Readings #2

Intelligence on Tap: Artificial Intelligence as a New Design Material

Artificial Intelligence, AI, is becoming more and more accessible to non-experts which is why it could be the next big utility for designers. I must question the desire for it, do designers want to use AI? Wouldn’t that take away from some of the creativity that designers love to incorporate into their products? Incorporating AI will redefine what constitutes a “unique” design in many ways. I am interested to see the rate at which design practices will shift when AI is widely implemented into new products. The article mentioned there was a big learning curve when designs went from paper to screen and I believe utilizing AI is a much larger jump.

UX Design Innovation: Challenges for Working with Machine Learning as a Design Material

Machines can learn from large datasets through reoccurring trends and explicitly separating what is “correct” for a given situation and what is “incorrect”. Will machine learning be able to take tendencies, likes, dislikes, and other characteristics of a person and cater to a specific person’s needs? I feel some people are afraid of machine learning because it doesn’t feel personal, in other words, they just like another line in a huge dataset. Every UX design has a certain demographic related to a persona created by the designer. Machine learning will be more welcoming to less technologically savvy users when it can take datasets from individuals and quickly create a comfortable environment for them. Sort of like an episode of Black Mirror where a company cloned the customer’s consciousness to provide the perfect home assistant. The assistant knew all the customer’s preferences because the assistant was an exact copy of the customer’s current state.

Machines Learning Culture

Art is a term that does not have a single definition. People who deem themselves as artists all specialize in a certain form of art and relay it to the world to express themselves. So why is the integration of technology and machine learning all of the sudden excluded from the idea of art? As a computer science (CS) student, I have realized that CS is an ambiguous topic to people who study it and those who do not. Someone could describe it as a science meanwhile someone else could describe it as an engineering practice. The interpretations are endless and now I can see why someone would deem CS as a form of art. I have seen beautiful code before and although I wouldn’t know what that looked like five years ago, it doesn’t make that code any less beautiful. Therefore, I think that machines built with beautiful code can and will create beautiful art. The art created by machines won’t be any less or any more artistic than an art piece created by a human.

Readings

Intelligence on Tap: Artificial Intelligence as a New Design Material

This article brought up some good points about AI’s uses and limitations.  It discussed how it is important for a designer to understand what AI is good and not good at, similar to use of materials.  I think understanding when not to use or limit AI is just as important as its use.  Every product does not need to predict what the user wants, and if it has that functionality, it becomes important for the user to understand why the machine made the decision that it did.

 

UX Design Innovation: Challenges for Working with Machine Learning as a Design Material

I think it was interesting how this article said how designers often see AI and ML as “black magic.”  As a designer this “black magic” often is in other complex systems as well (electronics, production techniques)  when a designer does not have a thorough knowledge of specifics of a process.  I know I definitely have made some projects where I was like “the circuit board will go in there” without actually knowing what exactly that entails.  AI and ML are very similar, in that designers need to gain an adequate understanding of their strengths, uses, and limitations in order to use them in their designs.  It is important that designers gain this understanding of AI as its uses broaden in the future.

 

Machines Learning Culture

I thought this was interesting how AI can evaluate art and culture, but does it in a very different way than humans.  As humans we usually look at art and culture with a context of emotion, connecting things with that human perspective. AI does not do this and evaluates things much more mathematically.  This could have interesting applications as a tool for artists to make possible decisions that they would not initially think about.

Second Readings

In response to these articles on Artificial Intelligence and Machine learning I will start by delving into this on a personal note. I play way too much of the video game Fifa 18, which is created by EA sports. There is a huge community around this game, and recently there has been mention and an uproar about how EA might be editing you gaming experience based off of data they collect from you. You can find the post here. Now there is a lot of skepticism on whether this is actually a well informed former employee of EA, or just a frustrated keyboard warrior.

The article basically states that EA collects data on how you game, when you game, how often you game, the last time you played the game and if you are on a winning streak or not. Recently there was an opportunity for players to connect their twitch (live streaming app/website) accounts to their EA accounts. And the article goes into a more tinfoil hat type argument that EA could be gaining information on you from Amazon. Amazon owns twitch so theoretically they could be selling your shopping habits and other bits of data to EA through twitch. What the post concludes is that based off of all the data that EA collects on you they can alter your drop rates in the packs (lootboxes) that you open.  This is one of those cautionary tales of how machine learning using big data can be dangerous and unethical.

The European Union passed a law called General Data Protection Regulation in 2016, which will go into effect on the 25th of May. Companies will be forced to disclose what information they have on you when you request it and you can also request that it be deleted. This will be a very interesting day for machine learning since it learns from big data.

Readings

I know that for many people, the second they hear ‘AI’ they begin blowing it out of proportion, by picturing the robots from the movie 2001: A Space Odyssey. But in reality, it surrounds what we do already on a day to day life, which is why it works so well as a design medium. Since it was introduced gradually into peoples lifestyle, first as online bots, then later to in-home smart devices such as Google Home or Alexa, people did become frightened by it. While describing the challenges of using AI as a design material, the one that resonated with me the most was that AI should not overstep it’s boundaries. For many people, all it takes is one instance of an AI that went farther than expected for them to denounce AI and reject all technology based off it. Even if the problem was resolved, they would have lost their trust in the technology completely. This is why they must be Designed for “..transparency ..opacity .. unpredictability .. learning .. evolution .. shared control” (Holmquist, 5).

 

Whenever reading about or using Machine Learning tools, I never considered the user experience aspect of the technology. I always assumed that ML would only be on the backend, never to be touched by users other than through API calls. Before I read the scholarly article by Dove, Halskov, Forlizzi, and Zimmerman, I pictured an application (which was what the user interacted with) that sits between the user and the machine learning back-end. One of the largest difficulties of using it as a medium appears to be the lack of understanding of the medium itself. Participants that were surveyed by this did not understand what Machine Learning was, in that the “Participants uniformly described difficulties in understanding what ML was and how it worked”(Dove, 282). In a way, that is how I saw it as well at first. I would choose from a list of available algorithms, upload some data, and suddenly receive results that were relevant to me. But after learning the science behind it, I can see more areas this technology can be applied to, which is exactly must happen with the UX designers as well, which this article concludes.

Machine Learning Readings

Intelligence on Tap

I think this article does a very good job at outlining some issues with designing with machine learning.  My favorite part is when he addresses “AI coming of age.”  He really hits the nail on the head about the GPUs and massive data sets paving the way to allow for machine learning to really thrive.  However, I do think that this should raise some concern.  Consider who has massive data sets available to them.  I sure don’t.  The need for large amounts of data raises the barrier of entry for designers.  If we want to utilize AI in a project, we are limited by the data available to us.  This means that we can’t just add AI powered components to a product unless the data we have access to allows it.  A possible solution is to gather our own data, but this is a catch-22.  The device will need a large number of user to get the feature to work, but in order to get a large number of users, we need the working feature.  Therefore, I thoroughly agree that services to supply AI to the common man will be available in the near future, just look at Amazon Web Services and Google Cloud, they are already starting.  But I am still skeptical about how to integrate AI into everyday products due to the private nature of collected data.

UX Design Innovation

I think that the main takeaway from this paper is that machine learning is hard to integrate into design projects.  One thing I would specifically like to comment on is that it is difficult to prototype with AI.  Consider autocorrect on your phone.  This is a design feature that is deeply rooted in machine learning, and it has gotten better over they years.  But it didn’t start out that way.  It took years to train, and it doesn’t even work perfectly now.  I machine learning is to be implemented seamlessly, in a more critical position in some other project,  then the device simply would not work before the model model is trained.  This means that early in the design process the feature will seem less powerful than it really is because there is no way that a prototype has access to the same data as a product that has penetrated the market.

Readings #2

Intelligence on Tap, by Lars Erik Holmquist

In this article, Holmquist clearly explains how artificial intelligence works while referencing how an AI beat the world’s best Go player. He explained how the AI learned from playing the game against others and ultimately himself, learning different tactics and such. I find that AI are able to learn from interacting with others the most interesting aspect of this subject. I remember in Freshman year, I researched and wrote an essay about the AI named Tay. Tay was an AI given an account Twitter and she was able to learn from interacting with other Twitter. What started off as an innocent and fun idea, turned sour really fast as Tay was turned into an AI that started tweeting racist and horrible slurs against others… all because she learned from the “trolls” of the internet. It is incredibly exciting to able to begin looking at AI as a design material and see what sort of advancements and new products/objects/systems can be developed, however it doesn’t hurt to be wary of how society can influence a machine that is trying to pick up some human behaviors.

UX Design Innovation

As I read through UX Design Innovation, I could completely relate to the research done with surveying UX designers, as some stated they felt that machine learning was near “black magic.” I personally have never done much digging about how machine learning could be utilized further as a design tool, or would require designers. Any research I have done as been on my free time and I never took the information I learned further than what was stated in any articles or videos. However, after reading this paper, there’s a lot more potential and need for designers to step in and design along side engineers machine learning objects. But there is much needed compromise on both parties to try and communicate findings and how even prototyping would function on this topic.

Machine Learning Culture

I have never really thought about it, but I have experienced machines learning culture in relation to art quite a few times in the past few years. Being from northern Virginia, I have always made numerous trips to Washington D.C. to explore the city and the museums it has to offer. As I read through the Body Tracking and Provocative Art Installations portion of this paper, I realized that I have constantly experienced machine learning in the D.C. museums I explored in these past years, and never made the connection. I can think of two museums that would display collections that would contain some form or aspects of machine learning: ARTechouse and the Hirshhorn. Both museums brings in modern and contemporary collections from all over the place,  and although I cannot specifically remember a display (I’m sure it’ll come to me soon enough), it’s amazing I have never thought about how machine learning is even integrated into museums I love exploring.

Reading #2

Intelligence on Tap:

I really enjoyed this article.  I like reading any article that discusses the evolution of AI and what it could be in the future.   While I do like the concept of AI, the thought of where AI could advance kind of worries me.   I believe that the advancement of AI could bring a lot of ethical conundrums into play because in a sense AI is giving a machine a brain of its own.  But how is that brain any different than that of a human?  I feel with the advances that AI is making that soon humans and AI will soon be indistinguishable, so will we have to make new ethical laws for AI or will they have to abide by human laws?

Machines Learning Culture:

I found this article to be very interesting.  I am actually in the Creative Computing Capstone right now, so I like to see the ways that Machine learning was integrated into the arts.  I never considered Machine learning to play a role in the visual arts, but this article proved me wrong.  Knowing that now that it can play roles like this, I would like to see other ways that Machine learning can be implemented.

UX Design Innovation

Machine learning is a fascinating topic to me.  I’ve seen how it is used and implemented, and I believe that it is a great advancement in technology.  My only concern is that it may be integrated into a part of human society that does not require the technology.  At some point, humans need to realize that all things must be automated.

 

Reading #2 Response and Ethical Implications

AI as a New Design Material:

The author importantly notes that neural networks require huge amounts of learning data. Additionally, the data needs to be differential: both correct and incorrect so that the network learns the difference. I have seen many people make false assumptions about machine learning due to not understanding this principle. Due to the ubiquity of the buzz words, many equate ubiquity to its implementation. However, a great deal of work is involved which the author addresses such as defining correct solutions, time allotment to train and test the neural network, and immense data acquisition. You require twice the amount of data you might initially assume you need, not to mention that humans might have to prepare. The author notes some interesting solutions to this such as image recognition during a login screen, which I had not even considered to be for computer vision.

 

As a side note, another function for GPUs, which the author does not reference, is mining for bitcoins. Hence, with such high demand for GPUs concurrent with both AI and bitcoin especially, prices have become astronomical, with supply remaining intentionally low.

 

Back on track, another interesting concept the author notes is the magnitude of data tech companies harvest from people. They measure virtually all of your behaviors when you are using their products. This brushes on the principle of privacy; as more devices become sensors on our behaviors, to what extent these devices influence us when recommending our future behaviors, such as through advertisements or recommendations? It is interesting to see the author takes the approach that more machine learning is preferable. Clearly, the more data the better for the network, but the cost is people’s personal privacy. When one designs AI, one must also take into consideration the ethical implications involved. Are people consenting to the use of their information? Does profit emerge at the expense of privacy? Will nefarious actors use this information? The analogy to coffee brewing does not suffice. The author should have mentioned how designers must also take into consideration the security of the learning system they build. How are they preventing others from extracting the data? How might they prevent another machine from learning from the machine they taught? One might consider these scenarios when building the necessary infrastructure.

 

Challenges for Working with Machine Learning as a Design Material:

I appreciate that the authors note that unfamiliarity with ML is a source of apprehension to integrating ML. I sympathize due to my own unfamiliarity. I have not been exposed to machine learning in my CS curriculum, as the class has a few prerequisites and is not required, and as such understand that while ML grows in pertinence, curriculum always has to catch up rather than pioneer. Thus, a solution I find interesting is that classes such as these are integrating ML rather than dedicating an entire semester to parse its inner workings.

 

The authors also briefly mention an interesting field that could have many ML applications: medicine. The article specifically mentions detection of depression. Personally, I have seen many interesting projects try to address condition detection with intelligent systems. One such project involved an Xbox Kinect and Microsoft’s Azure cloud services. A user would pace back and forth a certain number of times to detect, if I remember correctly, stroke or some other kinds of brain injuries. In reference to the first article, this is another example of gaming technology influencing the capability of other non-entertainment fields.

 

Another interesting topic the authors discuss is the comfortability. Again, we should also consider the expense of pandering to the consumer. What if the machine learns to perpetuate culturally insensitive behaviors? What if, as in the case of Microsoft, the machine learns to be racist? The machine has a potential to encourage or reinforce societal degradations. Further, should we then be obligated to train out such behaviors, even if users are more complacent with a machine that matches their behaviors?

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.

Artificial Intelligence

Information and data as a product

I don’t only believe we are in an age of technology. I believe that what corporations, designers, and people overall are in an age of information transaction. Consumers are constantly giving away their personal data to these big corporations without even realizing it. This can be very good for companies and designers that use this information to design products and services that can be catered to their exact needs. But, at the moment seems to be non regulated which could have detrimental effects on society. This also opens the gates to a new market which is data. How will that effect other markets?

UX Design Innovation: Challenges for Working with Machine Learning as a Design Material

I believe that humans a computers can work hand in hand in this new age. There are certain things that machines can achieve that humans can not and vice versa. It could be a system where we and the machines compliment each other. I do also believe there is a fine line to where we must question what the limits of that are and the impeding applications. Humans may seem like they want of tasks simply to be completed by a computer but Im not convinced we as a society are ready to give that much power to something we wont fully be able t understand. All living creatures were created to carry out daily tasks and I feel that we will ever be comfortable to be reliant on something other than ourselves.

Machines Learning Culture

Theres something about the arts that allow us to interpret everything with a sense of fantasy. At the end of the day any art we create is an interpretation of a reality. With that in mind the only pressing question that comes into my mind is what is going to be the difference from human interpretation and artificial intelligence. I also believe bringing that technology an integrating it into our perception (augmented reality) might actually take away from the experience. I don’t see augmented reality as training wheels but more as a crutch because i fell we eventually will have a more difficult time to interpret the information we read in the instillations. I would love to see its application in a case by case study.