Machine Discovery

Don't Miss Out on Staffing Tech Weekly!

Stay ahead of the competition with the latest AI-driven staffing insights, trends, and best practices delivered straight to your inbox.
Share this post

We’re seeing more and more about how algorithms and machine learning are being applied behind the scenes in our digital lives. These techniques are being applied to tools used in the HR industry to the point that they are becoming old hat. So what’s next? Machine discovery.

No one is really talking about “machine discovery” at this point except for this guy; Raul Valdes-Perez. We’re excited he’s covering the topic because it highlights an emerging field of data science that has the potential to be more powerful and user friendly than anything that’s out there. In a November 2015 article on Tech Crunch called “Machine Learning Versus Machine Discovery” Mr. Valdes-Perez breaks down this difference. Being that our core data science initiatives at Vettd are based on these principals, an alternative point of view can help better articulate what it’s all about and here are some highlights.

“Machine learning is hot. Where it applies, it heatedly enables data-rich and knowledge-lean automation of valuable tasks of perception, classification and numeric prediction. Its sibling, machine discovery, deals with uncovering new knowledge that enlightens or guides human beings…

Armed with these key ideas, let’s consider which is the better design — discovery or learning — for a proposed app: A guest-introducer for large parties or events. A good party host identifies areas of common interest among guests and endeavors to introduce them, explaining what they have in common in order to stimulate conversation. It’s a hard task and hosts are busy. Given an attendance list, could making good introductions be automated?

An AI or discovery approach proceeds like this: Study, or figure out, what makes a good introduction. What determines quality? Is there scope for innovative introductions that serve the core purpose? What data sources enable these automated inferences (e.g., LinkedIn profiles or other biographical sources)?…

Discovery requires studying the task logic (i.e., the space of possible solutions), the knowledge that prioritizes good paths within that space and algorithm design to make it all practical. There is scope for innovation in the space being searched and the heuristics used. But the most innovations may come from novel, creative outputs on specific inputs, because automation enables exploring a much larger space of possibilities than people can practically consider…

Machine discovery will address specific tasks that require knowledge and training when done humanly. Discovery tends to be hand-crafted, more elaborate and rarer.”

Thanks for the clarification Raul!

We’ll leave you with our take on the comparison between machine learning and machine discovery. Here’s what a conversation between a recruiter and each technique might look like.