Boost Accuracy with Amazon Augmented AI

Table of Contents

• Introduction
• The Current State of Machine Learning
  • Machine Learning Algorithms
  • Use Cases for Machine Learning
  • The Role of Human Review in Machine Learning
• Use Cases for Human and AI Collaboration
  • Media Analysis
  • Subtitle Generation
  • Document Processing
  • Image Moderation
  • Model Monitoring
• Challenges in the Integration of Humans and ML
  • Talent Requirements
  • Development and Maintenance
  • Accuracy of Human Review
• Introducing Amazon Augmented AI (A2I)
  • Implementing Human Review in ML Workflows
  • Fast Model Deployment
  • Flexibility in Workforcce Options
  • Integration with Amazon Recognition and Amazon Textract
  • Providing Highly Accurate Results
• Customer Use Case: Ripcord
  • Robotic Digitization of Paper Records
  • Leveraging Amazon A2I for Data Annotation and Review
  • Increasing Accuracy and Lowering Costs
  • Streamlined Workflow and Workforce Options
• Leveraging Amazon A2I for Oil and Gas Documents
  • Document Examples and Key Fields
  • Template Customization and Review Workforce
  • Extracting and Reviewing High-Quality Data
  • Leveraging Geolocation Mapping Technology
• Conclusion
• Frequently Asked Questions

Introduction

In today's world, machine learning (ML) plays a significant role in various applications and industries. However, ML models often produce probabilistic outputs that require human review and validation. Customers are looking to combine the power of ML and human judgment to achieve high accuracy and ensure the right decisions are made. This article introduces Amazon Augmented AI (A2I), a service that enables the seamless integration of human review in ML workflows. We will explore the current state of machine learning and discuss the use cases for human and AI collaboration. Additionally, we will highlight a customer use case from Ripcord, a robotics digitization company, and demonstrate how Amazon A2I can be leveraged for extracting and reviewing data from oil and gas documents.

The Current State of Machine Learning

Machine learning is a set of algorithms that learn by example, enabling predictions and decision-making based on Patterns in data. Customers are increasingly using machine learning in various applications due to its scalability, low cost, and speed. While machine learning models handle many use cases effectively, they often provide probabilistic outputs. These probabilistic outputs require human review to ensure accuracy and make nuanced decisions when needed.

Machine Learning Algorithms

Machine learning algorithms learn from labeled datasets to make predictions on new, unlabeled data. For example, an algorithm trained on images of dogs and cats can predict whether a new image contains a dog or a cat based on the patterns it has learned. However, the confidence levels of these predictions vary. A machine learning model might be 80% confident that an image contains a dog or 50% confident.

Use Cases for Machine Learning

Machine learning is currently being used in a wide range of applications, including media analysis, subtitle generation, document processing, image moderation, model monitoring, and more. Customers are infusing machine learning into their workflows to automate repetitive tasks, speed up processes, and reduce costs. However, machine learning models are not always fully confident in their predictions, making human review and judgment necessary for accurate results.

The Role of Human Review in Machine Learning

Customers often face the challenge of choosing between machine learning-only workflows and human-only workflows. While machine learning workflows are scalable and cost-effective, human reviewers possess an intuitive sense of decision-making nuances and can validate low-confidence machine learning predictions. Customers are increasingly looking for ways to leverage the strengths of both machine learning and human judgment in their workflows. This approach allows machine learning to provide the first pass, and human reviewers to step in when more nuanced or confident decisions are required.

Use Cases for Human and AI Collaboration

There are several use cases where human and AI collaboration is crucial for achieving accurate results and making informed decisions. The following are some examples:

Media Analysis

Customers are using machine learning models to identify objects in videos and understand the key message. However, they also want humans to review the videos to analyze emotions, determine if the video is a Tutorial or testimonial, provide context, and perform other tasks that require human judgment.

Subtitle Generation

Machine learning models can generate accurate subtitles for videos. However, humans need to review the subtitles to ensure accuracy and correct any mistakes made by the model.

Document Processing

In scenarios such as invoice processing, customers require highly accurate Data Extraction. Machine learning models can extract data, but human reviewers are needed to verify and correct any errors. Accuracy is crucial in cases where a single incorrect data entry can lead to significant consequences.

Image Moderation

Online platforms that allow user-generated content rely on machine learning models to moderate images and videos. However, the guidelines for content moderation can vary by region and require human judgment to ensure the platform remains safe for its audience.

Model Monitoring

Customers want to ensure their machine learning models are performing as expected. By randomly sending a portion of ML inferences for human review, they can compare the human output to machine learning predictions, assess model performance, and catch any data deviations that may occur in the input data.

These are just a few examples of the use cases where customers are leveraging the power of both machine learning and human judgment for accurate and reliable results. The integration of human review in machine learning workflows can provide significant value across various industries and applications.

Challenges in the Integration of Humans and ML

Integrating humans and machine learning in the same workflow poses several challenges that need to be addressed:

Talent Requirements

Building machine learning and human workflow systems require a wide variety of talents, including machine learning scientists, engineering teams, and operations teams to manage a large number of reviewers. Acquiring the necessary talent and expertise can be challenging for organizations.

Development and Maintenance

Developing and maintaining a system that combines humans and ML in workflows requires significant time and effort. It involves writing custom software, building user interfaces, and ensuring the system is scalable, efficient, and user-friendly.

Accuracy of Human Review

Ensuring high accuracy in human review tasks can be challenging. Different reviewers may have different interpretations, leading to inconsistencies and potential errors. Developing strategies to improve reviewer accuracy is crucial for optimal results.

Despite these challenges, the benefits of combining human and AI workflows outweigh the complexities. With the right tools and processes, organizations can leverage the scalability and cost-effectiveness of machine learning while maintaining the reliability and accuracy of human judgment.

Introducing Amazon Augmented AI (A2I)

Amazon Augmented AI (A2I) is a service that simplifies the implementation of human review in machine learning workflows. A2I allows customers to easily integrate human judgment whenever machine learning confidence levels are low. By offloading the undifferentiated heavy lifting of managing workflows, A2I enables developers to focus on the business problem they are solving. A2I offers various benefits to customers:

Implementing Human Review in ML Workflows

A2I provides a straightforward mechanism to incorporate human review into ML applications and workflows. It allows developers to define when inferences should be sent for human review and specify what reviewers should do and how the review task should be presented. Defining workflows has never been easier.

Fast Model Deployment

A2I reduces the time-to-market for machine learning models. Pre-built workflows, with over 70 templates to choose from, enable fast implementation. Even when a model does not meet expectations, developers can still deploy it confidently, knowing that human reviewers will catch low-confidence results.

Flexibility in Workforce Options

A2I offers multiple workforce options to suit different needs. Customers can choose between the public workforce of over 500,000 independent contractors on Amazon Mechanical Turk or build their own private workforce by onboarding employees, operations teams, or third-party contractors. Additionally, pre-Vetted vendor workforces are available through the AWS Marketplace.

Integration with Amazon Recognition and Amazon Textract

A2I seamlessly integrates with other Amazon AI services, such as Amazon Recognition for image analysis and Amazon Textract for document processing. However, A2I can also be used independently for custom machine learning use cases. The flexibility to choose from pre-built integrations or build custom workflows makes A2I adaptable and versatile.

Providing Highly Accurate Results

A2I includes answer consolidation algorithms that consolidate answers from multiple reviewers into a single result. This ensures highly accurate results that can be directly used in downstream workflows. The consolidated answers mitigate any discrepancies or potential errors resulting from the individual human reviewers' interpretations.

A2I brings the power of human judgment together with machine learning to achieve accurate and reliable results. It streamlines the implementation of human review and facilitates the integration of humans and ML in a seamless workflow.

Customer Use Case: Ripcord

Ripcord is a robotics digitization company that specializes in extracting value from paper records. By combining hardware, software, and a central platform, Ripcord provides an integrated solution for digitizing physical documents. The Robotic Automation process, combined with recognition tools and the Canopy cloud repository, enables Ripcord to extract valuable data and connect it with customers' business systems.

Ripcord leverages Amazon A2I for data annotation and review, as it lowers model time to production and helps mitigate operational risks. By integrating A2I with their existing workflows, Ripcord streamlines the data annotation and review process, gaining insights to improve model performance and accuracy.

Leveraging Amazon A2I for Oil & Gas Documents

Oil and gas documents contain crucial data that is often trapped on paper. Ripcord leverages A2I to extract and review data from these documents, improving accessibility and connectivity. High-value data points such as API numbers, well names, and fields are extracted using Amazon Textract and reviewed by human workers through A2I. This collaboration ensures accurate data extraction and allows for easy integration into business systems.

Ripcord's use of geolocation mapping technology enhances the user experience, providing contextual information based on the extracted data. By linking the data with geographic location, users can gain insights and make informed decisions using an intuitive interface. This integration of data extraction, human review, and contextual presentation is a powerful example of leveraging Amazon A2I for oil and gas documents.

Conclusion

Amazon Augmented AI (A2I) simplifies the integration of human review in machine learning workflows, enabling customers to combine the power of ML with human judgment. By offloading the undifferentiated heavy lifting and providing pre-built templates and customizable workflows, A2I accelerates model deployment and improves accuracy. Throughout various industries and use cases, customers are leveraging A2I to streamline processes, enhance data extraction, and achieve reliable results.

The customer use case from Ripcord exemplifies how A2I has empowered robotics digitization for paper records. By integrating A2I into their workflow, Ripcord has benefited from increased accuracy, lower costs, and improved user experiences.

Frequently Asked Questions:

• What is the pricing for Mechanical Turk and Vendor workforce options?

  • The pricing for Mechanical Turk is determined by the customer, based on task complexity and desired turnaround time. Vendor workforce pricing can be found on the AWS Marketplace.

• How does Amazon A2I differ from SageMaker Ground Truth?

  • While SageMaker Ground Truth focuses on building training datasets for ML models, Amazon A2I is designed to incorporate human review into ML workflows.

• Can human reviewers compare two documents to determine if they are the same?

  • Yes, Amazon A2I supports document comparison tasks. Reviewers can compare two documents and provide their assessment.

• Can reviewers add new fields in addition to reviewing the ML output?

  • Currently, the ML model defines the fields that can be reviewed. However, this is a valid suggestion for future development.

• Is there a mechanism to evaluate individual reviewer performance?

  • Individual reviewer performance is currently not available, but it is an idea that could be considered for future improvements.

Thank you for attending this session on Amazon Augmented AI and understanding how it enables the collaboration of humans and machine learning. For more information and to explore specific use cases, please feel free to reach out to us.