Background Knowledge Probe
- Create a spreadsheet with two columns and at least 20 rows.
- Take a few minutes to walk around your home, listing in the right column all the electronic devices you use on a regular basis.
- Review your list of devices and note in the left column which category you think each device belongs:
- Non-programmed circuit
- Example from Orange Unit: 5 Volt resistor -> momentary switch -> light -> ground
- Programmable circuit(s), no connectivity to other devices
- Example from Orange Unit: Using a momentary switch and Python on Raspberry Pi to turn LEDs on or off
- Example from Blue Unit: Using a momentary switch and MakeCode on Circuit Playground Express to turn LEDs on or off
- Programmable circuit(s), data connectivity with one or more local devices only
- Example from Blue Unit: Circuit Playground Express capacitive touch sensors informing which counterstory to play on the Raspberry Pi
- Programmable circuit(s), Internet connectivity providing remote information and/or control
- Examples from Rainbow Unit to follow
- Non-programmed circuit
- Which of the items on your list are analog electronics? Which are digital electronics?
- How many more devices would be added if the list were to include non-electronic, mechanical devices?
The Mechanical World
I wake up a little before the sun rises. I stand up and stretch, then walk out of the sleeping quarters and over to the outhouse with composting toilet. It’s light enough out now so my kerosene lantern isn’t needed, although it’s still a little dark in the outhouse. I start my campfire and 30 minutes later have some water boiling eggs and making some tea. The morning is first spent feeding and providing clean water to the chickens and washing clothes in the nearby stream before putting them out to dry. Then I take some time weeding in the garden while nibbling on the few first strawberries and snow peas of the season and picking the cold-tolerant spinach and kale greens of the spring garden. Getting out of the warming midday sun, I head into the woods to forage for mushrooms, ramps, dandelions, and nettles. Check on the clothes drying, catch a fish from the stream, make dinner over the fire or inside over the woodburning stove, turn on the lantern to read for a bit, and back to bed I go.
The romance of time in a mechanical world!
While I have and continue to live with a wide range of daily-use electronic devices for lighting, heating and air conditioning, cooking and cleaning, and the other staples of modern life, I come from a long family line who also keep a hold on the mechanical side of life in a range of ways. And I have a family who continues this tradition in our own ways. While we don’t live off-grid in a tiny house producing and preserving all our own food and resources, we continually explore and periodically test ideas from local rural and urban farmers and foragers.
At the same time, we’re a family who uses the Internet to explore resources such as Mother Earth News and Bryce Langston and Rasa Pescud’s Living Big in a Tiny House YouTube Channel to expand our understanding of the balance between mechanical and electronic, off-grid and on-grid, physical community and the larger online communities of practice. I’m someone who writes code, designs and builds electronics, has facilitated setup of a number of local networks and helped explore a couple community networks, and consulted on Internet choices, while also using a wide range of electronic and mechanical woodworking tools.
I live in a family and in communities that flex between the both/and of the mechanical and electronic, of the analog and digital, of the in-person and online.
From Mechanical to Programmable to “Smart”
For purposes of this discussion, we’ll consider mechanical devices as those involving pure physical processing. A common style of toilet within the United States flushes because we push down on a physical component that adjusts other physical components, thereby allowing water to leave a tank and flow through the toilet bowl. The water flows in an effective manner to remove liquid and solid waste through the physical porcelain S-shaped siphon, designed to remove wastes while physically protecting sewer gas from venting back into the room after the flush cycle has completed. Together, this represents a classic mechanical technology.
While we continue to make use of a range of mechanical devices as part of our daily lives, we have increasingly made use of electronic devices as the latter half of the 19th century experienced the advancement of electrical power generation and the eventual emergence of electrical engineering. We’ve moved from the building of wax candles or gas-soaked wicks in lanterns as a source of lighting to the incandescent light bulb providing an electrical device in which the flow of electricity can pass through a wire filament embedded within glass. More recently, we’ve seen the transition from the incandescent light bulb to light-emitting diode lamps, or LEDs, that use significantly less energy to achieve equal brightness and with greater longevity. Each of these represent simple non-programmed circuits.
But it many cases it is possible to expand the circuit to include additional components. The simple LED circuit can be constructed so as to include the necessary resistors, capacitors, transistors, and photocells such that it automatically dims or brightens depending on the amount of light currently within the area. This can be designed as a non-programmed circuit, but often is incorporated into a microcontroller or microprocessor to control the brightness of the LED based on certain levels of light as sensed by the photocell. It is now a .
The programmable circuits can become increasingly complex, as when an LED only comes on at the appropriate brightness based on current light levels when movement is detected via an integrated motion sensor. It may include built-in switches or touch sensors that can be used to determine whether to make use of the dimming and/or motion sensing functions of the program. Depending on the technology being used, it’s possible that as we observe our own or others’ uses of the technology, our behaviors are shaped by the choices being made in that use. And it may be that we begin shaping our choices to further shape behavior. The electronic devices continue on as sociotechnical artifacts. Over time, the programs running some of our electronic devices were further expanded to incorporate code that altered the actions of other segments of the code based on previous data collection by that software. To begin, code can be combined with a mathematical model using initial “training data” to create predictive decisions. Over time, machine learning algorithms are set up such that the software learns from new data in ways that help set up more precise predictions and corresponding actions.
The predictive analytics of machine learning has emerged from the broader exploration known as . Programming languages such as LISP started emerging in the late 1950s, helping to advance the field of AI that was also emerging at the time. Autonomous devices with software facilitating perception of its environment and execution of actions that maximize chances of achieving target goals are formally referred to as within AI, and popularly as today.
In their 1997 article, “An Overview of Smart Technology,” Goddard, Kemp, and Lane noted that the term “smart” for a diverse range of materials, structures, systems and technologies originated in the early 1980s, mainly via researchers working in the United States and funded through defense budgets. It was the reports of “smart” bombs and other “smart” munitions of the Gulf war in newspapers and popular science journals that brought about public awareness. From this emerged the fashionable use of “smart” technologies as part of industrial applications well beyond the original aerospace and defense uses that began in the early 1980s. Thus, while “intelligent agents” were already being developed at about the same time as part of various dedicated research groups, these academics “had been working on ‘smart’ technologies for several years without realising it!”
Intelligent or smart systems include: 1) sensors used to collect associated information on environment, condition, or operating history; 2) a trainable control algorithm to read sensor data, draw inferences on that data, and alter system characteristics based on these inferences; 3) control hardware interconnecting arrays of sensors, a microprocessor running the algorithmic code, and actuators; 4) the actuators, or mechanical devices that can move or control something and thereby implement the change in configuration based on the control algorithm; and 5) structural members allowing the sociotechnical artifact or system of artifacts to perform its primary function.
Take 11 minutes to watch this deeper exploration of machine learning & artificial intelligence introduction from Carrie Anne Philbin’s Crash Course Computer Science series hosted by PBS Digital Studios:
Bringing this together, each of these terms and concepts, from circuits that are mechanical or programmable, to sociotechnical artifacts and systems, to artificial intelligence and intelligent agents, and to smart devices and technologies is shaped by and shapes individual people, scientific disciplines, research funding resources and their governmental, corporate, and non-profit funders, journalists and popular media, and many other social, cultural, political, and economic influencers. As the previous sentence is complex, so too is each individual word within it. But only in acknowledging the complexity is it possible to meaningfully enter into true works of liberation of all direct and indirect stakeholders seeking advancement of their individual and communal valued beings and doings.
Bringing Together Sociotechnical Artifacts into an Internet of Things
As we’ve seen in the Blue Unit, it is possible to bring together microcontroller-based programmed sensors with microcomputer-based processing software using data communications cables such as our UART serial cable. This can be very effective to connect the microcontroller to the microcomputer as a single device. But a difficulty arises as control algorithms work to facilitate interdevice internetworking; that is, to facilitate two or more devices working with each other, whether or not interdevice internetworking code incorporates artificial intelligence functions and thus formally serves as intelligent agents. From farming to the home to the office cubicle to the library shelves, a growing number of independent small microcontroller/microcomputer sociotechnical artifacts are capturing and routing volumes of data between devices. The code on these devices, while much smaller than centralized cloud computing devices and services, can be very effective at rapid collection and analysis of data.
If we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss and cost. We would know when things needed replacing, repairing or recalling, and whether they were fresh or past their best.
It is here that the open protocols and processes of the Internet serve an essential role, leading researchers such as Neil Gershenfeld and others to formally advance an Internet of Things (IoT), allowing myriad devices to intercommunicate and interoperate. The initial IoT concept could incorporate, but did not require, use of Broadband connectivity to support much of its functionality. Key is the recognition that the circuits and software, even including a Web server, can be brought together on very low-cost microcomputers. While our educational-focused toolkit includes a $35 US Raspberry Pi and $25 US Circuit Playground Express, along with a range of other materials, it’s possible to build various everyday devices for as little as $1 US. Alarmingly, today many of our smart devices are really not intelligent agents, but rather basic data gathering devices that use the Internet to communicate with proprietary controllers located in the ‘Cloud,’ storing (and often taking ownership of) data and the information emerging from this data. Management and use of data and information of sensors that include both known and unknown input from consumers in many cases, is determined by the commercial providers, then returned to the devices to be implemented. Devices are indirectly interconnected via cloud platforms and software, divorced from the interdevice interconnectivity envisioned within an Internet of Things. Such conflicting meanings also appear in terms such as “smart grid,” “smart homes,” and “smart cities.”
For Neil Gershenfeld and the team at the Center for Bits and Atoms, this has meant bringing together the computer and physical sciences to explore how to turn data into things, and things into data by creating location fabrication facilities, Fab Labs, and a global network for training people and sharing knowledge, a Fab Academy, thereby digitizing fabrication in the same way we digitized communication over the last two decades. In so doing, it is to shift the focus from plumbing and power to safety and convenience, from pills to proper medication. To do this, it requires shifting focus to the local edges rather than a centralized cloud, exploring the boundaries between the digital and physical worlds.
Gartner is a global research and advisory firm who has developed the Gartner Hype Cycle model to inform its customers, senior leaders across the enterprise, regarding technology hype versus its commercial viability. This can be a helpful way to more broadly recognize the ongoing shaping and reshaping of sociotechnical artifacts over an extended period. Initially, innovation of emerging technologies leads to early proof-of-concept stories and increasing publicity of a product. However, as this often still requires further research and development, increasing wide use of the product can lead to a “Peak of Inflated Expectations” as success stories begin to be accompanied by increasing stories of failure. In many cases, interest continues to wane until the product reaches a “Trough of Disillusionment.” Only through ongoing development and testing do we reach a “Slope of Enlightenment” leading to a “Plateau of Productivity.” View the Gartner Top 10 Strategic Technology Trends for 2020 video to see an example of this methodology in practice, with indications that we are slowly moving from a BITNET of Things as noted by Gershenfeld to an increasing plateau of productivity that includes both the edge and the cloud, each in its own way.
The possibilities and pitfalls of the Internet of Things as seen through a critical lens is to recognize the many seen, unseen, and unforeseen dangers, as Richard Milner brings forward in “Race, Culture, and Researcher Positionality.” Furthermore, it is to question “Whose Culture Has Capital?” as asked by Tara Yosso, questions that we explored at the end of the Blue Unit. It is essential that we continually bring to the table not only our positivist and interpretive meta-theoretical assumptions, but also our critical meta-theoretical assumptions into the methodological landscape.
Ultimately, choice between cloud services, local servers and server farms, or the Internet of Things and smart devices does not require either “this or that” decisions. In many cases, strategic decision making, design, and implementation may reveal that a combination of several different networked information systems is the ideal practical path to facilitate functional diversity in achieving valued beings and doings for the broadest range of stakeholders. The first objective of this session is therefore to introduce us to the concepts and terms, the technical components, and the sociotechnical codifications underlying these networked information artifacts and systems, helping advance the existence and sense of choice, as well as our ability to more effectively execute and achieve that choice to advance our valued beings and doings.
And as we decodify these materials, a second objective of this session is to recognize that the data and information being collected is not just of things. It is often data and information about human and more-than-human persons and their activities. Individually and collectively, people have shaped the devices and systems which collect data and facilitate information processing for a range of objectives. In this process, we gather data on people’s actions and behaviors within various social and environmental contexts which we also are observing. How we reflect upon, investigate, and act upon this determines the form of power that is used: power within ourselves to advance our power over others, or power within others to advance power within all, to every extent possible. How the artifacts are shaped—and how we are shaped by them —also influences how we go on to shape things and persons alike.
- Burgess, Matt. “What Is the Internet of Things? WIRED Explains.” Wired UK, February 16, 2018. https://www.wired.co.uk/article/internet-of-things-what-is-explained-iot.
- TED Institute. “Internet of Things: Transforming the routine.” YouTube, November 15, 2016.
- Stop Autonomous Weapons. “Slaughterbots.” YouTube, November 12, 2017. Note: Watch the following dystopian science-fiction arms-control short film with caution. This film was produced by the nonprofit Future of Life Institute and Stuart Russell, who provides short remarks at the end. It first screened to the November 2017 United Nations Convention on Certain Conventional Weapons.
- Full Frontal with Samantha Bee. “Black Future Month.” Produced by Halcyon Person with Adam Howard. TBS, March 20, 2019.
- Harwood, Trevor. “Internet of Things (IoT) History.” Postscapes. Accessed July 16, 2020. https://www.postscapes.com/iot-history/.
- Feather, Katie. “Attack of the Internet of Things.” Science Friday, October 28, 2016. https://www.sciencefriday.com/segments/attack-of-the-internet-of-things/.
- Gershenfeld, Neil, and JP Vasseur. “As Objects Go Online: The Promise (and Pitfalls) of the Internet of Things.” Foreign Affairs 93, no. 2 (March 2014): 60–67. https://www.foreignaffairs.com/articles/2014-02-12/objects-go-online.
- Gershenfeld, Neil, and Jim Euchner. “Atoms and Bits: Rethinking Manufacturing.” Research-Technology Management 58, no. 5 (September 2015): 16–23. https://doi.org/10.5437/08956308X5805003.
- Huckle, Steve, Rituparna Bhattacharya, Martin White, and Natalia Beloff. “Internet of Things, Blockchain and Shared Economy Applications.” Procedia Computer Science 98 (January 1, 2016): 461–66. https://doi.org/10.1016/j.procs.2016.09.074.
Professional Journal Reflections:
- Review your original list of devices from the opening Background Knowledge Probe. How does it need to be revised, if at all? Why or why not?
- In what ways do you have greater control than you previously thought over your devices? Less control? Why?
- How do we recodify the concepts, terms, and technologies of microcontrollers and microcomputers, of smart and intelligent devices, technologies, and systems, of the Internet of Things/Bitnet of Things, so as to advance a more just society for all, and especially for those marginalized and oppressed?
- N. D. R. Goddard, R. M. J. Kemp, and R. Lane, "An Overview of Smart Technology," Packaging Technology and Science 10, no. 3 (1997): 129. https://doi.org/10.1002/(SICI)1099-1522(19970501/30)10:3%3C129::AID-PTS393%3E3.0.CO;2-C. ↵
- Kevin Ashton, "That 'Internet of Things' Thing," RFID Journal, June 22, 2009, https://www.rfidjournal.com/that-internet-of-things-thing. ↵
- Neil Gershenfeld, Raffi Krikorian, and Danny Cohen, "The Internet of Things," Scientific American, October 2004, https://www.scientificamerican.com/article/the-internet-of-things/. ↵
- Neil Gershenfeld and Jim Euchner, "Atoms and Bits: Rethinking Manufacturing," Research-Technology Management 58, no. 5 (September 2015): 16–23. https://doi.org/10.5437/08956308X5805003. ↵
An electronic component with an undefined function, allowing it to be programmed and used in reconfigurable ways.
A branch of Artificial Intelligence (AI) in which algorithms include "teaching" models that continually improve machines predictions when fed data.
The use of computers to not just collect and store data, but to run algorithms which learn from data in order to make predictions and decisions about the data.
Autonomous devices with software that facilitates the device's perception of its environment and execution of actions that maximize chances of achieving target goals.