A Different Use of Time Series to Identify Seasonal Customers
I had previously written about creatively leveraging your data using segmentation to learn about a customer base.  The article is here.  In the article I mentioned utilizing any data that might be relevant.  Trying to identify customers with seasonal usage patterns was one of the variables that I mentioned that sounded interesting. ... Read more
Frontier AI: How far are we from artificial “general” intelligence, really?
Some call it “strong” AI, others “real” AI, “true” AI or artificial “general” intelligence (AGI)… whatever the term (and important nuances), there are few questions of greater importance than whether we are collectively in the process of developing generalized AI that can truly think like a human — possibly... Read more
There are many mixed opinions regarding the future of deep learning, and how far it can really go. Gary Marcus’s paper, “Deep Learning: A Critical Appraisal” overviews the social and more technical concerns with deep learning, and examines the possibility of it simply hitting a wall. Having only reached... Read more
Requests for Research
Table of contents: Task-independent data augmentation for NLP Few-shot learning for NLP Transfer learning for NLP Multi-task learning Cross-lingual learning Task-independent architecture improvements It can be hard to find compelling topics to work on and know what questions are interesting to ask when you are just starting as a... Read more
The Six Stages of Computational Science
This is the second in a series of articles related to computational science and education.  The first article is here. The Six Stages of Computational Science When I was in grad school, I collaborated with a research group working on computational fluid dynamics.  They had accumulated a large, complex code... Read more
Let’s start with machine learning In short, machine learning algorithms are algorithms that learn (often predictive) models from data. I.e., instead of formulating “rules” manually, a machine learning algorithm will learn the model for you. So, let me give you an example to illustrate what that means! Say you... Read more
Named Entity Recognition: Milestone Models, Papers and Technologies
Named Entity Recognition: Extracting named entities from text Named Entity Recognition (NER), or entity extraction is an NLP technique which locates and classifies the named entities present in the text. Named Entity Recognition classifies the named entities into pre-defined categories such as the names of persons, organizations, locations, quantities, monetary... Read more
Beyond Computational Reproducibility, let us Aim for Reusability
Scientific progress calls for reproducing results. Due to limited resources, this is difficult even in computational sciences. Yet, reproducibility is only a means to an end. It is not enough by itself to enable new scientific results. Rather, new discoveries must build on reuse and modification of the state... Read more
Web Scraping Indeed for Key Data Science Job Skills
Editor’s Note: Check out our 2017 State of Data Science Jobs Report to compare stats, sentiments, and POVs. *available in Spanish   As many of you probably know, being a data scientist requires a large skill set . . . Read more
The software engineering rule of 3
Here’s a dumb extremely accurate rule I’m postulating* for software engineering projects: you need at least 3 examples before you solve the right problem. This is what I’ve noticed: Don’t factor out shared code between two classes. Wait until you have at least three. The two first attempts to solve a problem... Read more
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