Our blog
What is Machine Teaching?
The past few years have been incredibly exciting for the Machine Learning research community: from Generative Adversarial Networks (GANs) to Learning-to-Learn, it seems that a new training paradigm or a new model architecture is seeing...
Everything You Need to Know about DataPrepOps – Part 3
The many flavors of DataPrepOps
Everything You Need to Know about DataPrepOps – Part 2
What is DataPrepOps? Half a century went by from the inception of the fundamental principles of AI, to the day where AI applications finally saw the light of day. The 2010s finally marked the beginning of a new era for ML experts after...
Everything You Need to Know about DataPrepOps – Part 1
The Origins of Data Labeling Remember the last time you took a picture with your friends to post on social media, and the app helped you save time by automatically tagging everyone for you? It might be hard to believe, but not so long...
What is Data-Centric AI?
A Primer San Francisco, CA, May 2018: as the Train AI conference was drawing to a close, the audience was gathering one last time for what was one of the most anticipated talks of the day: Andrej Karpathy, Director of AI at Tesla. The...
Agile Data Labeling: What it is and Why you Need it
This article was originally published in KGNuggets; see original publication here. The concept of agility is certainly a popular one in technology, but not one that you would naturally associate with data labeling. And it’s fairly easy to...
Leveraging Active Learning on Small Datasets
At Alectio, we’re usually known to help ML teams reduce the size of their datasets; our typical users come to us when they have more data than they know what to do with, and when training on their entire dataset is just not feasible, the...
Planes, Training and Anomalies (or How Much Data is Really Needed When Working with Sensor Data?)
If you are an avid Alectio follower, you’ll know by now that efficient training is what we do. It is not just a matter of philosophy (even though it is, as we have described here, here and here); it’s also a simple matter of economics....
9 Reasons Why Active Learning is Not Widely Adopted
The Machine Learning field is full of buzz: deep learning, LSTMs, generative adversarial networks; the list keeps going on and on and on. Some of the most promising concepts, though, stand at the other end of the spectrum. Active learning...
How Active Learning Can Massively Reduce Aerial Imagery Labeling Costs
As we enter 2021, active learning is perhaps the least understood and most underutilized technique in machine learning today. Its promise is simple and elegant: to reduce the overall records you use to train models without trading off...
How We Helped Voyage Increase Their Model Performance with Active Learning
This article was written in collaboration with Voyage, and originally published on their blog. The field of computer vision reached a tipping point when the size and quality of available datasets finally met the needs of theoretical...
There’s No Such Thing as Green AI
Meet John. John is a machine learning scientist. He likes his cat and he loves his dogs. He has a wife and two kids and he cares about the environment. He rides his bike to work instead of driving but when he has to drive, he takes his...