Algorithms Need Managers / The Curious Case of iPod Shuffle

Algorithms Need Managers, Too summary

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Companies today are turning more and more to computer algorithms, which perform step-by-step analytical operations at incredible speed and scale. Algorithms make predictions more accurate—but they also create risks of their own.

 

Algorithms have many shortcomings and the article provided many examples of instances where and an algorithm produces undesired results. Here is one of the better ones … “Another example comes from social media. Today many sites deploy algorithms to decide which ads and links to show users. When these algorithms focus too narrowly on maximizing user clickthroughs, sites become choked with low-quality “click-bait” articles. Click-through rates rise, but overall customer satisfaction may plummet.”

 

Sometimes algorithms are too literal. To get the most use out of an algorithm it must be used correctly and managed carefully. The article also refers to algorithms as “black boxes” that provide incomplete information with no explanation for their output. Example: “An algorithm can read through every New York Times article and tell you which is most likely to be shared on Twitter without necessarily explaining why people will be moved to tweet about it.”

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Knowing that algorithms have their shortcomings allows us to use them more efficiently.

When using algorithms the manager must be explicitly clear. A manager can include “soft goals” in their algorithm to refine its results. Algorithms also suffer from myopia, or near sidedness. They seek to get their job done as fast as possible. If a manager is not careful, this might result in a dip in quality for the sake of speed.

 

When choosing the data inputs for an algorithm remember 2 things: the more data, the better and diversity matters.

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Algorithms, when used properly, can improve a users experience. I discovered this first hand with the evolution of the “shuffle” option in iTunes.

I still use my first ipod (it has a wheel to navigate the library). For years I have been putting my ipod on “shuffle” and been amazed by the truly random mix of songs that plays. But things started to change when I began using my iphone as a music player; something seemed off. When I pressed shuffle on my iphone I no longer heard a random mix of smooth jazz and death metal. Instead, I noticed that my iphone was playing the music I wanted to hear, and that freaked me out. I would catch myself wondering, “How does my phone know I want to listen to Norah Jones?”

Well after a bit of research my fear of the Skynet takeover was put to rest. It turns out the “shuffle” option on newer apple products is no longer truly random. Instead, the ipods in our phones use algorithms to identify the type of music we have been listening to lately. Our phone then takes this information to create a “random playlist” that is not random at all. In fact, it is tailored specifically for us based on our listening trends. My phone knew I wanted to listen to Norah Jones because I have been listening to her all week.

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Go figure. ¯\_(ツ)_/¯

7 comments

  1. I found the news about the more recent “shuffle” algorithm to be interesting. I wonder what other algorithms have gone through iterations in Apple’s products and also how other music streaming platforms like Spotify & Pandora handle the “shuffle” feature.

  2. Great summary of our reading, and I love that you supplemented it with your discovery about the shuffle feature on Apple music players. Algorithms are certainly a huge step in the right direction for effective computing, but I think they’re often mistaken for a solution rather than a tool. They’re really good at giving us answers, and giving them to us fast, but the “why” is what matters, and that’s something we often have to figure out for ourselves. The examples of ads on social media and shareability of NY Times articles perfectly illustrate that. I also definitely noticed the decreased randomness of shuffle when I started using my iPhone in place of my iPod. I didn’t think it was intentional by Apple, and it was kind of surprising to find that it was. I personally wish there were separate options for personalized “shuffle” and truly “random.”

  3. Nice summary of the article. I did know that the original iPod uses a random shuffle, but didn’t know they moved away from this. I think the best algorithms build in some sort of randomizing ,so that we don’t get stuck in one set of ways.

  4. Good post. I actually like the how the new shuffle type feature works. I noticed it recently when a friend of mine mentioned the same thing. I am a shuffle type person because I like to hear different songs I like, but I hate when I have too much random music I have to sort through, so I like the “not so random” shuffle. But if companies learn to use the algorithm based data better, it can pay in efficiency

  5. Interesting. Thanks for the summary, I didn’t know about the new shuffle algorithm. I think it kind of defeats the purpose of playing all your music on shuffle; if I wanted to hear what I’ve been listening to recently, I’d just go to my Recently Added playlist! In a way this must become a sort of self-absorbed cycle where what you’ve listened to recently makes it appear more on shuffle, which in turn makes it recent. I think I’m getting too in my head here. Anyway, I propose we start a campaign to #MakeShuffleGreatAgain.

  6. Very interesting post. I, too, use the shuffle option extensively on my phone and I have been since I had a green iPod mini. I think algorithms are very unique in predicting different things based on a number of variables. Most recently, I saw a Game of Thrones algorithm that has been pretty good with predicting who would be next to die on the show.

  7. Speaking of algorithms…. great post! You were abel to give such a comprehensive guide to the technology as well as some good insights. I particularly liked where you mentioned that shuffle isn’t random. I have noticed that recently as well, as many of my favorite songs will now play on shuffle first and there are some in my library that won’t be played at all. Good work!

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