Navigation and GPS

Every two weeks, I go to the grocery store that’s 0.5 miles away from my house. That’s a 6 minute walk and a 3 minute drive. I’ve been living at my current house for a few months now, and you’d think I would be able to get there on my own, but every time I get into the driver’s seat, I find myself opening up my Google Maps application in order to navigate myself there.

Even though the grocery store is just 0.5 miles away, I still want Google Maps to tell me where to go. Not because I don’t know my way there, but because I think Google Maps knows better. It knows the route I know and it also knows multiple more. Plus, through analytic technologies that I don’t have, it is able to calculate a live flow of traffic and provide me faster-route recommendations.

This is just one example of how integrated GPS/navigational systems have become with our daily lives. It’s also the most common implementation that comes to mind when thinking about GPS/navigational systems; however, its range in implementation does not stop there. GPS systems can be implemented into so many different areas that we normally do not even consider. They are extremely versatile and can be found in almost any industry sector. They can be used to deliver real-time data during races, to help emergency crews locate disaster relief sites, to help farmers harvest their fields, to update customers on their purchased goods’ shipping statuses, to locate friends at a social gathering, to keep a visual journal marked by location and more.

When looking at all the different ways GPS systems can be implemented for consumer use, it’s interesting to note that GPS (a Global Navigation Satellite System (GNSS)) was originally developed by the U.S. Department of Defense. An early satellite-based system was running as early as 1960, but it wasn’t until 2000 that GPS became truly open to the public. In 2000, President Clinton signed a bill ordering the military to stop the intentional degradation of public GPS signals. This instantly upgraded the accuracy of the few consumer-based systems already in existence by a factor of 10, and opened the doors to a much larger, consumer electronics-based industry for GPS navigation.

In remembering this moment, I can’t help but to think of how much power the U.S. Department of Defense had and continues to have. GPS is the only fully functioning GNSS in the world and because it is managed by the U.S. Department of Defense, the U.S. Department of Defense has the power to deny GPS service at anytime. Right now, users are able to utilize the system free of cost, but how will the future look? Why are users being allowed to utilize the system free of cost? For larger economic benefits? To increase use and identify ways to better the system? How has the system been bettered since 2000? How much has information intake changed and how is the information privately being used by the U.S. Department of Defense?

Don’t get me wrong, I love GPS, but doesn’t the fact that the U.S. Department of Defense is the sole controller of the only fully functioning GNSS in the world concern you a little bit?

Let me know your thoughts! Am I being too critical? Or am I onto something?

Emotive Display and Social Robotics

In the beginning of creation (of robots), robots were designed to do things and go places that humans are not able to do nor go to. They explored deep oceans, traversed through volcano interiors, and even went to Mars!

Sojourner, the first Mars rover.

Sojourner, the first Mars rover.

Now, with the next generation of robots, the designs are changing to become even more complex. The robots are being designed to do things and go places that humans do/go all the time. Enter: social robotics.

Big Thoughts About Big Data

Before we go into Big Data, let’s talk about machine learning. Machine learning is a branch of Artificial Intelligence (AI) that works to build systems that are able to automatically improve with more experience, more data. Once designed, they don’t need to rely on explicit programming. Through identifying patterns in the data, they are able to improve the accuracy of their predictions on their own.

Machine learning algorithms have been around for a long time; however, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. We are facing a data revolution where the volume, variety, and velocity of available data has grown exponentially (Big Data). This growth in data has allowed for Big Data and machine learning to come together. As Machine Learning needs large amounts of data to “learn”, Big Data has played a Big role.

It has made it possible to produce models that can analyze bigger, more complex data and deliver faster, more accurate results on a very large scale. Systems are able to learn user’s interaction patterns, their interests, their likes and dislikes, and more. It has made it possible for every user of such systems to have optimized user interactions with individualized experiences.

Big Data and machine learning is changing and evolving the world around us.

Just a few years ago, if I wanted to discover new music, I would read through pages of Pitchfork and create playlists for myself based on my readings. Now, all I have to do is go onto my Spotify. There, fresh new playlists with songs that I’ve never listened to, but based on the type of music that I have been listening to, are created for me every week.

I appreciate Big Data and machine learning for moments like these. They’re allowing for more efficient human-centric designs that truly understand the users’ wants and needs.

However, something that I’ve been thinking about lately is the impact of our society on machine learning. Since the data that systems are collecting and analyzing are human behaviors, the algorithms that are based on them will inevitably be an extension of those behaviors. Not just the innocent ones, but the violent ones too.

For example, in the aftermath of Michael Brown’s death, St. Louis police turned to HunchLab, a predictive policing software that identifies “hot spots” where crimes are most likely going to occur. They turned to the software because they thought it would help them be more objective in their policing, but something that they didn’t consider was that HunchLab was learning from previous crime reports that hold histories of bias. The software learned to identify majority black and brown neighborhoods as high-risk “hot spots” to police. And because it kept sending police to those areas, they reported more crimes from those areas and caused a feedback loop of over-policing just majority black and brown neighborhoods.

It’s examples like this that make me wonder, how can systems be built to protect against learning discrimination? Can they be built?

Future Humans

At the Tisch Family Galleru and Koppelman Gallery, there is an ongoing exhibition called “States of Freedom: The Figure in Flux” that showcases different artists’ works celebrating the human form as an unstable amalgamation of histories, technologies, and cultures.

The exhibition invites you to ask yourself questions such as “What is the human body?” “What happens when we enhance the human body?” and “When does the human body stop being the human body?”

We are living in a time where technology is only expanding. As technology expands, so does our meaning for what constitutes a human body. This notion does not only encompass a human body with prosthetic limbs, but also experimental brain implants and prosthetic organs that can take the place of one’s spleen, pancreas or lungs. Although these advancements are extremely important and invaluable to those with medical needs for these prostheses, what happens when prostheses become so enticing that instead of being the substitute, they become the desired replacement? The average adult human body is 50-65% water. In the future, will the average adult human body be 50-65% integrated technologies?

Perhaps not that high of a percentage, but I think the changes that technology will bring to what constitutes a human body is inevitable. Since our creation, humans have always been looking for ways to adapt to adversities, survive and thrive. We’ve always been creating tools to extend our abilities. Clothes to add another layer to our bodies and keep us warm. Spears as extensions of our hands to hunt with. Medicine to alleviate and/or cure illnesses that our body can’t fight on its own. Phones to be able to communicate long distances. The Internet to expand our brain’s wisdom. Utilizing technology to enhance our body and mind is nothing new.

In envisioning what the future might look like, I’m reminded of another art installation called A. Human. It is an exhibit produced by Society of Spectacle that brands A. Human as a "a futuristic fashion brand" that "displays body modifications" instead of clothing. Could this be our potential future for fashion?

Glow SS19, worn by Kim Kardashian. The necklace glows like a heartbeat.

Glow SS19, worn by Kim Kardashian. The necklace glows like a heartbeat.



Signal Detection and Information Theory

There is this person I have been meaning to ask out on a date, but I always end up just saying “See you later!” instead. In trying to simplify Signal Detection Theory and imagine how Signal Detection Theory rears its head in my personal life, I keep coming back to this interaction that ends up in me not pursuing this person.

Signal Detection Theory is much more straightforward than its haughty title would suggest. It stems from the fact that nearly all reasoning and decision making occurs in the presence of some uncertainty. Signal Detection Theory holds that the detection of stimulus depends on both the intensity of the stimulus and the physical and psychological state of the observer.

SDT.png

Its data is presented in a 2x2 matrix with four possible outcomes. The four outcomes are Hit, False Alarm, Miss, and Correct Rejection. Hit means that the signal is present and the observer is able to correctly detect its presence. False Alarm means that the signal is not present, but the observer incorrectly detects that it is present. Miss means that the signal is present, but the observer is unable to detect its presence. Correct Rejection means the signal is not present and the observer correctly does not detect its presence.

Going back to my earlier example, whenever I speak to this person - let’s call him Will - we always have really witty banter together. There is something about our conversations that feels like a signal is present telling me to “YES! ASK HIM OUT!” but the signal never feels intense enough for me to actually ask him out. As I did/do not ask him out, I was/am either missing an opportunity to go on a wonderful first date or correctly rejecting a very awkward conversation. Frankly, the latter option is deterring enough as it is that I don’t mind missing an opportunity to go on a date with Will. Signal Detection Theory has to do with the process of gauging how strong a signal is and gauging the risks associated with acting on the presence of the signal in order to make a decision about something.

In regard to design, Signal Detection Theory is a handy means of assessing a system’s functionality. It reminds us to reduce noise and increase the intensity of our signals in order to reduce the chances of misses and false alarms, especially in high risk situations.

Task Analysis

What is a task analysis? Let’s break it down (pun intended).

“Task” can be defined as an action that needs to be performed. “Analysis” can be defined as a separation of a whole into its component parts, a detailed examination of something. In the case of task analysis, that something being separated into its component parts is the action that needs to be performed.

Task analysis is a step-by-step look into the sub-actions that occur when someone is performing a task. It is extremely important to conduct a task analysis prior to design work as it helps you be better informed about the structure your design should have.

Let’s do a real-life example to really bring this concept home!

Task: Brushing your teeth

Brush Teeth.png

Step 1. Grab your toothbrush from its location.

Step 2. If you are not already near the bathroom sink, make sure you are near a sink with a faucet.

Step 3. Once you are near a sink with a faucet, grasp the faucet handle.

Step 4. Turn the faucet handle to open the water tap.

Step 5. Holding your toothbrush, face the bristle side of the toothbrush up.

Step 6. Hold the toothbrush with the bristle side up under the water flow in order to wash and rinse your toothbrush.

Step 7. Grasp the faucet handle.

Step 8. Turn the faucet handle to close the water tap.

Step 9. Grab your toothpaste from its location.

Step 10. Turn the lid of your toothpaste counterclockwise to open it.

Step 11. Place the toothpaste lid down.

Step 12. Squeeze some toothpaste out onto the bristle side of the toothbrush.

Step 13. Pick the toothpaste lid up and place it back on the toothpaste’s opening.

Step 14. Turn the lid of your toothpaste clockwise to close it.

Step 15. Put the toothpaste back in its location.

Step 16. Open your mouth into a wide smile.

Step 17. Bring the toothbrush up to your teeth.

Step 18. Scrub the outside surfaces of your upper teeth with the bristle side of the toothbrush.

Step 19. Scrub the chewing surfaces of your upper teeth with the bristle side of the toothbrush.

Step 20. Scrub the inside surfaces of your upper teeth with the bristle side of the toothbrush.

Step 21. Scrub the outside surfaces of your lower teeth with the bristle side of the toothbrush.

Step 22. Scrub the chewing surfaces of your lower teeth with the bristle side of the toothbrush.

Step 23. Scrub the inside surfaces of your lower teeth with the bristle side of the toothbrush.

Step 24. Scrub your tongue with the bristle side of the toothbrush using small strokes.

Step 25. Take the toothbrush out of your mouth.

Step 26. Spit out any toothpaste and saliva in your mouth.

Step 27. Grasp the faucet handle.

Step 28. Turn the faucet handle to open the water tap.

Step 29. Hold the toothbrush with the bristle side up under the water flow in order to wash and rinse your toothbrush.

Step 30. Run your thumb through the bristles, still under the water flow, in order to thoroughly wash and rinse your toothbrush.

Step 31. Put the toothbrush back in its location.

Step 32. Cup your hand under the water flow to gather some water in your hand.

Step 33. Bring your hand, filled with water, to your mouth.

Step 34. Open your mouth.

Step 35. Tilt your hand up to let the water cupped in your hand to fall into your mouth.

Step 36. Rinse your mouth with the water.

Step 37. Spit out the water.

Step 38. If you still feel some toothpaste residue in your mouth, perform Steps 32 - 37 until you no longer have any toothpaste residue left in your mouth.

Step 39. Grasp the faucet handle.

Step 40. Turn the faucet handle to close the water tap.

Step 41. Smile to show off your freshly brushed teeth!

As you can see, although it might not seem like it, there’s a lot of sub-actions that occur while brushing your teeth. It is important to lay all these steps out prior to design work because they help identify what requirements are needed in the structure of your design and help identify which areas can be provided additional support through your design. As the goal of designing is to design something that is as easy as possible for the user to use, conducting a task analysis allows you to see which areas, which sub-actions, can be automated so that the user doesn’t have to perform as many sub-tasks to complete a task.

Task analysis should be one of the first things you do before designing, but as designing is a continual process, remember to come back and perform task analyses throughout your design journey!

Automation: For the People

Due to the exponential growth in technological advances over time, technology has become a constant in everyone's lives. As these advancements are constantly being made, it is important to keep track of their impact on society.

Today, automation in technology is the area that I will be focusing on.

When I explain automation to my mom, she contorts her face into a raisin and questions, "But Riva, what about the people?" She doesn't have to continue for me to understand what she means. Her concern is one that aligns with most people. There is a public fear around the idea of automation - people are worried about automation leading to significant job losses and a rise in inequality. It is a valid concern as automation will definitely revolutionize the workplace; however, it is important to remember that technology has a long history of erasing jobs only to create more job opportunities.

Moreover, when speaking about automation, there is a tendency to overlook the profoundly meaningful impact that automation has in helping our society become more inclusive of people with disabilities. Advances in automation such as the integration of voice control into products, creation of home automation technology and domestic robots empowers people with disabilities and supports them in their pursuits to live more independently. 

Automation is designed to make lives easier and better. When my mom questions me, "But Riva, what about the people?" I respond back, "Mommy, it is for the people - all people."