Myths (12)

• Seven Myths About the True Costs of AI Systems - Oct 24, 2019.
  
  While there is much excitement today around implementing AI at the enterprise level, the financial costs of this process are often unexpected and underappreciated. These seven myths are crucial lessons learned that executives should know before heading down the road to AI.

  AI, Enterprise, Myths, ROI

• AI is a Big Fat Lie - Jan 26, 2019.
  
  Is AI legit? This treatise by Eric Siegel, which ridicules the widespread myth of artificial intelligence, is enlightening and actually pretty funny. It's time for the term AI to be “terminated.”

  Pages: 1 2

  AI, Eric Siegel, Myths

• KDnuggets™ News 19:n03, Jan 16: Top 10 Books on NLP and Text Analysis; End To End Guide For Machine Learning Projects - Jan 16, 2019.
  
  Also: Why Vegetarians Miss Fewer Flights - Five Bizarre Insights from Data; 4 Myths of Big Data and 4 Ways to Improve with Deep Data; The Role of the Data Engineer is Changing; How to solve 90% of NLP problems: a step-by-step guide

  Big Data, Data Engineer, Data Science, Insights, Machine Learning, Myths, NLP, Text Analysis

• KDnuggets™ News 18:n42, Nov 7: The Most in Demand Skills for Data Scientists; How Machines Understand Our Language: Intro to NLP - Nov 7, 2018.
  
  Also: Machine Learning Classification: A Dataset-based Pictorial; Quantum Machine Learning: A look at myths, realities, and future projections; Multi-Class Text Classification Model Comparison and Selection; Top 13 Python Deep Learning Libraries

  Classification, Data Science Skills, Deep Learning, Myths, NLP, Python, Text Classification

• Ten Myths About Machine Learning, by Pedro Domingos - Jan 3, 2017.
  
  Myths on artificial intelligence and machine learning abound. Noted expert Pedro Domingos identifies and refutes a number of these myths, of both the pessimistic and optimistic variety.

  Machine Learning, Myths, Pedro Domingos

• Data Science and Big Data: Definitions and Common Myths - Dec 12, 2016.
  
  A well-set data strategy is becoming fundamental to every business, regardless the actual size of the datasets used. However, in order to establish a data framework that works, there are a few misconceptions that need to be clarified.

  Big Data, Data Science, Myths

• 8 Myths about Virtualizing Hadoop on vSphere Explained - Dec 22, 2015.
  
  This article takes some common misperceptions about virtualizing Hadoop and explains why they are errors in people’s understanding.

  Pages: 1 2

  Containers, Hadoop, Myths, Virtualization, VMware, vSphere

• The Five Myths of Big Data - Nov 16, 2015.
  
  Here, we are bursting couple of the myths which have been built around the big data. Ranging from does it predicts future, it is only for big businesses and is it a better data?

  Big Data, Matt Reaney, Myths

• Paradoxes of Data Science - Aug 21, 2015.
  
  There are many paradoxes, ironies and disconnects in today’s world of data science: pain points, things ignored, shoved under the rug, denied or paid lip.

  Data Science, Data Science Skills, Myths, Thomas Ball

• Statistics Denial Myth: Repackaging Statistics With Straddling Terms - Jul 16, 2015.
  
  Data science is nothing but the old wine in new bottle versions of the statistics with different fields. Here, we are busting the myth which states data scientist is new and different than traditional statisticians.

  Data Analysis, Data Management, Data Science Skills, Myths, Randy Bartlett, Statistics

• Deep Learning Adversarial Examples – Clarifying Misconceptions - Jul 15, 2015.
  
  Google scientist clarifies misconceptions and myths around Deep Learning Adversarial Examples, including: they do not occur in practice, Deep Learning is more vulnerable to them, they can be easily solved, and human brains make similar mistakes.

  Adversarial, Deep Learning, Ian Goodfellow, Myths, Regularization

• Interview: Mark Weiner, Temple University Health System on Addressing Healthcare Data Gaps through Advanced Simulation - May 12, 2015.
  
  We discuss dealing with current gaps in healthcare data, challenges in using real world healthcare data, desired skills for data scientists in healthcare industry, advice, and more.

  Healthcare, Interview, Mark Weiner, Myths, Simulation, Skills, Temple University, Trends