Statistical models and machine learning algorithms are often mysterious and confusing for average business and data professionals. However, even the most math-adverse can reach some understanding.
- By William McKnight
- February 22, 2017
Data initiatives that increase government transparency, Google experiments with competitive AIs, and the difference between a data scientist and a data analyst.
- By Lindsay Stares
- February 21, 2017
At the 2017 World Economic Forum meeting, tech industry leaders openly discussed some of the ethical and societal challenges facing the current surge in big data-driven AI.
- By Barry Devlin
- February 17, 2017
With the increasing demand for data science professionals, what skills are necessary to succeed in our new digital world?
- By Devavrat Shah
- February 14, 2017
Infrastructure speed has reached a plateau. Today's enterprises should be seeking out predictive, automated solutions to improve their enterprise infrastructure.
- By Rod Bagg
- February 9, 2017
How data analytics might improve city life, drug manufacturers should use social media data, and the difference between AI and machine learning.
- By Lindsay Stares
- January 26, 2017
So-called smart machines will be used in nearly one-third of large enterprises by 2021. That's tantamount to mainstream adoption.
Despite the attraction of consumer-facing AI, IBM's focus on practical business applications may yet win the race for widespread AI adoption.
- By Barry Devlin
- January 18, 2017