CreatorOS
The people, ideas, and movements who influenced the way I run creative projects today.
Albert Einstein
Albert Einstein was a famous theoretical astrophysicist. He is one of the most celebrated figures in all of science, most popular for discovering the general theory of relativity (Belanger). Einstein is famous not only for his accomplishments in science but for his views on life and the human condition. For example, Einstein was vocal about his belief there was more to discovery than merely being knowledge: “Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world” (Goodreads Writers). Einstein was the first person that helped me understand the importance of being curious, and that I too could encircle the world if I was willing to see it from more than my own perspective.
In my own work: this time last year I was in my first ever Computer Science class at Make School, taking an advanced course on tree data structures. I was searching for a way to learn a variant called the m-way tree, but it was just too abstract, and not really sticking. Thankfully, I realized the advice Einstein would give in this situation, and I imagined ways to use this data structure in my own work. As this took place right in the heat of the coronavirus pandemic, I built a disease simulator called Herd Immunity. Today this site is a freely available tool for students and researchers to run virtual experiments on how a population may respond to an epidemic virus — and it uses trees in the backend to make it all work! As a result of building this, I was able to help my old high school math teacher create a lesson plan for her students to learn about COVID-19, and I better appreciated the usefulness that imagination can have in technical pursuits.
The Toyota Production System
The Toyota Production System (TPS) was an efficient system for automobile manufacturing that was first developed, as the name suggests, by Toyota in post-WWII Japan (Braus). TPS applies to more than just manufacturing — it speaks to the principles behind how work can be done efficiently in creative environments. Software engineers on both sides of the Pacific have benefited from applying the principles of TPS into their teams.
In my own work, understanding the principles of the TPS directly helped me in one of my deep learning projects. I was once on a team where I was tasked with building consensus on an implementation decision for the AI that would go into the product, and our team just didn’t have rapport. With our development already nearly half-depleted, I borrowed what I learned about the TPS principle of nemawashi, or “piecemeal consensus” (Braus) to quickly bring the team from miscommunication to collaboration. We swiftly reached a decision, worked diligently for the next month or so, and finished the project on time. The end result of that work today is Tinydoor, a website that creates less-biased customer analytics for retailers using deep learning and computer vision. All it took was applying a little tact to improve the communication, and we went from a dysfunctional team to a working one.
Dr. Ed Catmull
Dr. Ed Catmull is a computer scientist and a businessman. He is most notable for being the co-founder of Pixar, and is currently the president of both Pixar and Walt Disney Animation Studios. I first came across the work of Dr. Catmull in his book Creativity, Inc. (2014), in which he compiles several memoirs that give advice on how to unlock the creativity of yourself and your team, through the lens of Pixar’s genesis in the ’80s and ’90s. Pixar’s award-winning films are created through the marriage of highly advanced computer graphics and evocative storytelling, and Catmull’s book influenced me by opening my eyes to how computer scientists do not necessarily have to confine themselves to doing solely technical work.
My own work with technologically created art came into play at the Data Science Club I participated in during college. I sensed that everyone in the Zoom meeting, most of whom still did not even have their cameras, needed something to break the ice.
“Hey everyone! I have a random idea. How about we take a group photo, and I’ll post the picture in Slack using neural style transfer?”
To those unfamiliar, neural style transfer is a relatively new field of machine learning research, which looks to see how AI can be used for creating art. The gist of it is basically presenting the AI with a piece of existing art, letting it pick up on patterns found in that artwork, and applying them to a new image. In my case, I thought I could liven things up in the Data Science Club by using neural style transfer on our group photo, combined with Wassily Kandinsky’s “Composition 7”. Combining Kadinsky’s work with our club photo resulted in the following image, posted on the Make School Students Slack:
Perhaps it is needless to say, however, folks in the club really enjoyed it! That led to some buzz in the Data Science Club (over Slack and Zoom of course). Nonetheless, I like to think it brought our group closer together.
While today I am uncertain if I want to create art at the level of an animator working under Dr. Catmull, I do believe art created through technology can add a little creative spice to my work in machine learning; and creates something to relate better with my family, friends, and the users of my products in the future.
Open Source Software
My last influence is open source software (OSS). Open source software is any kind of software whose underlying source code is made available so that anyone can read, use, or modify it for free (Dewey). When I first came to Make School, I felt trepidation about the accelerated pace of the program, and if I would really be able to become a software engineer in just two years. What I did not understand yet was that Make School courses directly leveraged the vast amounts of code that is open-source, and freely available on websites like GitHub. It was incredibly empowering to learn with the help of OSS, and the sense of community that it has brought to software engineering. It enabled me to rapidly pick up web development by learning how to use tools like Django and Flask, which are free to use and have vast amounts of documentation and communities to go to for support. Today Django powers nearly all of the web development projects in my portfolio, and it was vital to the development of Carbon0, the startup I worked at on my first software development internship.
In my own work, OSS touches most areas of software development, including machine learning. One of the open-source projects that are most important to the progress of AI is Tensorflow, a free open-source library for building models of artificial intelligence created by Google. For example, at Carbon0 (where I did the aforementioned internship) we determined that it would be beneficial to build a computer vision service for our users called Plant Vision, an AI which can assess the health of plants just based on images. My task was to build this AI model, more specifically called a neural network. I did so by directly transferring knowledge from another neural network called Inception V3, (also open-sourced by Google), and modifying them to fit our use case. Since I had used so much open-source software to build the AI, such as Tensorflow and Flask-RESTPlus, I decided it would only be right to give back. The PlantNet API is today free for all to use, and of course, the source code can be found on GitHub!
For the full polymodal essay, click here.
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