Covering everything you need to know in order to build AI products faster.
Labelbox•February 15, 2021
American University of Beirut takes on dietary behavior research with ML
Researchers at the American University of Beirut have embarked on a project International Development Research Centre (IDRC) to build two machine learning models that classify images of food taken by wearable cameras.
Labelbox•January 22, 2021
Software to AI: a paradigm shift
Labelbox CEO and Cofounder Manu Sharma and Andreessen Horowitz’s Peter Levine compare and contrast AI with traditional software.
Labelbox•December 15, 2020
University of Exeter uses ML to reveal how the media impacts human behavior
Researchers at Exeter University have been conducting two studies utilizing machine learning to explore the effects of news and social media on public attitudes and behavior.
Labelbox•November 10, 2020
Teach your ML model to perceive time
At Labelbox, our mission is to build tools that teach machines how to perceive reality and understand and predict patterns in our surroundings.
Brian Rieger•October 30, 2020
Automating Computer Vision Annotation: Let Your Model Do The Work.
Discover the 3 basic ways to automate labeling depending on how aware the method is of the data to be labeled.
Labelbox•October 24, 2020
Automate labeling with your own model
Model-assisted labeling from Labelbox is the fastest way to improve model performance. Reduce labeling costs by up to 50-70% with your own model.
Labelbox•October 22, 2020
Using Labelbox to optimize crop production
Learn how Labelbox's training data platform plays a central role in xarvio's ML pipeline to handle the full data annotation process and help farmers innovate through digital farming.
Labelbox•October 14, 2020
How Cape Analytics uses active learning to get to production AI faster
Learn how Cape Analytics created an active learning workflow whereby low confident predictions were visible and prioritized to their data scientists and labelers for quick analysis and correction.
Labelbox•September 25, 2020
How MIT researchers are using neural nets to automate manual tasks for novel serotonin research
Jungsoo Kim is a PhD graduate student at MIT’s Flavell Lab which focuses on cutting-edge neuroscience research. His team is currently exploring how computation and behavior emerge from neural circuits. Neurons, just like electronics, need to be properly wired in order to function well. Their project investigates how serotonin modulates a large number of neurons, altering the circuit dynamics in the brain. Serotonin supports a wide range of computation and behavior ranging from mood regulation to
Labelbox•August 18, 2020
How Arturo builds AI-first products from the ground up to deliver insights for the $1.2 trillion insurance industry
Gareth Jones, data scientist at Arturo, recently spoke with our COO, Brian Rieger on how Arturo’s machine learning team built mature and complex AI-first products while juggling resource costs, data labeling and scalability as top priorities.