Reflections–Andrew Walraven

1) Final Project Experience:  Our project certainly got off to a slow start, and we were probably a bit behind for most of the semester, but once we got on top of it, the results came out all right.  For me personally, working on this project really felt like working on a separate studio-level workload alongside my regular studio work.  It was a constant challenge to make sure deadlines were being met for both classes.  In the end, it was satisfying to see our project reach the point of a solid concept pitch, and now I have some new elements to add to my portfolio.

2) UX Class Experience:  Overall, it did not turn out the way I had anticipated.  My expectation was that this was going to be more of an instructional, skill-building course, where we learned how to work on the CS side of design (making apps, websites, programming, etc.)  Rather, it was a more abstract, concept-building course, focused on the “why?” not the “how?.”  It certainly was an interesting subject, and I enjoyed our discussions about AI, robotics, ethics of computer science and engineering, social science, etc.  After dipping my feet in a bit, I think that I don’t want to pursue UX design as a career path (instead, focus on product design), but I consider it valuable experience nonetheless.


Machine Learning/Artificial Intelligence

Intelligence on Tap:

The prospect of using Machine Learning or Artificial Intelligence as a “design material” is an exciting prospect, one with potential that we likely can’t even anticipate fully.  However, I think the challenges that the author brings up (and still others he did not) need to be addressed before ML/AI can/should be “on tap.”  These challenges are: Designing for transparency, Designing for opacity, Designing for unpredictability, Designing for learning, Designing for evolution, and Designing for shared control.  I think, especially that it is critical that users should always be informed of the use of these systems and the unpredictable nature they embody.  Also, I think it’s worth mentioning that no matter how “intelligent” these systems may be, they should, on principle, not be able to override the authority of its user.  This would make the creator of the system liable for any and all havoc caused by the machine.


Challenges for Working with Machine Learning as a Design Material:

“We did not see research investigating issues such as the impact of false positive and false negative responses from agents, or the need to collect ground truth labels, which might negatively impact UX.”  This is something that I had been thinking about when reading the previous article: It sounds so grand when people talk about the power and efficiency provided by machine learning associated with big data powerhouses like Amazon and Google, but what about when it is accidentally or intentionally trained poorly?  I think the chatbot experiments proved why this is a huge problem.  People may lie to the machine, which has no way of distinguishing true from false statements provided by its user.  It’s possible that there may be a way to train the machines to account for this in the future, but I assume that would mean teaching it to lie itself, to understand lying, which would be incredibly problematic and would destroy any trust in the machine that users could build up over time.
Machines Learning Culture:
For this article, I wanted to comment on the part regarding “normalcy.”  The Turing Normalizing Machine is designed to somehow identify what makes people “normal,” and the creators hope to decode the mystery of “what society deems ‘normal.'”  But here is the problem with this goal: It will never be truly solved.  Each person has their own beliefs and biases of what normal is, which in turn affects what they consider to be normal appearances.  In consequence, the machine will be getting flawed and contradictory data.  Sure, they can construct and image of what the machine thinks is the most “normal” appearance, but even if it could sample the data of every person alive, it couldn’t choose a form that satisfied everyone’s “normal.”  In other words, people may see the machine’s aggregate person and think it looks weird, and they would be neither right nor wrong, as what is normal to them is different to people of different locations, beliefs, age, gender, etc.  All this to say: It’s a fun little project, but will never “decode the mystery,” and has no real, practical value other than ironically pointing out all the differences in perspectives about “normal” that prevent it from being ever universally accepted.

“A Practical Solution”–Andrew Walraven

1) PDF Presentation: Walraven_Wallet

2) Tagline: “A Practical Solution”  –I picked this tagline, because I think it accurately describes the wallet design I developed.  It isn’t too much different from a standard bi-fold wallet, except that it makes it easier to reveal Identification, and it offers a practical solution to the problem of storing change for people who use cash.

3) My persona is a conglomerate person, based on the people I interviewed for feedback: Chad–A junior engineer, studying at Virginia Tech.  He is a busy man and tries to be as efficient as possible.  He likes his basic, bi-fold wallet well enough, but he wishes it had a way of quickly storing the change he collects when making a cash transaction.  Also, when he uses the Blacksburg Transit system, he dislikes having to unfold his wallet and pull out his ID card to show to the bus driver.

4) Post-It Note: “Change slot is mostly a feminine attribute; is this for a certain gender?”  My Response: First, I want to address the question and say that my user group was all male, but I didn’t really have a gender in mind when I was designing mine.  Second, I want to address the initial assertion that a change slot is “mostly a feminine attribute.”  Is this really true?  If so, why would that be the case?  From my perspective, a place to store change hardly seems like a gender-specific element, so I’m really not sure what you [the writer of the note] mean.

I (Andrew Walraven) am the only remaining member of this team, but originially was working with Hithesh Peddamekala.