Unsupervised Learning might sound to those not well-versed in AI and Machine Learning (ML) technologies like machines going rogue in the presence of a substitute teacher, yet the breakthrough methodology we’re pioneering is far more structured, streamlined and striking in its potential.
“Every enterprise is looking at how machine intelligence can improve its operations and generate new business models,” writes Helm.ai CEO Vlad Voroninski, “but not all organizations have the tools or the know-how to take advantage of the technology.”
In this June Forbes longform, our CEO shared some insights into unsupervised learning and the future of AI.
The promise of Unsupervised Learning (UL) is enormous, making AI and ML orders of magnitude more scalable, accessible and accurate than ever before. The already award-winning methodological breakthrough is one of the most exciting developments in emergent technology, and that excitement is tied to a previously unfathomable reality: “ML models can [now] identify patterns and reach conclusions with little to no human intervention, making them much cheaper and faster to build.”
To get you up to speed, here’s CEO Vlad Voroninski again with the succinct UL overview:
“With unsupervised learning, you feed the model images without labels and it learns to understand those images purely algorithmically. Removing the need for labeling means you can train models faster and more cheaply. And because the cost is lower, you can use much larger data sets for training, which makes the models more accurate.
Faster and more independent Deep Learning, given the affectionate moniker “Deep Teaching,” means more data-per-dollar, by orders of magnitude. Accurate results come faster and cheaper, even after the initial mathematical hurdles of setup.
This more scalable, cost-effective method of neural network programming carries with it the promise of applications far afield from autonomous vehicles. The real promise of UL lies in not just what it can do, but in what else it could do.
“Unsupervised learning can be applied to anything that involves interacting with the physical world — like mining, consumer robots, drones, [and] automated retail checkouts,” adds Voroninski. He posits that further applying UL to fields like cybersecurity and physical security could prove invaluable.
That’s the hype, sure, yet it’s all backed by real results and some of the most advanced mathematical modeling and engineering out there. All that drives real hope as we look to expand our operations, invest even more heavily in R&D, and further refine our methods, with more pilots and partner programs on the way with Tier-1s and OEMs. The world is waiting to watch what happens when humans can, both metaphorically and literally, take their hands off the wheel, thanks to UL.
The promise and potential are there. The technology is continually evolving and we’re exploring beyond the limits of what major players in the autonomous technology spaces could only dream of doing, in a very real sense.
Unsupervised Learning: It may not require human monitoring, but in the coming years, it could become the one technology breakthrough truly worth keeping an eye on.
