- AI is not at all like Mobile/Cloud/SaaS - Feb 10, 2017.
AI is a hard problem and will take much longer to solve in any scope. The sudden uptick in interest may revert back to normal, but the cycle of work will be longer, much more diverse, and interesting than Mobile/Cloud/SaaS.
- Machine Learning: Separating Hype From Reality - Jul 22, 2016.
When it comes to business value and ROI, does machine learning live up tot he claims? We’ll explore a pure machine learning approach through the lens of a typical enterprise use case.
- Deep Learning is not Enough - Feb 9, 2016.
Deep Learning has real successes, but is not enough to reach artificial intelligence, according to latest KDnuggets Poll. For more complex problems, should pure neural-net approaches be combined with symbolic, knowledge-based methods?
- KDnuggets™ News 16:n04, Feb 3: Is Deep Learning Overhyped? Businesses Will Need 1M Data Scientists - Feb 3, 2016.
New Poll: Deep Learning - does reality match the hype?; Is Deep Learning Overhyped?; Businesses Will Need One Million Data Scientists by 2018; KDnuggets New Responsive, Mobile-Friendly Design.
- New Poll: Deep Learning – does reality match the hype? - Jan 29, 2016.
New KDnuggets Poll looks at the very hot field of Deep Learning and asks: does reality match the hype? Please vote!
- Is Deep Learning Overhyped? - Jan 29, 2016.
With all of the success that deep learning is experiencing, the detractors and cheerleaders can be seen coming out of the woodwork. What is the real validity of deep learning, and is it simply hype?
- KDnuggets™ News 15:n29, Sep 2: How to become a Data Scientist for Free; Big Data Out, Machine Learning In - Sep 2, 2015.
How to become a Data Scientist for Free; Gartner 2015 Hype Cycle: Big Data is Out, Machine Learning is In; KDnuggets part-time internship in Data Science, Data Journalism; The one language a Data Scientist must master.
- Essays On Statistics Denial - May 20, 2015.
Statistics denial comes in waves as areas of application discover and rediscover the potential of data insights. We examine the statistics denial myths and where they come from.