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+Clive Thompson a very prominent technical author came to Rochester on
+November fifteenth to talk about the “Problems of Efficient Coding”.
+Going into this talk, I expected it to go along the lines of how
+making super “efficient” code often results in code that nobody
+understands and is hard to maintain. To my pleasant surprise, Clive
+Thompson provided a nuanced discussion around the cultural problems
+created when we try to optimize every problem using technology. 
+
+![Clive Thompson](media/thompson-talk.jpg)
+
+To understand Clive’s point, he used Facebook as a prime example of
+this problem. Before the Facebook feed system, the web largely acted
+like a blog where people had to actively reach out to everyone’s page
+to get content. Right after Facebook implemented the feed system there
+was a big debacle where nearly 20% of Facebook users entered a
+Facebook group opposed the new feed system. For nearly a week there
+were student protesters outside of the Facebook office. People
+initially found the feed system creepy because it gave everyone
+ambient awareness of everything happening in their network; this in
+some regards decreased “anonymity”. You no longer had to go out to
+every one’s page, Facebook created a tailored newspaper for you to
+consume. As a result of the new feed system, people started producing
+a lot more content to put on social media sites since people consumed
+it immediately. To filter content and only provide people with
+“important” posts, Facebook employed machine learning algorithms which
+favored posts that get more clicks. It turns out that people are very
+likely to click on things that are highly emotional or
+controversial--machine learning algorithms were quick to learn this
+and favor controversial content. People started to play the algorithm
+and turn Facebook into a hot take tire fire as it get littered with
+absurd conspiracy theories like #Pizzagate. Facebook’s motto used to
+be “move fast and break things”, however, after Zuckerburg was
+lambasted in front of congress, that motto is slowly changing.  
+
+
+Facebook like many tech companies creddits it’s major success to
+optimizing a sometimes niche problem -- this is something that
+programmers love to do and computers are perfect at. Facebook
+optimized how people consume media, but they did it at the detriment
+of quality content. Youtube tremendously optimized how we view videos
+by suggesting us recommended videos to watch, but, if often suggests
+repulsive content. Uber optimized how people found rides, but it
+resulted in an influx of part time drivers that are slowly pushing out
+full time drivers. This is not to say that optimization is a bad
+thing. As a result of optimizing tasks we can save a tremendous amount
+of time and be more productive members of society. Thompson suggests
+that there are certain cases where we should slow down and add
+friction to cases that we initially see the need to optimize.
+Reflection and deliberation are important things that are often thrown
+to the wind when we optimize things. 
+
+
+This now begs the question: how do we do we solve these issues? This
+is something that Thompson didn’t discuss in depth nor had a great
+answer for. We could point our fingers at governments, companies, or
+consumers and tell them to solve the problem. Surely having the
+government enact some well constructed public policy based on the
+current policy environment would solve the issues… right? The problem
+in the age of big data is that things are changing at a rapid pace and
+by the time we realize the dangers of a particular issue, it may have
+already caused grave damages or morphed into another form. Look at
+gambling for example, we have had decades of laws and regulations
+surrounding underage gambling, however, online gambling issues has
+been consistently creeping its way into policy discussion over the
+last five. It is fascinating that most public policy generated in the
+technology field is actually created in the court systems. This is
+good in the sense that the court system is often faster than passing a
+new law, but, it is also very problematic. Old laws when used to
+interpret a nuanced technological problem often yields outcomes that
+the original authors of the law would possibly disagree with. 
+
+
+Although Thompson’s talk raises more questions and problems than
+tangible easy to implement solutions, we must start having discussions
+like this so we can enact a cultural change around how we approach
+optimization tasks. Adding back careful reflection and deliberation
+back to currently optimized tasks on the internet could give us more
+freedom over how we consume content and interact with the world.