By Vedant Misra | November 1, 2016
If you’re a business leader with access to a technology budget, over the past decade, there are a handful of phrases that have suddenly became impossible to ignore. At this point, you have basically no choice but to act like you really understand what they mean.
Here’s an inexhaustive list, in roughly the order that they blew up:
- Big data
- Predictive analytics
- Data science
- Machine learning
- Deep learning
- Artificial intelligence
At every conference, you’ll find some industry leader declaring “X is dead, Y is the future”—where “X” and “Y” are both items from the list above, and where both X and Y are neither dead nor the future.
The newest terms that have been thrown into the fray are probably Deep Learning and Artificial Intelligence. And they are bandied about with reckless abandon. What are they? What’s the difference between them? What’s the difference between both of them and machine learning?
If these sound like questions you’re asking yourself, here’s what you need to know to understand what’s going on.
Big data, predictive analytics, data science, and machine learning
All of these terms appear to have peaked in their popularity. They started to blow up in roughly 2006, 2008, 2010, and 2012, respectively, and at this point are so oversaturated in their use in marketing material that people have basically stopped using them. We’ve discussed each of them in the past, and our earlier posts are worth checking out if you’re curious what they mean:
The terms that are hot these days are machine learning, deep learning, and artificial intelligence. In fact, I’d venture to say that machine learning is on its way out. Nonetheless, keep reading to understand what each of them means.