Crowdsourcing Vs. Managed Service Vs. In-House Labeling
Labeled data is required for all supervised machine learning projects. Labels are added to raw data, such as images, text, audio, and video, in order...
How to Find Training Data for Machine Learning
Supervised machine learning projects require training data. By learning from training data, a supervised algorithm aims to be able to accurately predict outcomes when exposed...
Manual Vs. Automated Data Labeling
As demands increase for high-quality, large-scale training datasets, data labeling has become an increasingly important function within AI. Manually labeling training data is labor-intensive and...
Is Synthetic Training Data the Future of Machine Learning?
Even as the availability of processing power increases exponentially, and machine learning algorithms are commoditized, there is a problem that persistently slows the development of...
Objectivity and Ground Truth in AI
‘Ground truth’ is somewhat of a confusing machine learning buzzword. Discussions of the ground truth naturally touch on areas such as bias, representation, and objectivity,...
How Drones and Computer Vision are Used to Enhance Crop Yield
Drones or UAVs (Unmanned Aerial Vehicles) are propelling a revolution in farming and agriculture. In 2019, the FAO published E-Agriculture in Action: Drones for Agriculture, a detailed guide...
Ultimate Guide to LIDAR
Light Detection and Ranging (LIDAR) is a remote sensing and mapping technology designed to measure the dimensions and topography of terrain and 3D spaces. While...
AI Embodies The Bias Of Internet Content
OpenAI is a significant player in the modern artificial intelligence (AI) and machine learning (ML) space. While the parent company is a non-profit, it also...
Using AI and Machine Learning to Decipher Ancient Languages
he Sumerians inhabited Mesopotamia some 6000 years ago, the region between the Tigris and Euphrates rivers, in modern-day Iraq, Kuwait, Turkey, and Syria. Mesopotamia marks...
What is a Human-in-the-Loop?
Machine learning (ML) and artificial intelligence (AI) models still largely require the input of humans to train, tune and test models to ensure their accuracy...