“AI is much more broader, all-encompassing, compared to only machine learning or algorithms,” Susarla said. That’s why you might hear “AI” as a loose description for a range of things ...

Feature engineering is a hard problem to automate, however, and not all AutoML systems handle it. In summary, machine learning algorithms are just one piece of the machine learning puzzle.

Google uses a plethora of machine learning algorithms. There is literally no way you, me, or likely any Google engineer could know them all. On top of that, many are simply unsung heroes of search ...

However, machine learning can be used to automate this process by training algorithms to identify defects ... We will take it for granted. Given all of this, those using and developing AI must ...

But when you or I don’t expect that any time soon or in the foreseeable future all of them would become machine learning based. Or that’s what we call the core algorithm would become machine ...

by exposing the algorithm to more data. We hear about applications of machine learning on a daily basis, although not all of them are unalloyed successes. Self-driving cars are a good example ...

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products ... computers and AI for nearly all aspects of daily life ...

Machine learning is ... it’s not always clear why. (Deep learning algorithms are particularly plagued by this “interpretability” problem.) Still, the process itself is easy to recognize. Deep down, ...