At the Structure Data conference, Jeremy Howard, CEO of Enlitic, said, "Deep learning is unique in that it can create features automatically." Enlitic has used deep machine learning to develop an ...

Quanta Magazine recently spoke with Engelhardt about the shortcomings of black-box machine learning when applied to biological data, the methods she’s developed to address those shortcomings ...

Left to rely on just its training data, machine learning can be kind of dumb, unable to discern common objects in uncommon location, like a hammer on a bed (pictured above) instead of a workbench.

It's a data terminology mess out there. Let's try and untangle it, because there's more to words than lingo. ... They prepare data to be used in data products, such as machine learning models.

I honestly feel for recruiters who are tasked with filing data-science and machine-learning job openings. The list of requirements that employers draw up for those roles is pure bravado with a ...

Big data machine learning is best put to use in a recommendation engine. It combines context with user behavior predictions to influence user experience based on their activities online.

Poor quality, unusable data is a burden for those at the end of the data’s journey. These are the data users who use it to build models and contribute to other profit-generating activities.

But AI is not only about large data sets, and research in “small data” approaches has grown extensively over the past decade—with so-called transfer learning as an especially promising example.

The potential for machine learning to transform data-intensive businesses is undeniable, but realizing this potential requires more than just an investment in technology. Newsletters Games Share a ...