Lab Notes

Machine learning has changed, and will continue to change the world around us. Over the course of November, I spent time completing an online Lynda course titled, Deep Learning: Image Recognition by Adam Geitgey. In this course, I learned key concepts about neural networks and built on what I learned in last month’s machine learning course. By the end of this course, I was able to get a toy neural network working and understanding, at a basic level, the different components that play a part in making it function.

During my time at the Design Lab, I have been interested in how our work in Open Accessibility can help future students on campus. For example, in the lab we are currently developing tactile maps of the library for the visually impaired. A long-term goal of ours is to, not only produce maps for our university, but to create a workflow so that our research can help other universities do the same.

As a resident of Open Accessibility here at the Design Lab, I am interested not only in how to make spaces and resources more accessible at our school with the projects I work on, but also how to create workflows for other people – and other schools – to easily follow and do the same for their spaces and resources. The more automated things are, the easier it is for people follow it and do it themselves, especially those who may not have experience or simply do not have the time. So the best way, in my opinion, to bring open accessibility options to the public, is to automate as much of it as possible.

Coming into the Citizen Science arm of the Design Lab, I knew I had decent knowledge of one aspect of the activity: the science part. Having extensive education in scientific fields means that I generally understand what the strengths and weaknesses of science are, what a good data collection protocol looks like, and the sorts of things it can tell us about our world. What I joined knowing quite a bit less about was the “people” aspect of citizen science: the part where we have to engage people and communities to demonstrate how these activities can be beneficial, informative, effective and fun for them as well. One of the things that has allowed me to understand the people aspect of citizen science better was the Rackham DEI workshop on Entering Communities.

Aimi Hamraie visited University of Michigan, as a part of Disability Awareness Month, and shared her well-known book as Building Access: Universal Design and the Politics of Disability. Hamraie shared key points from her book such as strategies for designing and making things more accessible for those with disabilities. She shared history from the time people have been advocating for rights and accessibility to now. Lastly, she discussed her remarkable work at Vanderbilt University with an ongoing project revolving around participatory mapping, data collection, and Crip technoscience project that are based on principles of disability justice, intersectionality, and spatial practice to explore mapping as a tool for social justice.

When approaching projects on accessibility, I first wanted to understand my community. Who I am designing for? What goals am I trying to achieve? With these questions in mind, I chose to focus my attention on diversity in technology. More people are using technology than ever before. How can we then use technology as a tool to, not only acknowledge, but improve the lives of the diverse groups of people within our communities? The Michigan Meetings Fall Symposium took a closer look into these questions.

The Shapiro Design Lab seeks undergraduate students to become Audio Interns for the upcoming semester, to help develop the Lab's programs in audio production and post-production.

1. What brought you to the Shapiro Design Lab?
I was first introduced to the Shapiro Design Lab two years ago when the director, Justin Schell, served as my mentor for a U-M Library mini-grant project. Justin’s mentorship of our misfit group of wide-eyed, wanna-be social entrepreneurs was nothing short of pivotal to keeping our group together through our first pitch competition, Innovation in Action. In 2017, Justin told me that the next year he would be starting a graduate student residency program concentrating on video games and I have been lucky enough to be part of the Design Lab ever since.

Machine learning is changing the world around us and it will continue to dramatically shape our future. Complex computer algorithms are slowly starting to define our interactions with vehicles, social media, and Netflix. That’s right, even the shows Netflix recommends for you are, in part, the result of a complex machine learning algorithm.

Despite the omnipresence of machine learning in the world around us, the mystique that surrounds these two words is palpable. Many people forfeit trying to understand how machine learning works upon reading the word algorithm. I, however, decided to delve into the topic through an online course. In an attempt to better understand just what is happening during a machine learning program, I completed the Lynda.com course, “Machine Learning and AI Foundations: Value Estimations,” taught by Adam Geitgey. Participating in the course left me believing that, although machine learning is a highly complex process, one does not need to understand exactly what is going on under the hood of the algorithm, so long as they can navigate the program.

Imagine you slipped on a wet floor and fractured your feet. The doctor recommends you not to exert any pressure on your feet for the next 2 months. You still have classes to attend, assignments to submit and exams to write in these 2 months. You decide to stay strong and be brave enough to continue to go to school, instead of skipping the whole semester.
On your first day of class after the accident, you reach to your school only to realize that your class is on the third floor and the lift is out of order. What do you do?