.com/blog/human-in-the-loop/\n\n#Difference between human-in-the-loop and active learning\n\n\"Active learning generally refers to the humans handling low confidence units and feeding those back into the model. Human-in-the-loop is broader, encompassing active learning approaches as well as the creation of data sets through human labeling. Additionally, HitL can sometimes (though rarely) refer to people simply validating (or invalidating) an output without feeding those judgments back to the model.\"\n\nhttps://appen.com/blog/human-in-the-loop/\n\n# More training data = better performance\n\n\"HITL refers to systems that allow humans to give direct feedback to a model for predictions below a certain level of confidence.\"\n\n\"In practice, you need to determine what level of confidence is acceptable for the process: If it is ok to have wrong predictions \"slipping through\", you can set threshold rather low – which, in turn, requires much less manual intervention through human labor. In other cases, you want to be sure that the system only records \"correct\" predictions.\"\n\n\"Why can't we just use better algorithms to achieve higher confidence?\"\n\n\"The field of AI has seen great technological advances . The fundamental problem of this is that training data is hard to get by as it requires human expertise. And while there are many public datasets available, they generally don't exist for your specific problems. Hence, they have to be created.\"\n\n\"In order not to spend 3 years building a dataset, it is possible to already start training a model and using it sooner. In many cases, this already leads to considerable productivity gains.\"\n\nhttps://levity.ai/blog/human-in-the-loop\n\n#Humans and machines - Human-in-the-loop Deep Learning approach.\n\n\"In supervised learning, experts use labeled data sets to train algorithms to produce appropriate functions. These can then help to map new examples. Doing this will allow the algorithm to correctly determine functions for unlabeled data.\"\n\n\"In unsupervised learning, unlabeled datasets are fed to the algorithms. Thus, they need to learn on their own to find a structure in the unlabeled data and memorize it accordingly. This falls under the human-in-the-loop deep learning approach.\"\n\nhttps://levity.ai/blog/human-in-the-loop\n\n#Improving the accuracy of rare datasets\n\n\"Where there is a lack of data, machine learning models are not very useful.\"\n\n\"Healthcare as an example. A 2018 Stanford study found that a human-in-the-loop AI model works better than AI on its own or human doctors on their own.\"\n\n\"These systems can help improve accuracy while maintaining human-level standards of work. This can apply to many industries and change the way people work all across the world!\"\n\nhttps://levity.ai/blog/human-in-the-loop\n\n#A Survey of Human-in-the-loop for Machine Learning\n\nAuthors: Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, Liang He\n\narXiv:2108.00941 [cs.LG]\n\n\"Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for computers in the pipeline with the help of machine-based approaches.\"\n\n\" In this paper, the authors surveyed existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) the work of improving model performance from data processing, (2) the work of improving model performance through interventional model training, and (3) the design of the system independent human-in-the-loop. \"\n\n\"That survey intends to provide a high-level summarization for human-in-the-loop and motivates interested readers to consider approaches for designing effective human-in-the-loop solutions.\"\n\nhttps://arxiv.org/abs/2108.00941\n\n#Accelerating the AI Lifecycle\n\n\"The AI lifecycle moves from proof of concept to proof of scale, then to model in production. This is a cyclical process, and people play an important role. Humans in the loop (HITL) inspect, validate, and make changes to algorithms to improve outcomes. They also collect, label, and conduct quality control (QC) on data. The benefits of humans in the loop begin with model development and extend across the AI lifecycle, from proof of concept to model in production.\"\n\n\"Poor utilization of people in the AI lifecycle is costly and yields low-quality data and poor model performance. It is best to consider workforce early in the AI lifecycle, and before model development begins, to achieve higher quality outcomes.\"\n\nhttps://www.cloudfactory.com/human-in-the-loop\n\n#Human-in-the-loop (HITL) Definition\n\n\"Human-in-the-loop or HITL is defined as a model that requires human interaction. HITL is associated with modeling and simulation (M&S) in the live, virtual, and constructive taxonomy. HITL models may conform to human factors requirements as in the case of a mockup.\"\n\n\"In this type of simulation a human is always part of the simulation and consequently influences the outcome in such a way that is difficult if not impossible to reproduce exactly. HITL also readily allows for the identification of problems and requirements that may not be easily identified by other means of simulation.\"\n\n\"HITL is often referred to as interactive simulation, which is a special kind of physical simulation in which physical simulations include human operators, such as in a flight or a driving simulator.\"\n\nhttps://en.wikipedia.org/wiki/Human-in-the-loop\n\n#Herbarium 2022 Kaggle Competition. Topics of interest include: Human-in-the-loop\n\nFine-grained categorization with humans in the loop\n\n- Embedding human experts’ knowledge into computational models\n\n- Machine teaching\n\n- Interpretable fine-grained models\n\nhttps://www.kaggle.com/c/herbarium-2022-fgvc9/discussion/307777", "votes": 11 } ], "comments": [], "raw_markdown_by_id": { "1692199": "#Human-in-the-Loop and Machine Learning (Appen Blog)\n\n\"Human-in-the-loop (HITL) is a branch of AI that leverages both human and machine intelligence to create machine learning models. In a traditional human-in-the-loop approach, people are involved in a virtuous circle where they train, tune, and test a particular algorithm. Generally, it works like this:\"\n\n\"Label data. This gives a model high quality (and high quantities of) training data. A machine learning algorithm learns to make decisions from this data.\"\n\n\"Tune the model. This can happen in several different ways, but commonly, humans will score data to account for overfitting, to teach a classifier about edge cases, or new categories in the model’s purview.\"\n\n\"Test and validate a model by scoring its outputs, especially in places where an algorithm is unconfident about a judgment or overly confident about an incorrect decision.\"\n\n\"Those actions comprises a continuous feedback loop. Human-in-the-loop machine learning means taking each of these training, tuning, and testing tasks and feeding them back into the algorithm so it gets smarter, more confident, and more accurate. This can be especially effective when the model selects what it needs to learn next–known as active learning–and you send that data to human annotators for training.\"\n\nhttps://appen.com/blog/human-in-the-loop/\n\n#Difference between human-in-the-loop and active learning\n\n\"Active learning generally refers to the humans handling low confidence units and feeding those back into the model. Human-in-the-loop is broader, encompassing active learning approaches as well as the creation of data sets through human labeling. Additionally, HitL can sometimes (though rarely) refer to people simply validating (or invalidating) an output without feeding those judgments back to the model.\"\n\nhttps://appen.com/blog/human-in-the-loop/\n\n# More training data = better performance\n\n\"HITL refers to systems that allow humans to give direct feedback to a model for predictions below a certain level of confidence.\"\n\n\"In practice, you need to determine what level of confidence is acceptable for the process: If it is ok to have wrong predictions \"slipping through\", you can set threshold rather low – which, in turn, requires much less manual intervention through human labor. In other cases, you want to be sure that the system only records \"correct\" predictions.\"\n\n\"Why can't we just use better algorithms to achieve higher confidence?\"\n\n\"The field of AI has seen great technological advances . The fundamental problem of this is that training data is hard to get by as it requires human expertise. And while there are many public datasets available, they generally don't exist for your specific problems. Hence, they have to be created.\"\n\n\"In order not to spend 3 years building a dataset, it is possible to already start training a model and using it sooner. In many cases, this already leads to considerable productivity gains.\"\n\nhttps://levity.ai/blog/human-in-the-loop\n\n#Humans and machines - Human-in-the-loop Deep Learning approach.\n\n\"In supervised learning, experts use labeled data sets to train algorithms to produce appropriate functions. These can then help to map new examples. Doing this will allow the algorithm to correctly determine functions for unlabeled data.\"\n\n\"In unsupervised learning, unlabeled datasets are fed to the algorithms. Thus, they need to learn on their own to find a structure in the unlabeled data and memorize it accordingly. This falls under the human-in-the-loop deep learning approach.\"\n\nhttps://levity.ai/blog/human-in-the-loop\n\n#Improving the accuracy of rare datasets\n\n\"Where there is a lack of data, machine learning models are not very useful.\"\n\n\"Healthcare as an example. A 2018 Stanford study found that a human-in-the-loop AI model works better than AI on its own or human doctors on their own.\"\n\n\"These systems can help improve accuracy while maintaining human-level standards of work. This can apply to many industries and change the way people work all across the world!\"\n\nhttps://levity.ai/blog/human-in-the-loop\n\n#A Survey of Human-in-the-loop for Machine Learning\n\nAuthors: Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, Liang He\n\narXiv:2108.00941 [cs.LG]\n\n\"Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for computers in the pipeline with the help of machine-based approaches.\"\n\n\" In this paper, the authors surveyed existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) the work of improving model performance from data processing, (2) the work of improving model performance through interventional model training, and (3) the design of the system independent human-in-the-loop. \"\n\n\"That survey intends to provide a high-level summarization for human-in-the-loop and motivates interested readers to consider approaches for designing effective human-in-the-loop solutions.\"\n\nhttps://arxiv.org/abs/2108.00941\n\n#Accelerating the AI Lifecycle\n\n\"The AI lifecycle moves from proof of concept to proof of scale, then to model in production. This is a cyclical process, and people play an important role. Humans in the loop (HITL) inspect, validate, and make changes to algorithms to improve outcomes. They also collect, label, and conduct quality control (QC) on data. The benefits of humans in the loop begin with model development and extend across the AI lifecycle, from proof of concept to model in production.\"\n\n\"Poor utilization of people in the AI lifecycle is costly and yields low-quality data and poor model performance. It is best to consider workforce early in the AI lifecycle, and before model development begins, to achieve higher quality outcomes.\"\n\nhttps://www.cloudfactory.com/human-in-the-loop\n\n#Human-in-the-loop (HITL) Definition\n\n\"Human-in-the-loop or HITL is defined as a model that requires human interaction. HITL is associated with modeling and simulation (M&S) in the live, virtual, and constructive taxonomy. HITL models may conform to human factors requirements as in the case of a mockup.\"\n\n\"In this type of simulation a human is always part of the simulation and consequently influences the outcome in such a way that is difficult if not impossible to reproduce exactly. HITL also readily allows for the identification of problems and requirements that may not be easily identified by other means of simulation.\"\n\n\"HITL is often referred to as interactive simulation, which is a special kind of physical simulation in which physical simulations include human operators, such as in a flight or a driving simulator.\"\n\nhttps://en.wikipedia.org/wiki/Human-in-the-loop\n\n#Herbarium 2022 Kaggle Competition. Topics of interest include: Human-in-the-loop\n\nFine-grained categorization with humans in the loop\n\n- Embedding human experts’ knowledge into computational models\n\n- Machine teaching\n\n- Interpretable fine-grained models\n\nhttps://www.kaggle.com/c/herbarium-2022-fgvc9/discussion/307777" } }