This year, 2017, has been quite a busy year for artificial intelligence and data science professionals. In some ways, this is the year when AI truly began to be debated and discussed, from frameworks and technologies to ethics and morality. This is the year when opportunities for AI-driven improvement in businesses began to be examined […]Read more "Key Data and AI trends in 2017"
One of the more interesting mental models of machine learning I’ve come to understand in the last month or so, is the “five tribes of artificial intelligence” model popularized in “The Master Algorithm” by Pedro Domingos. To summarize in a phrase, the master algorithm is that approach which can uncover all possible insight from data […]Read more "Andrew Ng’s DeepLearning.AI (Coursera) Certification"
Although the data science and big data buzzwords have been bandied about for years now, and although artificial intelligence has been talked about for decades, the two fields are irrevocably inter-related and interdependent. For one thing, the wide interest in data science started just as we were beginning to leverage distribute data storage and computation […]Read more "The Expert System Anachronism in the Data Science and AI Divergence"
I’m given to spurts of activity on Quora. Over the past year, I’ve had the opportunity to answer several questions there on the topics of data science, big data and data engineering. Some answers here are career-specific, while others are of a technical nature. Then there are interesting and nuanced questions that are always a […]Read more "Quora Data Science Answers Roundup"
Outliers are points in a data set that lie far away from the estimated value of the centre of the data set. This estimated centre could be either the mean, or median, depending on what kind of point or interval estimate you’re using. Outliers tend to represent something different from “the usual” that you might […]Read more "Simple Outlier Detection in R"
While there is justifiable excitement in the technology industry (and other industries) these days on the widespread availability of data, and the availability of algorithms to process and make sense of this data, I sincerely think (like many others) that the hype behind Big Data is somewhat unfounded. For many decades, “small data” have been […]Read more "Data Science: Beyond the Hype"