Due: Thursday Mar 23; Documentation Friday Mar 24
Submit Documentation: Gallery Pool - Machine Assisted Beliefs Hidden
Brief: Drawing inspiration from Real Prediction Machines and Automato.farm’s Believe it Yourself (BIY) toolkit, prototype a tinyML-enabled electronic object that uses an otherworldly practice for fortune telling, soothsaying, or augury. Examine how these rituals — for example, reading the tea leaves, palm reading, horoscopes, numerology, or dowsing — might offer ways to move from prediction to open-ended interpretation of data, machine learning, and device intelligences. Reflect on the value of ambiguity and these otherworldly metaphors in unsettling expectations Reflect on the value of ambiguity and these otherworldly metaphors in unsettling expectations of or beliefs in predictive machine outcomes.
Goals Get familiar with basics of tinyML, working with advanced outputs, and experimenting with ambiguity as a resource for design. Goal is individually build deep skills with a machine learning process centered one sensing method (sound, movement, interaction) that can be used when students meet in the subsequent projects.
Another goal of this exercise is to consider how everyday objects might draw on the supernatural to render discursive designs. Remember: You should try to develop a critical stance on contemporary technology that sets up an intentional propostion for an alternative design. The possibilities are wide and varied. But you should:
Unusual approaches, left-of-center thinking, and impracticality is encouraged!
automato.farm (2018)
The fast spreading and ease of access to Machine Learning and Cloud computing has brought to a boom of experiments and excitement around our ability to build machines that make sense, learn, measure and predict the world around us.
Moreover, with enough examples, we can train a tool or a ‘machine’ to recognize or quantify pretty much anything we want. ‘Beauty’, ‘Hotdog-ness’ or the more problematic ‘Criminal-ness’ and ‘Sexual orientation’ can be now measured within a few frames, based on a model, a probability, determined by a set of arbitrarily collected data. Subjective judgments and biased datasets can easily be turned into objective measures and potential truths, which will then be embedded in devices around us.
But what if we would train machines to measure even more unmeasurable, personal and culturally driven things? If we gather enough samples could we detect signs that prove and detect our superstitions? and can we use that to build tools and devices that reflect our own beliefs?
BIY™- Believe it Yourself is a series of real-fictional belief-based computing kits to make and tinker with vernacular logics and superstitions.
James Auger and Jimmy Loizeau (2015)
Predicting the future is no longer about the mystical reading of natural and celestial phenomena. Today it is all about data.
The Real Prediction Machine (RPM) is a domestic product that uses big and small data, in combination with machine learning and predictive modelling to make predictions about specific future events.
Develop your domain understanding of ubiqutious computing by researching and making hybrid objects;
Develop an understanding of concepts like critical making, and counterfactuals, and how they relate to the design of alternative devices;
Build skills with prototyping hardware, electronics, and intelligent processes using tools like the Arduino Platform and Edge Impulse.
Imagine how new machine-learning enabled devices can be designed to present critical perspectives on technology;
Investigate and respond to concerns and considerations that currently surround everyday technology and smart home devices through hands-on making and exploration.
Develop a hands-on exploration that begins to tease-out the broader considerations and opportunities for building spooky-technologies
Work collaboratively to highlight your existing skillsets, expertise, and perspective within the context of this course and understand how they might contribute to an interdisciplinary investigation by making work.
Constraints:
Considerations:
Constraint is a good thing. Get creative and embrace it.
Use the project logs as both an opportunity to learn each element of this project one-by-one and an invitation to experiment!
This project will involve hacking, taking apart, and building onto the object you’re assigned. Do with it as you will.
Consider what are the issues, assumptions and ambiguities in Machine Learning processes. Attend to these. Identify the expectations that someone might have of it. How might you creatively engage, subvert, defy or unmake those issues and assumptions.
A physical prototype. You can take any approach to preparing this tangible manifestation that you feel is appropriate. This should be of reasonable fidelity to give form your your proposal, but will reflect your skills with prototyping interactive systems.
Other collateral to support the design (a flyer, advertisment, etc.) as needed
A demonstration and presentation of your process and outcome (5 minutes maximum)
Digital documentation of your process and outcome.
Final deliverables to be presented at the Crit/Review
Include a write up of the following:
Intent: What is the intent of this project and how does it reflect a critical perspective? Write about the big ideas behind your project? What are the goals? Why did you make it? What are your motivations?
Context : Give examples of prior work, ideas and projects that influenced your design. What work informed this idea i.e. make links to the material in class and the cases/projects you uncovered in this module. Describe theory, concepts, and research from this module that relate to your outcome.
Prototype/Outcome: Describe your experience/working prototype: What did you create, how, etc.? What tools and technologies were involved? Include appropriate content and illustration (e.g. a concept video, a video of the device in operation, diagrams, code, etc.) How does it relate or build on existing work (provide acknowledgements or cite this work). You should report this in sufficient detail that anyone knowledgable with electronics etc would be able to reconstruct your implementation. Be sure to include a system diagram, annotated images, code, and a bill of materials.
Process: Draw from your weekly project logs to tell the story of your exploration. Describe how you arrived out the outcome. What iterations, refinements, design decisions and changes were made? What challenges were encountered and how did you resolve them?
Open Questions and Next Steps: What remains unresolved (in the concept, implementation or conversation around this outcome)? What are the things we should pay attention to for future explorations? What questions about ‘spookiness’ or everyday technology did this exploration raise or generate? What questions reamin to be addressed?
Reflection: Critically reflect on the success of this project. Were the aspirations and ambitions achieved. Was it received and encountere din the ways you wanted? If not, why not? What do you need to get there, etc?
Attribution and References: Reference any sources or materials used in the documentation or composition.
Each of these sections should be no more than 200 words max. and well illustrated (images, videos, etc.)
For the Project Info’s goal description: it must be tweetable - summarise your outcome in no more than 140 characters