Open Innovation Spring 2017

Demo

Anjali Patel and 3 OthersJack Martin
James Meade
Cobe Maldonado

Portfolio

Camren Meier
1 / 14

Slide 1

Slide 2 : Yoyoi Kusama is a Japanese wrist that uses her mental disorder and visions as an inspiration for her artwork. 

Slide 3

Slide 4 : This is another example of Yayoi Kusama's work. 


Portfolio

Fisher Williams
1 / 15

Elbat (Rolling Picnic Table 2.0)

Jack Martin and 2 OthersJames Meade
Cobe Maldonado
1 / 16

Fae and Me

Katie Miner
1 / 12

Fae and Me is a comic about a transgender man named Eddy trying to escape the clutches of his abusive ex girlfriend. I made all of the comic pages in photoshop, and hope to continue it so it looks more like the first and last slides and less of the fifth. It is currently 46 pages long, and I am not even finished with the plot! 

Process

Christian Cornejo
1 / 15

Our video is a collection of various interviews, conducted by us, of students and faculty members answering personal questions about human rights. This process consisted of numerous steps. The first step was coming up with a number of thought provoking questions that challenged the interviewee to personally reflect on them. Next, we came up with a diverse list of students and faculty that we thought would have interesting perspectives to present in the video. After planning a meeting time in the video production lab, we carefully set up the video camera, microphone, background, and lighting in a way that created the specific tone that we wished to evoke in our video. After this, we filmed the interviews, downloaded them to an editing software, picked the best answers to each question, and edited the video. Finally, we added the component of our project that makes it innovative and more interesting than a regular documentary: the delivery. This video can be presented using a regular screening format or our new, creative method of streaming. This method consists of two screens streaming the video, one displaying the different scenes around our school, and the other showing interviews. By presenting our video in this format, it allows the viewer to become more engaged in the content by forcing their brain to actively adjust its attention to various points of focus. Therefore, the viewer will be able to deeply absorb and process the information discussed. We are hoping that our video generates an empathetic response from the viewers and gives them a better understanding of the importance of human rights and social equality in our community as a whole. 

Black Sheep

Lizzy Tolentino <3
1 / 12

Black Sheep is an art installation that shows different people's perspective on Generation Z (those born from the late-1990s to mid 2000s). When preparing for the project, I began to reflect on my own views of the generation to come up with my concept. I was inspired by the philosophy of artist Robert Rauschenberg, who aimed to create cohesive art pieces with the most outrageous components. This made me think of the "black sheep" idiom. The idea of the black sheep used to be used as an insult; used to describe an outcast of the group or the one who doesn't belong. However, this idea of being different and standing out has evolved over the past decade or so, and has become somewhat of a goal to my generation. Everyone strives to be unique. To some this could be seen as a positive evolution. Yet, based on Rauschenberg's philosophy, if everyone in a group is trying to stand out, once outstanding characteristics become diluted.  With this dual view idea, I was inspired to create a piece that showed two opposing views of Gen Z simultaneously. 

I started brainstorming concepts  for the actual piece. I first looked into creating a dual projection screen.  However, this idea didn't seem to work. So, I went back to the drawing board. After much thinking, the second idea that came to mind was, oddly, inspired by cereal boxes. I remembered, from the early 2000s, how cereal companies  would occasionally print red reveal mazes, messages,  and puzzles on the back and sides of their product boxes, then seal a "magical reveal lens" inside the packaging to reveal hidden objects. I began researching red reveal messages, and began studying additive and subtractive light theory to create this effect. With this knowledge, I was able to figure out the technical side of my project. 

After I had this idea, I started designing the physical pieces of my project. I was greatly inspired by the The Beatles Sgt. Peppers Lonely Hearts Club Band  (1967)  album. I felt both the cover and the music  contributed to the message I was trying to get across, and I decided to base my visual components off of the album's cover. 

After I had these components, I attempted to piece them together. I decided to create the dual view in my piece by surveying the teachers and students of the high school, and compare and contrast the data I collected. Then, I would paste the pairs of messages in the head of each singular sheep, and give the viewers a red or blue reveal  lens to see the two different views separately.  I did not have time to build the entire piece, but I created a loose sketch of what it will look like when I continue this project in the future. For presentation day, I created an example for the message reveal effect, and the reveal glasses with the red and blue reveal lenses.  

Process

Anna Katros
1 / 11

At the end of last semester I (Anna Katros), was struggling to choose a project for the next semester (the one I am currently at the end of). I spoke with NuVu fellow Anjali and we came up with a wearable oxygen bar powered by Bioluminescence. Anjali then decided because we both expressed an interest in my previous project (Aquaponics), that Isabella Bogdahn should join me in this project. Our final project ending up being the Oxygen Wearable in which we dropped the bioluminescent algae for chlorella algae which is best for oxygen production. The Oxygen Wearable is a wearable skirt that links clear, algae filtered air to the wearer via tubing and pumps. 

This past semester, instead of Isabella and I belonging to a specifically themed studio, we were in Open Innovation in which we presented our idea to a NuVu fellow, and they decided to accept or decline our proposal. This new concept is one of the many ways NuVu can help to prepare us for the future. I spoke a lot with Ryan (a NuVu fellow) who is currently going through the process of gaining his PhD. This conversation as well as a trip to NuVu studios in Cambridge where I met the CEO of NuVu's advisor helped me understand the importance of a thesis. Ryan compared his PhD proposal, thesis, and dissertation to the way that NuVu is formatted. In Cambridge, I learned about the CEO, Saeed Arida's dissertation topic. The topic was based on original and extensive research just like our ideas for NuVu projects are. The amount of research Isabella and I found on growing algae for oxygen production was slim to none. But regardless of the amount of information, we still had to propose our idea and well in order for it to get accepted. This is when I realized that Ryan was right. NuVu is very similar to applying for your doctorate so therefore it can undoubtedly help prepare students for the future. What I also realized was how hard it was going to be.

Isabella and I researched vigorously to find out what kind of algae to buy, how to grow it, and much more. We found that the majority of research on algae was either its health benefits, or the use of it for biofuel. While biofuel seemed quite neat and a lot more sustainable than oil, we realized that we could not use this research to help us with our project. Nor could we use the health benefits of adding algae supplements to smoothies, unfortunately.  What we did use to get our project going was research on how to grow algae. Once we ordered Chlorella, we filled up a tank with water, nutrients (iron),  salt, and of course, the algae cultures and thus, we became algae farmers. 

We spent quite some time trying to wrap our heads around the concept we were trying to create. Eventually we created our first prototype, which was a geometric skirt in which we would put algae inside each panel. There would be tubes that connected to a tank (reservoir) that would pump algae throughout the skirt, agitating it. This was the same principle as our final prototype, except the change of shape and use of acrylic. We had a difficult time sealing the skirt and the placement of the holes for our tubing could be improved in future iterations. Overall, our project was a successful prototype and we have a clear path for future iterations.




Donut Arcade

Trevor Hallett
1 / 9

Final Portfolio

Zachary Frielich and James Eschrich
1 / 12

Our project is a computer program that uses Markov chains to generate a statistical model of  the style and content of an input text, and generates text based on that style. 

The purpose of this project is to explore what characterizes the style of a particular author, and to try and use that stylistic information to generate text. This type of information can be used both for text generation as well as identifying text. Identifying the style of a text has a variety of applications beyond text generation, including forensics (for example, matching up the writing of a criminal to text posted on social media)and history (for example, dating a document, or identifying whether a new document is a forgery). 

This project was inspired by several different existing projects that are looking at the intersection between Computer Science and art. One is Experiments in Musical Intelligence, or EMI, which is a computer program that can imitate the style of classical composers. This program can actually fool some into thinking that its compositions were composed by classical composers, like Bach. (http://www.nytimes.com/1997/11/11/science/undiscovered-bach-no-a-computer-wrote-it.html). Another precedent for this project is Google’s DeepDream project, which uses Deep Learning techniques to imitate the style of a painter and applying it to a given picture. This project isn’t designed to perfectly mimic the style of a painter, but it does learn and mimic noticeable aspects of the style of a given painter (https://github.com/google/deepdream). Some final precedents for our project are the computer programs which write simple news articles, created by companies like Automated Insights or Narrative Science, which write news articles based on preset templates, and neural networks which have learned how to write Shakespeare (https://www.wired.com/2015/10/this-news-writing-bot-is-now-free-for-everyone/, http://karpathy.github.io/2015/05/21/rnn-effectiveness/).  

The way the program works it that is uses a several different Markov chains, trained on different aspects of the text, to understand the style and content. We choose to use Markov chains, which are stochastic models that transition from state to state based on learned probabilities, because a lot of current research in this area uses machine learning. We wanted to see what the best model we could make with a simpler, Markov-chain based approach — setting it apart from many current research projects. However, unlike the spam bots or news writing programs, which utilize Markov chains alongside remade templates, our project learns and generates templates based on the input text. These templates can then be filled with words, either from the source text or from a different source (to apply the style to a different topic)

From a technical standpoint, our program works by separating the training and generation steps into three parts. The first part trains a Markov chain on transitions from punctuation mark to punctation mark, and generates a list of punctation marks to base the generated text on. For instance, it might add a transition from a period to a comma 25% of the time, from a period to a period 25% of the time, and from a period to a dash 50% of the time. It learns these transitions from the input text, and generates text uses a weight random number generator. The second part trains a Markov chain on transitions between periods and number of spaces. For instance, a period might transition to a series of 5 words 10% of the time, 2 words 5%, etc, etc. These transitions are also learned from the input text. The third part learns transitions from one word to the next. This part of the program is the most similar to previous Markov-chain based programs.