artificial intelligence

Weekly Round-Up: Computer Vision, Machine Learning, Benchmarking, and R Packages

Welcome back to the round-up, an overview of the most interesting data science, statistics, and analytics articles of the past week. This week, we have 4 fascinating articles ranging in topics from computer vision to popular R packages. In this week's round-up:

  • Google Explains How AI Photo Search Works
  • Matter Over Mind in Machine Learning
  • Principles of ML Benchmarking
  • A List of R Packages, By Popularity

Google Explains How AI Photo Search Works

This is an interesting blog post about how Google recently enhanced their image search functionality using computer vision and machine learning algorithms. The post describes in layman's terms how the algorithms work and how they are able to classify pictures. It also includes a link to Google's research blog, where they made the original announcement.

Matter Over Mind in Machine Learning

This is a post on the BigML blog which talks about the work of Dr. Kiri Wagstaff from NASA's Jet Propulsion Laboratory. The post highlights a specific paper of hers where she argues that instead of aiming for incremental abstract improvements in machine learning processes, we should be focused on attaining results that translate into a measurable impact for society at large. More detail is provided about what that means, the author plays a little devil's advocate, and the post also includes a link to Wagstaff's paper for those that would like to read more about this.

Principles of ML Benchmarking

This is a post on the Wise.io blog about how to benchmark machine learning algorithms. The post is structured as a thought exercise where the author starts by thinking about the purpose of benchmarking, why we should do it, and what our goals should be. From that point, he is able to formulate a set of guidelines for benchmarking that are very logical. The post lists each of the guiding principles along with some steps that can be taken to make sure you are abiding by them.

A List of R Packages, By Popularity

Our last article this week is a post on the Revolution Analyitcs blog that lists the top R packages in order of popularity. Some of the most popular packages include plyr, digest, ggplot2, and colorspace. Check out the list, see where your favorite packages rank, and potentially discover some useful packages you didn't know about!

That's it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there's something we missed, feel free to let us know in the comments below.

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Weekly Round-Up: NSA, Data Science History, Best Practices, and Robot Writers

Welcome back to the round-up, an overview of the most interesting data science, statistics, and analytics articles of the past week. This week, we have 4 fascinating articles ranging in topics from the NSA's data collection practices to machines writing for the CIA. In this week's round-up:

  • Under the Covers of the NSA’s Big Data Effort
  • A Very Short History Of Data Science
  • 7 Habits of Highly Successful Big Data Pioneers
  • CIA Invests in Narrative Science and Its Automated Writers

Under the Covers of the NSA’s Big Data Effort

This is an interesting article about the types of technologies the NSA is using in their data collection practices and what they can and can't do with those technologies. The article also hypothesizes as to how much data they are able to collect and analyze.

A Very Short History Of Data Science

For those interested in how data science originated and has progressed up until current day, this Forbes article should be a worthwhile read. The article starts off in 1962 with John W. Tukey's paper titled "The Future of Data Analysis" and walks you through major milestones in the field up through September of 2012 when Tom Davenport and DJ Patil declared data scientist the sexiest job of the 21st century.

7 Habits of Highly Successful Big Data Pioneers

In the spirit of the 7 Habits of Highly Effective People, this Smart Data Collective article lists 7 habits for succeeding as a big data practitioner. The habits listed range from planning properly and making wise financial decisions when evaluating technologies to being flexible and adaptable when obstacles present themselves.

CIA Invests in Narrative Science and Its Automated Writers

This is an interesting article about a company called Narrative Science and their services that will be used by the CIA and the broader intelligence community in the near future. The company's product is able to transform data into sentences automatically and is currently being used to write up sports summaries from box scores and earnings reports from stock data.

That's it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there's something we missed, feel free to let us know in the comments below.

Read Our Other Round-Ups

Weekly Round-Up: Probabilistic Programming, Tech Startups, Data Viz Elements, and Super Mario Bros.

Welcome back to the round-up, an overview of the most interesting data science, statistics, and analytics articles of the past week. This week, we have 4 fascinating articles ranging in topics from probabilistic programming to machines playing video games. In this week's round-up:

  • What is Probabilistic Programming?
  • 5 Ways for Tech Start-Ups to Attract Analytics Talent
  • The Three Elements of Successful Data Visualizations
  • AI Solves Super Mario Bros and Other NES Games

What is Probabilistic Programming?

This is an interesting O'Reilly article introducing probabilistic programming. The article talks about what probabilistic programming is, how it differs from regular high-level programming, and intuitively explains how it works. The author also explains how he believes the technology's development will progress and the impact it will have on data science and other technologies.

5 Ways for Tech Start-Ups to Attract Analytics Talent

For those looking to hire analytical talent, this article provides some practical pointers for hiring a data scientist. These pointers focus on some of the softer skills that are necessary to really excel in these types of roles and also on structuring an environment where your data scientists are properly motivated to do their absolute best work.

The Three Elements of Successful Data Visualizations

This is a Harvard Business Review article about what elements are necessary in making great data visualizations. The article highlights three elements - understanding the audience, setting up and framework, and telling a story - and explains why each of these are important in a little more detail.

AI Solves Super Mario Bros and Other NES Games

This article is about an interesting and fun application of machine learning - teaching a machine to solve video games. It revolves around a paper written by computer scientist Tom Murphy about how he was able to accomplish this using lexicographic ordering. The article talks about Murphy's research and how he went about figuring out how to do this. It also has a link to Murphy's paper for those that would like some more in-depth reading on the subject.

That's it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there's something we missed, feel free to let us know in the comments below.

Read Our Other Round-Ups