It's every machine learning researcher's nightmare: the need to source or create large volumes of labelled data. For a generic task, you can crowdsource the labels. It's laborious and time-consuming, but doable.
For an expert task? That's a whole different level: experts are rare, often unavailable, and always expensive.
It would cost millions of dollars and take years to get the labels we need for expert tasks. That's wasted time, and wasted money. We found another way.
We're building all-purpose tagging machinery, based on interactive learning. The result? Our algorithm starts with unlabelled data. And it achieves the same performance as the baseline method, in a fraction of the time. Here's how:
We plot the data in 2D, so we can understand how the algorithm sees it.
We can see where the algorithm struggles most, and focus on that.
Our algorithm can apply knowledge from similar tasks.
So we can run algorithms on hundreds of millions of samples.
Tractable algorithms can label as much data in one hour as the baseline method can in a hundred. It’s a choice: go slow and blind, or get fast and interactive.
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