Our blog
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 reason being that most people...
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. For many companies in a wide...
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 has to be one of the top...
WEBINAR RECORDING: 5 Ways to Save on Data Labeling
You probably don’t need us to tell you this, but machine learning is expensive. Whether it’s data storage, data warehousing, rising compute costs, models that need retraining, hiring the best and brightest in a competitive marketplace, or data labeling, spinning up...
Alectio Explains it All
If you work in machine learning, chances are you’ve had that moment at a party or a family dinner where someone asked you to explain some esoteric ML concept in a way your audience could understand. For experts, this can be tough. After all, machine learning is...
Increasing Accessibility to AI
The first cell phone ever sold was called the DynaTAC 8000X. The year was 1983 and for the low, low price of $3995, you could have been an early adopter. The phone itself took ten hours to charge and had a battery life of half an hour. It was a large, unwieldy thing...
Creating More Opportunities in AI
Last piece, we dug into the concept of increasing access to AI. In this one, we’re going to tackle opportunity. You may be asking yourself what exactly the difference is here, and, we’ll admit this a Venn diagram with some overlap. Diverse data labeling, for example,...
Impact, Bias, and Sustainability in AI
Back in 2013, a man by the name of Eric Loomis was arrested in Wisconsin. Loomis was driving a car that had been used in a shooting and pled guilty to eluding an officer. It’s a case that should be fairly unremarkable but at sentencing, the judge sentenced Loomis to...
How We Got Responsible AI All Wrong
If you want to distill the idea of technology into a single sentence, a good place to start is with this: “Fire can cook your food or it can burn your house to the ground.” That is to say: technology isn’t good or bad in and of itself. The devil’s in the details. Or...
Why Amazon has an Alexa problem — and what you can learn from it
When Amazon launched Alexa, a lot of prognosticators and technologists were giddy about the possibilities. Here, finally, was an affordable machine heralding the era of real voice-activated compute, a simple device that could help you shop, turn your lights on, recite...
Is Big Data Dragging Us Towards Another AI Winter?
It can be hard to remember with the number of breathless press clippings in the past few years, but the history of artificial intelligence has been fraught with snags and setbacks. People with long memories remember the first pair of so-called “AI Winters” in the...
No, the world doesn’t need another synthetic data company
Let’s begin with the obvious: every machine learning project starts with data. Whether that data needs to be labeled, collected, generated, cleaned, munged, or fussed with in any way, shape, or form, we all understand that machine learning requires not only data, but...