2016 Dec Opinions, Interviews
All (100) | Courses, Education (9) | Meetings (11) | News, Features (26) | Opinions, Interviews (31) | Software (3) | Tutorials, Overviews (18) | Webcasts & Webinars (2)
- Citizen Data Scientist, Jumbo Shrimp, and Other Descriptions That Make No Sense - Dec 30, 2016.
No one would say “Citizen Lawyer” or “Citizen Nuclear Physicists” or “Citizen Physician.” I guess a “Citizen Physician” would be someone who “practices medicine but whose primary job function is outside of the field of medicine (meaning that they’ve had no training in medicine or medical procedures).”
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Game Theory Reveals the Future of Deep Learning - Dec 29, 2016.
This post covers the emergence of Game Theoretic concepts in the design of newer deep learning architectures. Deep learning systems need to be adaptive to imperfect knowledge and coordinating systems, 2 areas with which game theory can help. - Laying the Foundation for a Data Team - Dec 28, 2016.
Admittedly, there is a lot more to building a successful data team, and we would be lying if we pretended we have it all figured out. But hopefully focusing on the elements in this post is a good start.
- I’m A Data Guy And I Don’t Get Why Everyone’s Obsessed With Data - Dec 27, 2016.
In post after post, in article after article, battle-lines are drawn between Big Ideas and Big Data. I really don't get what the fuss is about. What's wrong with having both?
- How I Detect Fake News, by Tim O’Reilly - Dec 26, 2016.
Read how Tim O'Reilly traced the falsity of one internet meme, and what that teaches us about how an algorithm might do it.
- Internet of Things (IoT) Challenge: The Sensor That Cried Wolf - Dec 23, 2016.
William Schmarzo, the "Dean of Big Data," shares a personal story that identifies a tangible issue related to technology in general, and which carries an important message for the Internet of Things (IoT) in particular.
- The Five Capability Levels of Deep Learning Intelligence - Dec 22, 2016.
Deep learning writer Carlos Perez gives his own classification for deep learning-based AI, which is aimed at practitioners. This classification gives us a sense of where we currently are and where we might be heading.
- Privacy, Security and Ethics in Process Mining - Dec 21, 2016.
Data Privacy, Security and Ethics are hot yet complex topics in the business and data science world. This important article talks about and provide guidelines for privacy, security and ethics, specifically in the context of Process Mining.
- Mark van Rijmenam’s Top 7 Big Data Trends for 2017 - Dec 20, 2016.
Top Big Data expert Mark van Rijmenam weighs in with his top Big Data-related predictions for 2017.
- What we can learn from AI mistakes - Dec 19, 2016.
Because of recent innovations and research in AI, we have seen AI performing best in some very important tasks and even worst in even simple tasks. So the question is, Why is it that AI can look so brilliant and so stupid at the same time?
- Predictions for Deep Learning in 2017 - Dec 19, 2016.
The first hugely successful consumer application of deep learning will come to market, a dominant open-source deep-learning tool and library will take the developer community by storm, and more Deep Learning predictions.
- Data Science & Ancestry - Dec 17, 2016.
Ancestry is curious topic for many people to find out their origin and history. Today, data science is used to help these people to dig into their family history and build the family trees.
- The 5 Basic Types of Data Science Interview Questions - Dec 16, 2016.
Data science interviews are notoriously complex, but most of what they throw at you will fall into one of these categories.
- Deep Learning Works Great Because the Universe, Physics and the Game of Go are Vastly Simpler than Prior Models and Have Exploitable Patterns - Dec 16, 2016.
How is Deep Learning experiencing such success solving complex problems? Deep Learning is useful and powerful but it is also that the problems were not as big or as hard as researchers feared when they were unsolved.
- What You Are Too Afraid to Ask About Artificial Intelligence (Part II): Neuroscience - Dec 14, 2016.
As a followup to Part 1, which covered the advancements in machine learning, this post gives considers comprehension of the brain mechanisms. We have an ever-increasing understanding of brain processes, which may help to foster the development of an AGI.
- arXiv Paper Spotlight: Why Does Deep and Cheap Learning Work So Well? - Dec 13, 2016.
The recent paper at hand approaches explaining deep learning from a different perspective, that of physics, and discusses the role of "cheap learning" (parameter reduction) and how it relates back to this innovative perspective.
- Just How Smart Are Smart Machines? - Dec 12, 2016.
The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization’s business needs requires a deep understanding of their capabilities. Here we examine 4 levels of intelligence across task types.
- Bayesian Basics, Explained - Dec 9, 2016.
This interview between Professor Andrew Gelman of Columbia University and marketing scientist Kevin Gray covers the basics of Bayesian statistics and how it differs from the ordinary statistics most of us learned in college.
- 4 Cognitive Bias Key Points Data Scientists Need to Know - Dec 9, 2016.
Cognitive biases are inherently problematic in a variety of fields, including data science. Is this something that can be mitigated? A solid understanding of cognitive biases is the best weapon, which this overview hopes to help provide.
- The big data ecosystem for science: Physics, LHC, and Cosmology - Dec 8, 2016.
Big Data management is essential for experimental science and technologies used in various science communities often predate those in Big Data industry and in many cases continue to develop independently. This post highlights some of these technologies, focusing on those used by several projects supported by the National Energy Research Scientific Computing Centre (NERSC).
- Data Science Trends To Look Out For In 2017 - Dec 8, 2016.
Machine Learning is here to stay, with more firms following Google and Facebook in the race to attract the best machine learning experts and Data Scientists. We also see a merger of IoT and Data Science. Read on for more trends.
- What You Are Too Afraid to Ask About Artificial Intelligence (Part I): Machine Learning - Dec 7, 2016.
In the first of a 2 part series, this post will briefly discuss major advancements in pure machine learning techniques - while a followup post will similarly treat neuroscience - both with an agnostic lens.
- arXiv Paper Spotlight: Automated Inference on Criminality Using Face Images - Dec 7, 2016.
This recent paper addresses the use of still facial images in an attempt to differentiate criminals from non-criminals, doing so with the help of 4 different classifiers. Results are as troubling as they are unsettling.
- Big Data: Main Developments in 2016 and Key Trends in 2017 - Dec 6, 2016.
As 2016 comes to a close and we prepare for a new year, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2016 and their 2017 key trend predictions.
- CDO: to stay or to go? - Dec 5, 2016.
The Chief Digital Officer role has grown 1000-fold in the last 9 years, but will it remain popular in 2025? We examine the parallels between the electric and digital revolutions.
- Why Deep Learning is Radically Different From Machine Learning - Dec 5, 2016.
There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference?
- How To Make Your Mark As A Woman In Big Data - Dec 3, 2016.
Despite the shift in big data technology innovation that is driving tremendous growth and opportunities, women still play a small role in this arena. Here are 5 thoughts for women considering a career in big data.
- Interviews with Data Scientists: Claudia Perlich - Dec 2, 2016.
In this wide-ranging interview, Roberto Zicari talks to a leading Data Scientist Claudia Perlich about what they must know about Machine Learning and evaluation, domain knowledge, data blending, and more.
- Smart Data Platform – The Future of Big Data Technology - Dec 2, 2016.
Data processing and analytical modelling are major bottlenecks in today’s big data world, due to need of human intelligence to decide relationships between data, required data engineering tasks, analytical models and it’s parameters. This article talks about Smart Data Platform to help to solve such problems.
- The hard thing about deep learning - Dec 1, 2016.
It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.
- Top Reasons Why Big Data, Data Science, Analytics Initiatives Fail - Dec 1, 2016.
We examine the main reasons for failure in Big Data, Data Science, and Analytics projects which include lack of clear mandate, resistance to change, and not asking the right questions, and what can be done to address these problems.
Game Theory Reveals the Future of Deep Learning
Bayesian Basics, Explained
Nov
