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Data Science and Technology Monthly - August 2015

Hello and Welcome back!

This series is my attempt to start cataloging all the interesting articles, industry reports, whitepapers, and news that I read every month, related to technology and data science. We are at Month 2 and let us dig right in -

1. Network Effects, Digital Avatars and Advertising

This essay titled "We are Data" was published in the Financial Times Magazine section and it was an interesting read even though it provided a pessimistic (or some may call it realistic!) view of what the social context of data collection entails.

The author talks about this hypothetical app Wonkr that will tell you what the dominant political view is of the people that are in the same room as you. Oh wait, of course don't forget network effects - everyone should have their profile on Wonkr. The social context in such data collection only exists due to network effects. And we have seen several examples like Facebook, Uber, Airbnb, Tinder, Grindr, Yelp, LinkedIn etc. tapping into that phenomenon merely because of the social context.

In fact, it is also for this reason that Facebook's Aquila and Google's Loon are heavily investing in providing increased access to the internet. Although there is definitely an altruistic side to these projects, the benefits from network effects are enormous. So you can never tell, if the companies are investing in these projects because they want to make use of the network effects or if they are truly altruistic.

Tying this back to the "We are Data" essay, the author claims that the enormous amount of meta-data we generate by being online, gives rise to this digital avatar of ours that lives a corresponding digital life paralleling us on the physical plane. And he is sure companies try to use that meta data avatar of ours to track us across devices and across platforms so that they are use us as advertising targets. Yay!

2. MIT Technology Review: Teaching Machines to Understand Us. (More on Deep Learning here...)

Yann LeCun shared this on his Facebook page as soon as it came out on MIT Tech Review. Put very simply, Facebook is building software that understands you and that can talk to you in a normal conversation. Deep learning is very capable in taking dictation and in image recognition, but Yann LeCun and his team are trying to make the AI master human language. 

Make sure to read the entire story here, because it goes into the history of how AI went in and out of disuse.

Also, if you have not yet seen Facebook's Deep Learning CGI software EyeScream, it's about time...

3. More AI, and s'more history

Bloomberg published an article recently where they interviewed all the top researchers in AI and shared anecdotes about what inspired them to get into this field. Here is a link to that: Here’s What Inspired Top Minds in Artificial Intelligence to Get In....

Along the same lines, the Future of Life Institute, who promote the cause of using technological advancement for the benefit of humankind, presented an open letter signed by tech luminaries like Elon Musk, Steve Wozniak, Stephen Hawking, among others (yeah, I signed it too). The open letter urges the world to ban the use of AI in the development of next generation weaponry. Instead, they provide a link to a paper they wrote, titled "Research Priorities for Robust and Beneficial Artificial Intelligence" where they list a lot of alternative worthwhile research topics for humans to pursue.

4. Machine Learning for Execs.

 McKinsey Quarterly had an article in their Q2 2015 edition that helps introduce machine learning to the execs. Link: Here. They enumerated some use-cases in regular business scenarios like talent management, training, pricing and market segmentation analyses where companies utilize techniques from machine learning.

Also, as ML functionality in companies evolve, the authors claim that the level of sophistication also evolve from merely describing with data, to predicting, and then to actually prescribing solutions to problems that business might face in the future based on what the data shows.

Another really good visual introduction to ML for execs can be found here. 

5. Public Data for fun

If you are looking for more public data that you can potentially use for some fun analysis, I recently stumbled upon Google's Public Data Explorer. They have tons of free data and some pre-designed interactive charts. 

That's all for this time. Check back again in a few weeks for more!

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Tags: artificial, data, intelligence, learning, machine, reading, research, science

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