The folks at #dariah Teach have put together a first of a series of videos on My Digital Humanities. Despite appearing in it, the video seems very nicely produced and there is a nice mix of people. Stéfan Sinclair and I were interviewed together, something that isn’t clear in the first part, but will presumably become clear later.
The New York Times has a video series called the Retro Report. One story is about Dungeons & Dragons: Satanic Panic. It looks at the media fed moral panic that eventually lost steam. It ends by praising all the “leadership” and “moral” skills learned. Now experts are recommending “free play” and … ironically … role playing games are now the solution!
I particularly recommend the two Keynotes by Jeffrey Schnapp and Genevieve Bell. You can see my conference notes here.
I was part of the panel on Building Communities and Networks in the Humanities.
CBC and others are reporting on a new Nintendo Creators Program where Nintendo will take a percentage of the ad revenue associated with a YouTube channel or video with playthroughs (Let’s Play) of their games. See YouTube gaming stars blindsided by Nintendo’s ad revenue grab or Nintendo’s New Deal with Youtubers Is A Jungle Of Rights. This will
In the past, advertising proceeds that could be received for videos that included Nintendo-copyrighted content (such as gameplay videos) went to Nintendo, according to YouTube rules. Now, through this service, Nintendo will send you a share of these advertising proceeds for any YouTube videos or channels containing Nintendo-copyrighted content that you register.
This program is only for “copyrighted content related to game titles specified by Nintendo”. This is probably because Nintendo has to be careful to not be seen as making money off playthroughs of other publisher’s games.
This new policy/program raises interesting issues around:
- Fair use. Is a screen shot or a whole series of them that make up a playthrough covered by “fair use”? My read is that the publishers think not.
- Publicity from Playthroughs. YouTuber’s like PewDiePie who post Let’s Play videos (and make money off their popular channels) argue that these videos provide free exposure and publicity.
- New Economic Models for Gaming. Is Nintendo exploring new economic models tied to their copyright? Nintendo has been suffering so it makes sense that they would try to find ways to monetize their significant portfolio of popular game franchises and characters.
Michael pointed me to a story about how Stanford scientists put free text-analysis tool on the web. The tool allows you to pass a text (or a Twitter hashtag) to an existing classifier like the Twitter Sentiment classifier. It then gives you a interactive graph like the one above (which shows tweets about #INKEWhistler14 over time.) You can upload your own datasets to analyze and also create your own classifiers. The system saves classifiers for others to try.
I’m impressed at how this tool lets people understand classification and sentiment analysis easily through Twitter classifications. The graph, however, takes a bit of reading – in fact, I’m not sure I understand it. When there are no tweets the bars go stable, and then when there is activity the negative bar seems to go both up and down.
Alexis C. Madrigal has a fine article in The Atlantic on How Netflix Reverse Engineered Hollywood (Jan. 2, 2014). The article moves from an interesting problem about Netflix’s micro-genres, to text analysis of results of a scrape, to reverse engineering the Netflix algorithm, to creating a genre generator (at the top of the article) and then to an interview with the Netflix VP of Product who was responsible for the tagging system. It is a lovely example of thinking through something and using technology when needed. The text analysis isn’t the point, it is a tool to use in understanding the 76,897 micro-genres uncovered. (Think about it … Netflix has over 70,000 genres of movies and TV shows, some with no actual movies or shows as examples of the micro-genre.)
Madrigal goes on to talk about the procedure Netflix uses to create genres and use them in recommending shows. It turns out to be a combination of content analysis (actual humans watching a movie/show and ranking it in various ways) and automatic methods that combine tags. This combination of human and machine methods is also the process Madrigal describes for his own pursuit of Netflix genres. It is another sense of humanities computing – those procedures that involve both human and algorithmic interventions.
The post ends with an anomaly that illustrates the hybridity of procedure. It turns out the most named actor is Raymond Burr of Perry Mason. Netflix has a larger number of altgenres with Raymond Burr than anyone else. Why would he rank so high in micro-genres? Madrigal tries a theory as to why this is that is refuted by the VP Yellin, but Yellin can’t explain the anomaly either. As Madrigal points out, in Perry Mason shows the mystery is always resolved by the end, but in the case of the mystery of Raymond Burr in genre, there is no revealing bit of evidence that helps us understand how he rose in the ranks.
On the other hand, no one — not even Yellin — is quite sure why there are so many altgenres that feature Raymond Burr and Barbara Hale. It’s inexplicable with human logic. It’s just something that happened.
I tried on a bunch of different names for the Perry Mason thing: ghost, gremlin, not-quite-a-bug. What do you call the something-in-the-code-and-data which led to the existence of these microgenres?
The vexing, remarkable conclusion is that when companies combine human intelligence and machine intelligence, some things happen that we cannot understand.
“Let me get philosophical for a minute. In a human world, life is made interesting by serendipity,” Yellin told me. “The more complexity you add to a machine world, you’re adding serendipity that you couldn’t imagine. Perry Mason is going to happen. These ghosts in the machine are always going to be a by-product of the complexity. And sometimes we call it a bug and sometimes we call it a feature.”
Perhaps this serendipity is what is original in the hybrid procedures involving human practices and algorithms? For some these anomalies are the false positives that disrupt big data’s certainty, for others they are the other insight that emerges from the mixing of human and computer processes. As Madrigal concludes:
Perry Mason episodes were famous for the reveal, the pivotal moment in a trial when Mason would reveal the crucial piece of evidence that makes it all makes sense and wins the day.
Now, reality gets coded into data for the machines, and then decoded back into descriptions for humans. Along the way, humans ability to understand what’s happening gets thinned out. When we go looking for answers and causes, we rarely find that aha! evidence or have the Perry Mason moment. Because it all doesn’t actually make sense.
Netflix may have solved the mystery of what to watch next, but that generated its own smaller mysteries.
And sometimes we call that a bug and sometimes we call it a feature.
On December 1st, 2011 Wikileaks began releasing The Spy files, a collection of documents from the intelligence contractors. These documents include presentations, brochures, catalogs, manuals and so on. There are hundreds of companies selling tools to anyone (country/telecom) who wants to spy on email, messaging and phones. I find fascinating what they should about the types of tools available to monitor communications, especially the interfaces they have designed for operatives. Here are some slides from a presentation by Glimmerglass Networks (click to download entire PDF).
The New York Times and the National Film Board (of Canada) have collaborated on a great interactive A Short History of the Highrise. The interactive plays as a documentary that you can stop at any point to explore details. The director, Katerina Cizek, on the About page talks about their inspiration:
I was inspired by the ways storybooks have been reinvented for digital tablets like the iPad. We used rhymes to zip through history, and animation and interactivity to playfully revisit a stunning photographic collection and reinterpret great feats of engineering.
For the NFB this is part of their larger Highrise many-media project.
The University of Alberta has put together a set of short (60 second) lectures by faculty on what they do. See Arts in 60 seconds and ignore my one.
The New York Times has a fabulous new interactive visualization called Reshaping New York that shows how Bloomberg has changed the city of 12 years. It shows new buildings, the rezoning, the introduction of bike lanes, and the celebration of the waterfront. The visualization is more of a tour that combines a 3D model of the city with images of before and after Bloomberg.