Waypoint 1 - 9/16 Pre-Class Reflection
During these first two sessions, I found communication to be most effective when in small groups. More specifically, communication was most effective when we had broken into smaller groups to work on a given assignment. The format made it easy to share one's understanding as well as for people to ask clarifying questions as needed. One person's understanding became the group's understanding very quickly. In contrast, in larger group formats, or in inter-group formats, where back and forth communication was slower or not possible, this was far less effective. The ability to ask clarifying questions and to have a back and forth between members was integral for successful communication. Even when creating supposedly comprehensive directions, there were little things that the authors considered implicit that were lost, resulting in a various different mistakes. The ability to quickly get clarification would have solved this very effectively.
As far as features that support communication, the biggest thing that comes to mind, both from the activities and from this reflection, is that communication needs to be open and reciprocal. It is incredibly important to be able to ask questions, and have a brief discussion about them. I would argue, it is far more important to be able (feel comfortable/have space) to ask so called "trivial" questions. Quick clarifying questions are often the most efficient at resolving confusion in previous communication. Everyone makes mistakes, and no explanation or set of instructions is going to be perfect. Being able to ask, "what did you mean by this?" or "are you sure about ____" is incredibly important for ones understanding. For communication to be effective, it must go both ways. Simply sitting and listening to a lecture (or the written equivalent) is far, far less effective.
Waypoint 2 - 9/23 Networking
To form a networking connection, I reached out to Andrea C. Villanti at the Rutgers University - New Brunswick. I selected specifically because I could apply an "algorithm" with which I was already familiar.
To break down that process slightly more:
Research a topic of interest, ideally related to a current area of research/study.
Identify a person who has published in that area, the more recently the publication the better.
Read the work in question carefully.
Reach out to the author(s) in question, ideally with genuine questions/interest regarding their work.
Most academics will eventually respond, as they tend to really enjoy talking about their work.
In this case, I was introduced to their recent paper "Public Comments and Federal Rulemaking for Menthol Cigarettes and Flavored Cigars" through my current work. I am currently working remotely as a research assistant in lab looking into the same FDA Docket, if with a different process and research question. Naturally, when this paper was published, the group noticed. Since it is in an area related to something I am currently working on, I believe I was in a position to ask more potentially interesting questions. Additionally, even if this did not result in a long-term professional relationship, I would probably be able to glean some insights useful for my current work.
Andrea Villanti is listed as the first author on the paper, so I reached out to her. I have not yet heard back. However, I do not find this particularly surprising, especially given the short time frame. Academics tend to be a little bit hit or miss on whether or not cold emails actually get answered, particularly when they come from outside their institution. And when they do get answered, it can often take a little bit. So, I remain hopeful that I will hear back at some point.
Waypoint 3 - 9/30
Forrest - Genetic Algorithms
- Modeled after the process of evolution
- Process
o Create random population
o Judge fitness
o Recreate population based on best members, allowing for mutation
o Repeat
- Argument components
o Initial independent sampling via randomization
o High fitness individuals are preserved, improving overall fitness
o Crossover combines elements from multiple strings, increasing probability of success
- Implicit parallelism
- Schema Theorem – genetic algorithms guarantee exponentially increased samples of the best schema in the next step
- Wildly applicable
o Multiparameter function optimization most common
- Not universally successful
Moseley – On Dice Games:
- People throughout history have continually tried, and failed, to meaningfully replicate variants of art via mechanical constructions
- Wurfelspiele: German dice games
o Referencing a specific one or the category?
o Operates as a form of ludomusical machine
- As musical machines have advanced, their failing to surpass human creativity becomes even more stark
- Games as a way to reframe the unknowable
- Such constructs often emphasize an air of mystery in their function
- The rules/restrictions of such games, while patently arbitrary, can encourage creative problem solving
- Does music have a functional baseline/algorithm that can be meaningful taught/programmed?
- Successful versions of artificially generated music heavily rely on human creativity/intervention
While both pieces are deeply invested in the algorithimic process, they take a very different approach. The Forrest piece focuses on a specific space in which algorithims, while sometimes amorphous, have concrete start and end states, as well as no need for human intervention beyond its conception. In contrast, the Moseley piece is most concerned with an area in which pure algorithims fail. In the field of music, while you can create certain baselines or rules to guide creation, every interation that is not ultimately reliant on human creativity is doomed to be reduced to all but gibberish. This piece is about an experimental field, rather than an established one.
I feel like these pieces are fundamentally quite different. The Forrest piece provides a firm description of the process for solving a certain set of problems while the Moseley peice, in contrast, serves as more of a theortical framework through which algorithimic attempts at creative generation may be viewed. They are both interesting, if for very different reasons. The former is overtly applicable to a variety of computational problems, both solved and not. The latter, in turn, opens space for a more playful discussion over the fundamental necessities of creative works. I find this piece more gnerally interesting to consider in the present moment, with the way LLMs are changing aspects of many artistic industries, including music. The most successful of these attempts, that I have seen, as the essay describes, reuslt from a commbination of mechanical generation and human intervention and creativity.
Waypoint 4 - 10/7 Computational Thinking
Problem solving is simultaneously an incredibly generic term and one with myriad different meanings. At its basest form, problem solving is the identification of an 'issue' within a given context and the steps taken to resolve that issue, both the identification/planning and the execution thereof. Under this definition, 'initiative' serves as the critical step/character trait that allows one to move beyond identification and planning and into resolution. On another level, one could also say initiative is necessary for the other parts of parts of problem solving. One could say it takes initiative to identify a problem and come up with a reasonable solution.
The three different thinking frameworks we have discussed, computational, design, and entrepreneurial thinking, all center around this general process, identifying a problem and generating a solution for a given aim or in a given way. They differ chiefly in those aims and methodologies. Given my background in and general familiarity with computer science, I am most drawn to the general framework of computational thinking, more than the other two. Since the 'issues' I end up addressing are usually chiefly technical in nature, I am encouraged to try to break down problems into their individual components. While practically, many more elements of the three end up overlapping, computational thinking encourages more of a focus on functionality- just getting the thing working. Looking at an issue from another lens, one might instead want to focus on aesthetics and human-facing results. This more mirrors the ideas of design thinking. In turn, one might instead focus on the end product and fiscal viability, creating a solution that doesn't just solve the problem, but solves it in a way that people will want to buy and pay for. This more closely mirrors entrepreneurial thinking. In a practical setting, the three types are going to be constantly overlapping and tugging against one another for influence in the final product. One might first focus on solving the problem, then making it human-facing, then modifying it to be a product that people actually want. They all have their place and uses.
Waypoint 5 - 10/28 Problem Solving Reflection
I have slightly vague choices for the problems I want to discuss.
A Problem I felt engaged with - In Project 3, modifying existing image generation tech to get a result that would translate well into jewelry.
A Problem I felt (and still feel stuck on) - Maintaining this website and updating it with each project/activity.
I have found maintaining this website consistently to be incredibly difficult. Genuinely the hardest part of each Project for me has been trying to keep extensive documentation and writing up an exhaustive overview at the end. It is hard to articulate exactly what is difficult about this. On one hand, there is the public facing element, particularly considering that people outside of our class may be looking at at. On another, constantly updating the website kind of feels less like an assignment or problem to solve (even if it very much is for me), and more like a chore that I'm really want to avoid.
To take a moment to consider the problem(s) I actually have enjoyed engaging with thus far, I think the chief difference between those problems and updating this website is one of scale/time frame. The problems that I enjoyed working on tended to be somewhat contained. In the problem from Project 3 I describe above there is a specific starting point and a specific end goal. Moreover, in that problem, much of what I was doing was novel to me. This was true both on a project to project scale, as Project 3 introduced me to new technologies (such as Fusion 360) that I had not used before, and on an intraproject scale, as the process of solving the given problem required applying a lot of different techniques and doing a bunch of different things. Over the course of Project 3, I worked with Gemini, Photoshop, various conversion softwares, Fusion 360, BambuLabs and more. To update the website, I am doing the same thing over and over: typing and occasionally adding an attachment. Interestingly enough, I found the kind of writing writing that I do on here notably harder than say, writing an essay. I think documentation is a fundamentally different style of writing to essays and is one I enjoy far less.