How Insurers Are Using AI to Lower Customer Acquisition Costs

How Insurers Are Using AI to Lower Customer Acquisition Costs

Insurers are using AI to lower customer acquisition costs, identify new opportunities, and enable sales with personalized coaching and tools.

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The Ethical Sourcing of Training Data

The Ethical Sourcing of Training Data

Are you ethically sourcing training data for your AI models? And what does “ethically sourcing” mean, anyway? Read this post to explore the issue.

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4 Use Cases on Why Using Drones to Collect Data Improves Inspections

4 Use Cases on Why Using Drones to Collect Data Improves Inspections

These four use cases examine why using drones to collect data makes drone inspections safer, more accurate, and more efficient than manual inspections.

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Does Not Compute: The NLP Context Conundrum

Does Not Compute: The NLP Context Conundrum

The nuances of language can be difficult for a machine to understand, hence the need for human input to accelerate testing and ensure quality control.

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Sentiment Analysis—and Why Computers Can't Do it Alone

Sentiment Analysis—and Why Computers Can't Do it Alone

Sentiment analysis can turn the abundance of online information into actionable insights, but machines can’t do everything by themselves.

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The AI & Automation Must-Have: Humans-in-the-Loop

The AI & Automation Must-Have: Humans-in-the-Loop

Humans are necessary while automating decisions and processes with AI, machine learning, and RPA. Experts discuss the need for humans in the loop (HITL).

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How to Keep Your Machine Learning Models Up-to-Date

How to Keep Your Machine Learning Models Up-to-Date

No matter how robust your initial training may be, keeping your machine learning models up-to-date is essential. Here are two retraining approaches.

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Scaling Quality Training Data: Best Practices for Your Data Production Line

Scaling Quality Training Data: Best Practices for Your Data Production Line

Your training data operations are like assembly lines: data is your raw material, and you have to get it through production steps to structure it for AI. You need skilled people ...

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Scaling Quality Training Data: The Hidden Costs of the Crowd

Scaling Quality Training Data: The Hidden Costs of the Crowd

Anonymous crowdsourcing is a common alternative to an in-house team for AI development. It can be a cheap option for training machine learning algorithms but it’s rarely as ...

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Successful AI Development Means Fielding The Best Team

Successful AI Development Means Fielding The Best Team

Given the challenges of hiring and managing a team to complete the arduous data work behind AI, many companies are turning to outside help.

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Scaling Quality Training Data: Choosing the People in Your AI Tech Stack

Scaling Quality Training Data: Choosing the People in Your AI Tech Stack

AI innovators rely on external teams to structure data for ML algorithms. But scaling quality data requires the right people & processes in your tech stack.

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AI Bias And The 'People Factor' In AI Development

AI Bias And The 'People Factor' In AI Development

AI is only as good as the data it's trained to analyze. CloudFactory CEO Mark Sears shares in Forbes about how AI bias can arise from people, tools, algorithms, and human ...

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New Job Titles Created by AI

New Job Titles Created by AI

As more companies adopt AI techniques to improve products, service and profitability, we’re starting to see new job titles emerge for workers across the tech industry.

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Training Hybrid Human-Machine Classifiers

Training Hybrid Human-Machine Classifiers

When humans and computers work together we can do incredible things. Human-in-the-loop computer vision can help solve problems that are hard for even humans to do themselves.

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