YAAKOV BRESSLER
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Analysis

non-peer reviewed research and data-informed opinions.
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My Takeaways from the Panel Discussion "A Playlist for Our Future? Human Advantage in an Age of Technology" @ Columbia University

12/6/2019

 
The discussion was hosted by Columbia University's Center for Science and Society – an organization dedicated to "exploring the intersection of science and society through innovative interdisciplinary methods." I've been to several of their events in the past and have always had a great time.

I was drawn to this particular event because of its description: "This panel will address artificial intelligence and which lessons from the humanities and the social and behavioral sciences are needed."​ A narrative about affecting progress, cool!
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The panel consisted of:
  • Lydia Chilton, a professor of Computer Science
  • Matthew Jones, a professor of History whose focus is on how technology affects society
  • Alison Lord, head of talent at Google Creative Lab
  • Mickey McManus, researcher fellow & computer scientist
  • Beth Comstock (moderator) author and former CMO of GE

A concern shared by all panelists was that of access, specifically, the designing of systems and policies which promotes inclusiveness. Given that algorithms are biased by their designers and their training data (as is the infamous case of gender bias in Apple-GS Credit Card), the question of how might we prepare for  an increased presence of machine learning (ML) and artificial intelligence (AI) becomes pertinent. 

Some of the interesting solutions discussed:
  • Diversify technology teams. In addition to ethnicity & gender, includes different types of thinkers. And cross-generational learning. (Allow young to teach the old – teaching is a form of learning too!)
  • Identify monopolized / controlling systems. As the internet of things becomes more complex, being informed on a system's affect will become an increasingly important skill. (E.g. know if your phone is taking up too much time in your life.)
  • Be aware of unintended consequences. Technology always has unintended consequences, many good, many bad. (E.g. randomized controlled trials in clinical research --> Tusgeecee Study) We as a society need to be aware of when we misstep and take reparative & preventative action.

One additional point: 
All panelists all agreed that the age of machine learning is here and that the age of Artificial Intelligence is almost here. (As soon as computation allows for it.) That said, we can shape our a future to be inclusive and enabling in ways no society has experienced before.

This event was jam packed with mind blowing content (such as the term NCLF  non-carbon life form) and attended by a highly engaged audience (the Q/A session was brilliant). I'd encourage you to participate in CSS's future events.

Be Careful with Arya Stark – GOT Makers Have Huge Responsibility

4/22/2019

 
In Game of Thrones most recent episode (S8 E2), teenage heroin Arya Stark (played by Maisie Williams) was portrayed having her first sexual experience, a highly anticipated moment for fans. Aside from the artistic and dramatic effects of a sexually enticing moment, I'd like to highlight the potentially devastating effects of portraying sexuality in young characters in films.
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How old is Arya Stark?  
According to the books, she'd be 18 by Season 8. Source.

How old is Maisie Williams? 
22. (Her birthday is April 15, if you want to send flowers.)

An 18 year old consented to have sex, why the conflagration? 
My issue is that in all likeliness, most of GOT's 17.4 million viewers don't know Arya's or Masie's age off-hand. For the time between their viewing the content and the viewer researching these ages, there is an image of an underage person choosing to have sex with someone well above their age. This can be devastating to adults who are coping with non-offending pedophelia, a condition which affects 1-3% of U.S. men (approximately 1.18 million to 3.54 million men).[1] Visual and audio stimulation of minors – especially those not explicitly pornographic – can be toxically triggering for these people, who unsurprisingly, feel discouraged from seeking professional help.[2] 
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The relationship between sexual interests and portrayals in film and TV is extremely related. To demonstrate, the worldwide Google Trends results for queries relating to Arya Stark, her age, and her sexuality in the hours following the release of S8 E2 are shown below. The scores for Arya Stark Nude [4-21] has spiked since the release of the episode.
What if Arya Stark would have said "I'm 18 years old, I'm adult enough to do as I please."
Without doubt, it would have likely strengthened the passion of Arya's moment and empowered her in the moments following. But more importantly, it would help the show's millions of viewers by providing clarity to consent – and show how verbal language surrounding consent can be empowering, sincere, and sexy.

The makers of Game of Thrones missed a valuable opportunity in this past episode. I challenge you to find solutions in content you're creating that are aimed at similar objectives of
portraying verbalized consent, thus sincerity.
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References:
1. Seto, M.C. (2013). Internet sex offenders. Washington, DC: American Psychological Association.
2. ​Howitt, D. (1995). Pornography and the paedophile: Is it criminogenic?. British Journal of Medical Psychology, 68(1), 15-27.

Measuring Success in Broadway

10/31/2018

 
I'll let the slides do the explaining.
Success in Broadway – Summary by Yaakov Bressler

My article "Opening the Stage Door for Big Data in Broadway — Building Databases from unstructured text using Machine Learning."

9/26/2018

 
I've spent a good portion of my summer devising a complex machine learning algorithm which extracts data for all Broadway shows, ever. This article summarizes my approach, and provides explanations, pitfalls, and insights. Also, the code for this algorithm is available through the article!

Opening the Stage Door for Big Data in Broadway - Building Databases from unstructured text using...

Live theatre is an exciting industry whose form and product remains an in-person experience, despite rapidly advancing technology. In contrast to this steadfastness, the business landscape of Broadway has become highly modernized with digital marketing, social media, e-ticketing, and dynamic pricing at the cutting edge of technological advancements within entertainment.

Broadway Weekly Grosses – Interactive

8/15/2018

 
Scrapped data, no statistical analysis conducted.

Dynamic Ticket Pricing Model for Broadway – Research Proposal

7/12/2018

 
So far this summer, I've been researching the mathematics and data science of dynamic modelling. My goal is to develop a model for maximization of revenue and attendance for Broadway shows. Articulated in the proposal below are the speculated means for which I hope to accomplish this undertaking.

– Give it a read?
– Let me know what you think?
– Feedback or ideas are warmly welcome!

Interactive Visualization for Broadway Grosses, Week of July 1, 2018

7/4/2018

 
The following interactive graph describes the revenue performance of last week's Broadway shows. Try clicking on the graph and see what comes of it. This is a learning experience for me so I appreciate all feedback you can provide! Send me an email if something stands out: yb@yaakovbressler.com

Weekly Gross versus Capacity (Week of July 1, 2018

The x axis describes the percentage of occupied of seats for a production.​ A value of "80" would connote that the production full-filled 80% of their seats. A score above 100% is achieved by selling standing room.

The y axis describes the weekly gross of a production for that week's performance. This includes all sales for that week's performances, regardless of when tickets were purchased. For example, if someone purchased tickets 2 months ago for a show within this week, that sale would be included in this week's data set.

The size of each point describes total attendance of that data point for the week. Of note, the largest (grey) point describes Harry Potter and the Cursed Child.
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Some extra info: 
  • Data is credited to the Broadway League.
  • All analysis conducted in R.
  • This visualization was done using hPlot in rCharts, by ramnathv.
  • I tried adding the title of each show to the individual bubble but couldn't hack it. Maybe next time...
  • The slope of the graph resembles an exponential function, beginning as 80% capacity. This finding is also demonstrated in other data sets I've been looking at. My goal over the next week is to calculate that equation, then summarize the contribution of decision making factors to that equation, using machine learning.
  • Education through DataSociety.com.
  • Free to share, with citation.​

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