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How AI Can Improve Your Annotation Quality?

Smart Data Collective

One development that AI has led to is the growth of image annotation. Image annotation is the act of labeling images for AI and machine learning models. It involves human annotators using a tool to label images or tag relevant information. The resulting structured data is then used to train a machine learning algorithm.

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Equinix goes partner prospecting with AI

CIO

Building an innovative, intelligent AI-based prospecting engine for our channel program was the perfect use case that enabled us to combine the power of AI technology innovation, build competitive market differentiation for the company, and help improve the experience for our customers and our channel partners.”

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Data Labeling Improves Machine Learning & AI Efficiency

Smart Data Collective

Earlier today, one analysis found that the market size for deep learning was worth $51 billion in 2022 and it will grow to be worth $1.7 Simply put, data labeling involves annotating data to instruct a model on how to do the same. The quality and accuracy of data labeling have significantly improved due to AI and ML algorithms.

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The Future of AI: High Quality, Human Powered Data

Smart Data Collective

More and more business owners are adopting AI and other machine learning technologies to automate their decision-making processes and also help them uncover new business opportunities. However, Ai uses algorithms that can screen and handle large data sets. Even with these technologies, systemic prejudices are unavoidable.

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Best Enterprise Search Software in 2024 (Buyer’s Guide)

Alpha Sense BI

Our suite of tools currently includes: Smart Summaries This feature allows you to glean instant earnings insights (reducing time spent on research during earnings season), quickly capture company outlook, and generate an expert-approved SWOT analysis straight from former competitors, partners, and employees.

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How Aster DM Healthcare used federated learning to better secure AI analysis of sensitive data

CIO

Genomic data alone is predicted to be 2 to 40 exabytes by 2025, eclipsing the amount of data acquired by all other technological platforms,” it says. Federated learning is a method of training AI algorithms with data stored at multiple decentralised sources without moving that data. Annotation was already in our PACS system.

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