AI is helping people create exciting applications and new ways to serve customers, cure diseases, prevent security threats, and much more. Rapid progress continues to unlock increasingly more opportunities for enterprises and scientific research where AI can make a big impact. Many believe that the real-world potential for AI is highly promising. Speaking at a 2016 AI conference in London, Microsoft’s chief envisioning officer, Dave Coplin observed, “This technology will change how we relate to technology. It will change how we relate to each other. I would argue that it will even change how we perceive what it means to be human.” Apparently, the best is still to come.
To predict the future, one must look at the past, says the old adage. To determine what to expect in 2017, we thought it was best to draw lessons from 2016 despite our industry’s yearning for dramatic change. Laks Srinivasan, COO at Opera Solutions, shares his insights into the biggest Big Data trends of 2016 and reflects on where the market is going and how companies will react.
Artificial intelligence is no longer just evolving nomenclature in IT. Everyone is taking interest. With the mainstream press and bloggers from every corner weighing in, it is worth taking stock of the nomenclature and learning how to differentiate three overly used key terms: artificial intelligence (AI), machine learning, and deep learning. The simplest way to think of their relationship is to visualize them as a concentric model (as depicted in the figure below) with AI — the idea that came first and has since been evolving — having the largest area. This is followed by machine learning, which blossomed later and is shown as a subset of AI. Inside both of those is deep learning, which is just one class of machine learning algorithms but one that is currently driving today’s AI explosion.