In 2021 Jordan Peele remade the 1992 cult horror classic, Candyman. The 2021 remake received critical success and despite being delayed several times due to the covid-19 pandemic, was a box office success as well. In both the 1992 and 2021 versions, the eponymous main character is a black man. But in the remake, the character deviates from the usual narrative trope of being a menacing black man to a man with complex emotions and feelings. For most viewers, these changes make for a good story, but likely are not things that they dwell on, and certainly are forgettable by the time they have left the theater. But for our guest this week, literature MA student Marisa Williams in the School of Writing, Literature, and Film, these differences are what gives them inspiration and are what inform their research. While Marisa has just begun their thesis work, they know that they will examine issues of racism on black bodies within contemporary literature. Specifically, Marisa plans to explore how the legacy of colonialism has remained in the literature of French-Caribbean authors writing in the 21st century despite more than two centuries of emancipation from colonialism.
In order to do this kind of research, Marisa first has to learn about the history and philosophy of colonialism and post-colonial identity in the Caribbean. They plan to do this by exploring how notions of “Creole-ness,” the monstrosity of whiteness, and identity have all shaped the French-Caribbean experience in today’s literature. This has led Marisa to some interesting literary “rabbit holes,” that has taken them through history, philosophy, and fantasy literature.
To learn more about what is “Creole-ness,” the monstrosity of whiteness, and identity and how they relate to fantasy literature, tune in live on Sunday May 1st, 2022 on KBVR to listen. You can also catch more of Marisa’s story and research when they present as part of OSU’s 2022 Grad Inspire which will be taking place on May 12th.
Content warning: this article includes mentions of transphobia and suicide.
Rue Dickey found himself feeling helpless and frustrated upon reading the news about the onslaught of anti-transgender legislation sweeping the country this year. In the four months of 2022 alone, nearly 240 anti-LGBTQ bills have been filed in states across the United States. This skyrocketing number is up from around 41 such bills in 2018, and around half of these bills targeting transgender folks specifically. In February 2022, Texas governor Greg Abbott called for teachers and members of the public to report parents of transgender children to authorities, equating providing support and medical care for trans youth to child abuse – a move that made national headlines. It’s imperative that we understand the consequences of this wave of horrific and discriminatory legislation: a survey by the Trevor Project found that 42% of LGBTQ youth have seriously considered suicide within the past year alone, and over half of transgender and nonbinary youth have considered suicide.
Rue (they/he) graduated from Oregon State University in 2019, and they are currently the Marketing Coordinator for the Corvallis Community Center. They also develop and create content for TTRPGs, or Tabletop Role Playing Games. TTRPGs are role playing games in which players describe their characters’ actions and adhere to a set of rules and characterizations based on the world setting, and characters work together to achieve a goal or go on an adventure. They often involve improvisation and their choices shape the world around them. Think Dungeons & Dragons – many TTRPGs involve the use of dice rolling to determine the outcomes of certain actions and events.
Rue Dickey, 2019 OSU graduate and Marketing Director for the Corvallis Community Center.
Gaming as a way to crowdfund for a cause
Wanting to do something to help children and transgender people living in Texas, Rue decided to turn his passion for TTRPGs into a fundraiser. The online indie game hosting platform itch.io has been used in the past to create fundraisers for charities by bundling together and selling games. A few of Rue’s friends who run a BIPOC tabletop server have had experience with creating profit-sharing bundles using the platform in the past, so after he consulted them and walked through the steps, he set up a bund?ndraiser, Rue wanted to ensure that the money was going directly to transgender people. “At the time, a lot of the larger media outlets were encouraging people to donate to Equality Texas, which works to get pro-queer legislature through in Texas, but they don’t necessarily help trans folks on an individual level.”
After tweeting about the fundraiser and soliciting ideas for charities, he landed on two organizations in Texas that are trans-led and focused on transgender individuals: TENT (Transgender Education Network of Texas, a trans-led group that works to combat misinformation on the community level through the corporate level, offering workshops as well as emergency relief funds for trans folks in need) and OLTT (Organización Latina Trans in Texas, a Latina trans woman-led organization focusing on transgender immigrants in Texas, assisting with the legal processes of immigration, name changes, and paperwork.) Both charities serve transgender folks directly in Texas, and you can donate to the organizations by following the links we have included in the article. Both charities were thrilled to learn about the donation – for OLTT, it’s the largest single donation they have ever received, and they will be able to use it to perform needed renovations and expansions at their shelter facilities.
Since the fundraiser ended, Rue has been interviewed by several national news outlets, including NBC, Gizmodo, and The Mary Sue, as well as gaming-centric websites like Polygon, Dicebreaker, and GamesHub. Although they have received some harassment and nasty DMs, Rue says that the support from the community has vastly overshadowed the naysayers. Similarly, he spoke of the overwhelming rush of support from trans folks, queer folks, and allies to the movement in the face of structural legislation that seeks to harm trans people.
“It restores a bit of my faith in humanity to see that on a structural level, they are trying to get rid of us, but on a community level, there is support – there will always be a place to go and people looking out for you.”
Tune in at 5 PM on Sunday, April 24 for this special episode of Inspiration Dissemination. Stream the show live or listen to this episode wherever you get your podcasts! You can keep up with Rue and their games on twitter and itch.io.
Did you know humans have the ability to “taste” through smelling? Well we do, and it is through a process called retronasal olfaction. This fancy sounding term is just some of the ways that food scientists, such as our guest speaker this week, recent M.S. graduate and soon to be Ph.D. student, Jenna Fryer studies how flavors, or tastes through smell, are understood and what impact external factors have on them. Specifically, Fryer looks at the ways fires affect the flavors of wine, a particularly timely area of research due to the recent wave of devastating wildfires in Oregon.
Fryer at OSU’s vineyard
Having always been interested in food science, Fryer examines the ways smoke penetrates wine grapes. She does this by studying the ways people taste the smoke and how they can best rid the smokiness in their mouths, because spoiler, it has a pretty negative impact on the flavor. This research has forced her to develop novel ways to explain and standardize certain flavors, such as ashiness and mixed berry, as well as learn what compounds are the best palate cleansers. She will continue this research with her Ph.D. where she plans to figure out what compounds make that smoky flavor, and how best to predict which wines will taste like smoke in the future.
Through this work, Fryer has made some fascinating discoveries, such as how many people can actually detect the smoke flavor (because not everyone can), how best to create an ashy flavor (hint, it has to do with a restaurant in the UK and leeks), why red wine is more affected by smoke than white wines, and what the difference is between flavor and taste.
Fryer processing wine samples
Tune in live at 7pm on Sunday April 24th or listen to this episode anywhere you get your podcasts to learn about Fryer’s research!
And, if you are interested in being a part of a future wine study (and who wouldn’t want to get paid to taste wine), click on this link to sign up!
This week we have Colin Shea-Blymyer, a PhD student from OSU’s new AI program in the departments of Electrical Engineering and Computer Science, joining us to talk about coding computer ethics. Advancements in artificial intelligence (AI) are exploding, and while many of us are excited for a world where our Roomba’s evolve into Rosie’s (á la The Jetsons) – some of these technological advancements require grappling with ethical dilemmas. Determining how these AI technologies should make their decisions is a question that simply can’t be answered, and is best left to be debated by the spirits of John Stewart Mill and Immanual Kant. However, as a society, we are in dire need of a way to communicate ethics in a language that machines can understand – and this is exactly what Colin is developing.
Making An Impact: why coding computer ethics matters
A lot of AI is developed through machine learning – a process where software becomes more accurate without being explicitly told to do so. One example of this is through image recognition softwares. By feeding these algorithms with more and more photos of a cat – it will get better at recognizing what is and isn’t a cat. However, these algorithms are not perfect. How will the program treat a stuffed animal of a cat? How will it categorize the image of a cat on a t-shirt? When the stakes are low, like in image recognition, these errors may not matter as much. But for some technology being correct most of the time isn’t sufficient. We would simply not accept a pace-maker that operates correctly most of the time, or a plane that doesn’t crash into the mountains with just 95% certainty. Technologies that require a higher precision for safety also require a different approach to developing that software, and many applications of AI will require high safety standards – such as with self-driving cars or nursing robots. This means society is in need of a language to communicate with the AI in a way that it can understand ethics precisely, and with 100% accuracy. The Trolley Problemis a famous ethical dilemma that asks: if you are driving a trolley and see that it is going to hit and kill five pedestrians, but you could pull a lever to reroute the trolley to instead hit and kill one pedestrian – would you do it? While it seems obvious that we want our self-driving cars to not hit pedestrians, what is less obvious is what the car should do when it doesn’t have a choice but to hit and kill a pedestrian or to drive off a cliff killing the driver. Although Colin isn’t tackling the impossible feat of solving these ethical dilemmas, he is developing the language we need to communicate ethics to AI with the accuracy that we can’t achieve from machine learning. So who does decide how these robots will respond to ethical quandaries? While not part of Colin’s research, he believes this is best left answered by the communities the technologies will serve.
Colin doing a logical proof on a whiteboard with a 1/10 scale autonomous vehicle in the foreground.
The ArchIve: a (brief) history of AI
AI had its first wave in the 70’s, when it was thought that logic systems (a way of communicating directly with computers) would run AI. They also created perceptrons which try to mimic a neuron in a brain to put data into binary classes, but more importantly, has a very cool name. Perceptron! It sounds like a Spider-Man villain. However, logic and perceptrons turned out to not be particularly effective. There are a seemingly infinite number of possibilities and variables in the world, making it challenging to create a comprehensive code. Further, when AI has an incomprehensive code, it has the potential to enter a world it doesn’t know could even exist – and then it EXPLODES! Kind of. It enters a state known as the Principle of Explosion, where everything becomes true and chaos ensues. These challenges with using logic to develop AI led to the first “AI winter”. A highly relatable moment in history given the number of times I stop working and take a nap because a problem is too challenging.
The second wave of AI blew up in the 80’s/90’s with the development of machine learning methods and in the mid-2000’s it really took off due to software that can handle matrix conversions rapidly. (And if that doesn’t mean anything to you, that’s okay. Just know that it basically means speedy complicated math could be achieved via computers). Additionally, high computational power means revisiting the first methods of the 70’s, and could string perceptrons together to form a neural network – moving from binary categorization to complex recognition.
A bIography: Colin’s road to coding computer ethics
During his undergrad at Virginia Tech studying computer science, Colin ran into an ArachnId that left him bitten by a philosophy bug. This led to one of many philosophical dilemmas he’d enjoy grappling with: whether to focus his studies on computer science or philosophy? And after reading I, Robot answered that question with a “yes”, finding a kindred spirit in the robopsychologist in the novel. This led to a future of combining computer science with philosophy and ethics: from his Master’s program where he weaved computer science into his philosophy lab’s research to his current project developing a language to communicate ethics to machines with his advisor Hassam Abbas. However, throughout his journey, Colin has become less of a robopsychologist and more of a roboethicist.
Want more information on coding computer ethics? Us too. Be sure to listen live on Sunday, April 17th at 7PM on 88.7FM, or download the podcast if you missed it. Want to stay up to date with the world of roboethics? Find more from Colin at https://web.engr.oregonstate.edu/~sheablyc/.
Colin Shea-Blymyer: PhD student of computer science and artificial intelligence at Oregon State University
Basic biology and computer science is probably not an intuitive pairing to think of, when we think of pairs of scientific disciplines. Not as intuitive as say biology and chemistry (often referred to as biochem). However, for Joseph Valencia, a third year PhD student at OSU, the bridge between these two disciplines is a view of life at the molecular scale as a computational process in which cells store, transmit, and interpret the information necessary for survival.
Think back to your 9th or 10th grade biology class content and you will (probably? maybe?) vaguely remember learning about DNA, RNA, proteins, and ribosomes, and much more. In case your memory is a little foggy, here is a short (and very simplified) recap of the basic biology. DNA is the information storage component of cells. RNA, which is the focus of Joseph’s research, is the messenger that carries information from DNA to control the synthesis of proteins. This process is called translation and ribosomes are required to carry out this process. Ribosomes are complex molecular machines and many of them can also be found in each of our cells. Their job is to interpret the RNA. The way this works is that they attach themselves to the RNA, they take the transcript of information that the RNA contains, interpret it and produce a protein. The proteins fold into a specific 3D shape and the shape determines the protein’s function. What do proteins do? Basically control everything in our bodies! Proteins make enzymes which control everything from muscle repair to eye twitching. The amazing thing about this process is that it is not specific to humans, but is a fundamental part of basic biology that occurs in basically every living thing!
An open reading frame (ORF) is a stretch of nucleotides beginning with a start codon and ending with a stop codon. Ribosomes bind to RNA transcripts and translate certain ORFs into proteins. The Kozak sequence (bottom right, from Wikipedia) depicts the nucleotides that commonly occur around the start codons of translated ORFs.
So now that you are refreshed on your high school biology, let us tie all of these ‘basics’ to what Joseph does for his research. Joseph’s research focuses on RNA, which can be broken down into two main groups: messenger RNA (mRNA) and non-coding RNA. mRNA is what ends up turning into a protein following the translation by a ribosome, whereas with long non-coding RNA, the ribosome decides not to turn it into a protein. While we are able to distinguish between the two types of RNA, we do not fully understand how a ribosome decides to turn one RNA (aka mRNA) into a protein, and not another (aka long non-coding RNA). That’s where Joseph and computer science come in – Joseph is building a machine learning model to try and better understand this ribosomal decision-making process.
Machine learning, a field within artificial intelligence, can be defined as any approach that creates an algorithm or model by using data rather than programmer specified rules. Lots of data. Modern machine learning models tend to keep learning and improving when more data is fed to them. While there are many different types of machine-learning approaches, Joseph is interested in one called natural language processing . You are probably pretty familiar with an example of natural language processing at work – Google Translate! The model that Joseph is building is in fact not too dissimilar from Google Translate, or at least the idea behind it; except that instead of taking English and translating it into Spanish, Joseph’s model is taking RNA and translating (or not translating) it into a protein. In Joseph’s own words, “We’re going through this whole rigamarole [aka his PhD] to understand how the ins [RNA & ribosomes] create the outs [proteins].”.
A high-level diagram of Joseph’s deep learning model architecture.
But it is not as easy as it sounds. There are a lot of complexities to the work because the thing that makes machine learning so powerful is that the exact complexities that gives these models the power that they have, also makes it hard to interpret why the model is doing what it is doing. Even a highly performing machine learning model may not capture the exact biological rules that govern translation, but successfully interpreting its learned patterns can help in formulating testable hypotheses about this fundamental life process.
To hear more about how Joseph is building this model, how it is going, and what brought him to OSU, listen to the podcast episode! Also, you can check out Joseph’s personal website to learn more about him & his work!
