My sociotechnical plan involves using augmented reality as a learning tool. The concept involves developing a device that can provide instructions for a variaty of physical tasks. The device would be a headset like Microsoft Hololens that provides an augmented reality overlay that the user will see over their natural surrounding. The headset can track the users hands and movement and also see the users environment. Using this data, the system provides realtime instructions on how to perform various tasks like performing electrical or plumming work in the users house, or performing auto repair. The system would have tremendous commercial and personal uses from teaching homeowners how to fix a broken dishwasher, to guiding surgons during operations. A major impact that could result from the implementation of this type of device would be pulic concern over privacy. An example of how this may develop can be observed witht the history of Google Glass.

Google announced Project Glass in April of 2012 (McGee, 2015). Project Glass was name for the division at Google responsible for developing Google Glass, a wearable augmented reality device. Google Glass is a wearable computer that powers a small display window in the upper right or left corner of the users vision. The display can provide real time information based on several different factors like the users position, input from their phone, or even what they are looking at. Google Glass is equipped with a camera that records video and images that can be passed to Google search engines, captured on the users phone or cloud service, or streamed live over several social media applications. This is one of the aspects of Google Glass that lead to it being essentially abandoned as a product by Google.

When it was announced, Google demonstrated how Google Glass could instantly capture what the user was seeing by giving demos of skydivers, athletes, and regular users capturing their activites and live streaming them to You Tube, Twitter, and other social media sites. As Google Glass prototypes started being issued to Google employees for testing, growing public concern about privacy grew around the use of the device ("Global data protection authorities tackle Google on Glass privacy," 2013). People wanted to know if the system was always recording, or how they would know if a Glass users was recording them or sharing photos of them. People didn’t like the idea of possibly always being recorded.

One of the privacy issues with Google Glass was how it is engineered. The computer on the device needs to be small so it can be comfortablly worn, and it must offload processing to accomidate for being so small, and to reduce battery drain (Claburn, 2012). This means that the device must offload any recorded image or video to provide most of its augmentation features. Google Glass was being developed in a market where cell phone cameras were becoming the main way people recorded video and took pictures, and where debates about the ethicacy of recording strangers was a concern for many people. Google wasn’t very clear on when Google Glass would be recording or listening, and if users would even know if it was. Google wasn’t helped by reviewers wearing Google Glass in the shower, forgetting to take the devices off when entering restrooms and other private areas, and not being clear on when the device would send data back to Google servers. In the end, Google abandoned the project in January of 2015. They have since updated the software twice in 2017, but there is currently no commercial way to purchase Google Glass.

Using the history of Google Glass as an example of public reaction to privacy concerns dealing with augmented reality, I believe that concerns about privacy when using an augmented reality device can be broken down into two categories. The first is how the system processes and manages images, video, and sound. The second is the public understanding and perception of the use of that data. For the first category, I believe that we are seeing more devices being cloud enabled, and that this is a trend that is going to continue in the future.  While it could be argued that having a system that performs processing of recorded data locally would be more secure, I do not believe that this is a viable technical solution, and it is not the way that technology is trending toward. For the second category, public opinion about privacy may be changing. While Google Glass was generally rejected by the public over privacy concerns, devices like the Amazon Echo have become very popular. I believe that this is due to the Echo being in a persons home, and not a public space. Since my device would be used in a home or place of business, I think that this may change the way it is perceived as far as privacy is concerned. Each of these categories would require a large amount of research to be properly explored and could possibly be great dissertation topics on their own!

~ Ben

References:

Claburn, T. (2012). 7 potential problems with google's glasses. Informationweek - Online, Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com /docview/922740501?accountid=144789

Global data protection authorities tackle Google on Glass privacy. (2013). Biometric Technology Today, 2013(7), 1-3. doi:https://doi.org/10.1016/S0969-4765(13)70116-4

 McGee, Matt, (2015) The History of Google Glass. Retrieved from http://glassalmanac.com/history-

google-glass/

Serendipity and Smart Dust

Exaptation is the when an innovation that was originally designed for one purpose, is redesigned for another. A good example of this is the invention of air conditioning. Willis Carrier was trying to remove humidity from a lithographing office in 1902 (Lester, 2015). His invention was able to increase or decrease the humidity in a room and it had the additional benefit of cooling the air in room as well.

Discovery by error is likely the most common form of accidental invention. This is when a mistake during research or development returns a positive result. During software design, this is usually called turning a bug into a feature.  For example, Gmail originally had a 5 second delay when processing email. Instead of fixing the delay, developers added an ‘undo’ button that would stop the email from being sent (Leggett, 2009). This way, the error in processing the email message turned into a feature that allowed users to quickly call back an email after hitting send.

Serendipity occurs when a positive outcome is the result of a chance event. A good example of this could be getting lost but finding a great new restaurant or book store where you end up. You weren’t intending to look for a restaurant, but because of going the wrong way you found it.

In October of 2003, a graduate student at the University of California in San Diego won $50,000 as the grand prize in the Collegiate Inventors Competition for her invention of ‘smart dust’. Smart dust consists of silicon partials that can be used to detect a variety of biological and chemical agents in different media (Link, 2005). Since its invention in 2003, there have been many more proposed and applied applications for smart dust. Along with sensing the molecular structure of different objects, smart dust can be used to sense minute levels of light as well. Smart dust is being adapted to carry signals, which could result in things like wireless sensor nodes that are a cubic millimeter in size. Jamie Link was in the process of making thin multi-layer porous silicon when the silicon chip snapped. The accident released small amounts of silicon dust that held the same properties as the chip. This serendipitous event brought about this invention that has a wide range of uses in medical and environmental diagnostics and research.

References:

Leggett, M. (2009, March 19). New in Labs: Undo Send. Retrieved August 22, 2018, from

https://gmail.googleblog.com/2009/03/new-in-labs-undo-send.html

Lester, P. (2015, July 20). History of Air Conditioning. Retrieved August 22, 2018, from

https://www.energy.gov/articles/history-air-conditioning

Link, J. R. (2005). Spectrally encoded porous silicon “smart dust” for chemical and biological sensing applications. (3171107 Ph.D.), University of California, San Diego, Ann Arbor. ProQuest Dissertations & Theses Global database.

Forecasting Piracy

Forecasting is the act of predicting future trends based on past events. Typically, forecasting is used when predicting the weather. Meteorologist use past weather phenomenon as indicators on what future weather may be like. People use forecasting in almost every aspect of our lives. If you drank too much at last years Christmas party and embarrassed yourself, you may drink less at this year’s party, so you don’t suffer the same fate. What is essentially happening is that you can forecast the results of drinking too much at the party, and you adjust your actions to avoid that prediction. The concept behind this is that the future is relatively predictable, and events will tend to repeat themselves. Unfortunately, this isn’t always the case, and traditional forecasting could lead us to make the wrong decisions. Therefore, planning outside of forecasting should be implemented. Scenario planning is a different method for planning for future events. Scenario planning builds sets of likely events and then builds plans to respond to those likely events. The core concept behind scenario planning is answering the question ‘what if’ (Chermack, 2004). Scenario planning provides a benefit of allowing for the inclusion of possible events and agents of change that may be new or previously unrelated to our forecasting efforts. A good example on the differences between forecasting and scenario planning, and how this may affect business can be found by researching the music industries response to digital music and piracy in the late 1990’s.

For many years, the music industry sold music in albums. If someone wanted to listen to their favorite song, they had to buy the whole album. Sometimes songs were so popular that they’d be released as a single for less money, but it was often the case that the album had to be purchased. Piracy of music existed by creating illegal copies of these albums, first with devices that could press copies of records, to cassette tape recorders, and then digital compact disk (CD) writers. The music industry would combat these forms of piracy, as they saw each copy as a lost sale (Marshall, 2004). The years of music sales and distribution locked the music industry into forecasting the same actions to yield the same profits and success that they were used to. This all changed with the rise of digital piracy. The music industry knew that most people would prefer to purchase individual songs, but the music industry made more money when they purchased albums. This is the reason why singles weren’t as popular, not because they wouldn’t sell but because they weren’t as profitable. When music changed to a digital format, it was much easier for people to pull the individual songs they wanted and to share them between computers over the internet. Figure 1 shows how digital single downloads dramatically increased over physical CD sales and even full album downloads as digital music became more accessible over time. The first mainstream music piracy application called Napster, made digital music piracy easily accessible for people with only a moderate amount of technical knowledge. The music industry reacted to digital piracy the same way they reacted to record pressing machines in the 1950’s. They condemned the practice and raised prices in part citing lost sales due to piracy. According to their forecasting models, this was the tried and true response. The music industry had more than enough information and time to capitalize on this new distribution method but failed to do it due to poor forecasting. Instead of adopting and commercializing sharing apps like Napster, they fought them only to have hundreds of copycat programs replace the few that started. By the time the music industry decided that digital downloads were a permanent change in music distribution, the illegal methods of doing so were so refined and easy to use that they couldn’t create a system that was preferable to piracy.

Figure 1: Music Sale Trends (Rocket, 2018)

The music industry failed to capitalize on one of the biggest softball opportunities presented in any industry. They had hundreds of thousands of digital products that were in high demand. They had a model system in Napster in how to distribute their products, and they had a trend in technology that supported this new business model. The deck couldn’t be stacked more favorably in their favor, and they blew it. Instead of realizing that the market was changing and conducting any predictive modeling or scenario planning, the music industry stuck to their guns. They pushed legal action against pirates, they increased costs of physical media, and reduced access to single song digital download. In 2002, the music industry was on the verge of collapse. Consumers weren’t interested in buying the physical CD’s they were selling, and they had failed to adopt a distribution system that provided digital single song access like piracy applications had been doing for the previous few years. It wasn’t until Apple Itunes, Google Music, and other digital purchase and streaming sites became available that this trend started to reverse. Figure 2 shows that digital single song sales far surpass all physical sales previously recorded.

Figure 2: Digital Downloads (Cumberland, 2013)

Its very possible that the music industry could have capitalized on the changes in music distribution and customer demands if they would have conducted scenario planning to complement their forecasting. Scenario planning could have answered the ‘what if’ questions that could have allowed the music industry to have an adaptive strategy to embrace digital music access like iTunes and Google music did earlier (Marshall, 2004). The shift to digital streaming has reduced major music labels influence on the industry away from them and towards providers like Apple and Google. Since the need to produce physical copies of digital media has almost been eliminated, small producers can go direct to distributers like Google and get their music direct to the customers. The failure of the music industry to plan for this scenario resulted in them losing their hold on the music industry. Scenario planning can account for the social impact of change and build possible responses that can account for those changes. In the case of the music industry, scenario planning could have been the answer to keep the music industry in the same dominant position while adapting the social changes in digital music consumption.

References:

Chermack, T. J. (2004). A Theoretical Model of Scenario Planning. Human Resource Development Review, 3(4), 301-325. doi:10.1177/1534484304270637

Cumberland, R. (2013, June 13). The new music business model how did the industry get here and what's next? Retrieved August 19, 2018, from https://www.bemuso.com/articles/thenewmusicbizmodel.html

Marshall, L. (2004). The Effects of Piracy Upon the Music Industry: a Case Study of Bootlegging. Media, Culture & Society, 26(2), 163-181. doi:10.1177/0163443704039497

Music Industry Sales, Piracy and Illegal Downloads – Better or Same? (2013, July 03). Retrieved August 19, 2018, from http://www.rockitboy.com/blogs/music-industry-sales-piracy-and-illegal-downloads-better-or-same/

Traditional Forecasting vs Scenario Planning

Traditional forecasting:

Forecasting is one of the steps that is taken when planning for the future. It is a process of using current and past information to attempt to predict what may happen in the future. A family planning for a trip may use the amount of money spent of food for previous trips to forecast how much they may spend during the future trips. Meteorologists forecast future weather events in part by using data of past events. Traditional forecasting encompasses commonly used forecasting methods like the naïve forecasting method, casual forecasting, and the Delphi method of forecasting. While these approaches vary, each one uses past data in some way to attempt to predict future events (Porter, 2011). For example, casual forecasting attempts to use related data to predict a future event. If a movie has high ticket sales, we can assume that the action figure toys for that movie will sell well. The Delphi method uses the opinions of experts of past events to predict future events. A drawback to this type of forecasting is that it only prepares the participants for events that have already been observed, since the data is based primarily off past data. Using the meteorologist example, if data is coming in that the meteorologist doesn’t have a model to base their forecasting from, they can’t predict the weather. This is usually parodied in movies during a cataclysmic event when the resident expert is asked what is going to happen and turns to the camera and says “I don’t know!” The next time that happens, we now know to scream out in the theater “Traditional forecasting does not take previously unobserved phenomena into account during analysis!!” I strongly recommend not doing this.

Scenario Planning:

Scenario planning is a unique approach to predicting future events and trends. Instead of using past data to attempt to predict future events, scenarios are developed that represent what may happen in the future. Those scenarios are played out to their logical conclusions, and decisions are made based on the results. A good example would be when planning a fire escape plan for a building. The planners can develop scenarios based on where the fire may be. For example, if the fire is near the main escape path from the building, the planners can work through that scenario and determine an alternate path out (Wade, 2012). The strength of this approach is that a scenario can be determined from what could possibly happen, instead of what has happened. Using another Hollywood example, in the movie World War Z a plague sweeps across the world and only Israel is prepared. In the movie, the Israeli government identified this possible scenario early and planned through the logical conclusion. Using traditional forecasting, this would probably not be the results since this outbreak was the first of its kind. A weakness for scenario planning is the opposite of traditional forecasting to where it is not based on past data and can be wildly subjective and miss the mark of what really happens when the scenario plays out. Scenario planning also breaks down in planning for short term or specific planning (Coates, 2016). In the book Foundation by Issac Asimov (1951), humanity builds a massive super computer that can use traditional forecasting to predict the future for several thousand years. Throughout the book, we find that the scenarios built by the supercomputer during a massive war were way off when planning short term actions, and the techs monitoring the system added changes whenever they thought the system was wrong. Although fictional, this is a good example of this flaw with scenario planning.

References

Wade, W. (2012). Scenario Planning: A Field Guide to the Future. John Wiley & Sons. 

Porter, A. (2011). Forecasting and Management of Technology, Second Edition. John Wiley & Sons.

Asimov, I. (1951). Foundation. Gnome Press.

Coates, J. F. (2016). Scenario planning. Technological Forecasting and Social Change, 113, 99-102.