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Taking a Data-Driven and Model-Driven Approach to AI

Taking a Data-Driven and Model-Driven Approach to AI

Taking the first step toward more data-centric AI is as simple as recognizing the immense value your data holds and the value inherent in curating it carefully. But centralizing and systematizing your approach to AI isn’t enough—nor is it enough to focus on updating and testing the models you deploy at the expense of data quality. Even better than a data-centric approach to AI is a data-driven approach, one that focuses on both the quality of the data collected and the efficacy of the models this data is used to train.

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Object Detection: An Introduction

Object Detection: An Introduction

Spotting relevant information in an image or video feed can feel a little like looking for a needle in a haystack, but it doesn’t have to require so much time and effort. Training AI to detect objects can help by streamlining and automating the process of generating insights from visual data.

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Computer Vision and PPE Detection: More than Just Masks

Computer Vision and PPE Detection: More than Just Masks

While the COVID-19 pandemic continues through its third year, things have largely returned to normal for most Americans. Mask requirements – once a ubiquitous sign of pandemic life – have mostly disappeared as case numbers have decreased. Personal protective equipment of other sorts, however, isn’t going anywhere. Though you may no longer check for a mask before leaving the house, PPE remains vitally important for many businesses.

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Raise a Glass to Computer Vision: QC for Brewers & Beverage Manufacturers

Raise a Glass to Computer Vision: QC for Brewers & Beverage Manufacturers

The sheer number of recalls that occur each year suggest that typical best practices for identifying, tracking, and remediating hazards simply aren’t working well enough. Plainsight can help businesses of all sizes ensure the quality of all types of products by expediting and simplifying the process for creating and deploying custom AI models for proactively identifying risk factors, tracing contamination to the source, and identifying impacted process stations and equipment.

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Computer Vision and Agriculture: A Growing Partnership

Computer Vision and Agriculture: A Growing Partnership

Within the agriculture industry, businesses contend with a host of challenges like evolving regulations, supply chain complications, and the ongoing ravages of climate change. They are tasked with addressing worldwide issues like global hunger while also working to stay ahead of the competition. A new approach to computer vision, powered by an AI platform, can help.

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