summary of digital information from understanding context
TRANSCRIPT
UNDERSTANDING CONTEXTSummary of Chapter IV, Digital Information
“This is an abbreviation of chapter IV, “Digital Information” from a book “Understanding Context” by Andrew Hinton.
Without verbal explanation, some contents of this document cannot be understood properly.
For more details, please refer the book. http://www.amazon.co.jp/dp/1449323170/ref=cm_sw_r_tw_dp_7onhxb02YHYZZ
PART IV. DIGITAL INFORMATION
Roy S. Kim @royskimJP
SUMMARY OF THIS CHAPTER
➤ Origins of digital technology, and why it is different from the other modes.
➤ How digital information influences the way we understand the world, the way we make software, and the properties of digital agents and simulated affordances.
CHAPTER 12.
DIGITAL COGNITION AND
AGENCY
➤ Digital devices use ‘encoded information’ for improving accuracy and efficiency.
➤ The ‘meaning’ of a message is irrelevant to the engineering problem.
➤ Encoded information for digital devices cannot be understood by a human without decoding and showing it in proper formats.
Example of Encoded information: TCP/IPのデータ構造
SHANNON’S LOGIC
➤ Decoupling information from context
➤ We understand our experience by coupling with the information the environment provides us; but digital technology, by its nature, decouples its information from our context.
➤ We need to think about design’s communication and craft in more complicated and changed way for digital information.
DIGITAL LEARNING AND AGENCY
➤ We need to create the representation intentionally or it can be done by some algorithmic process.
➤ Without it, computers can barely do any valuable thing.
➤ Now we have technology that has agency; the ability to make decisions and take actions on its own.
➤ Digital system only knows about the world by abstracted representations of the real world.
Humans and digital agents learn in different directions.
➤ What happens when you ask about the location of the closest gas station to Siri?
➤ Siri does not know the meaning of the question.
➤ It just calculates and return result by the predefined algorithm using structured data.
➤ What happens when you use Shazam to recognise a song?
➤ Just a “pattern matching” using its database.
➤ Emotional or cultural context of the song is ignored.
➤ In digital, context has to be artificially generated, from already-abstracted inputs.
EVERYDAY DIGITAL AGENTS
➤ The number of rules to control physical objects is restricted by its shape, size or properties of the material .
➤ Digital agents can enact as many thousands of couples rules as will fit on device’s microchips.
➤ Digital agents are getting more complex and can go terribly wrong!
EVERYDAY DIGITAL AGENTS
➤ It’s hard to figure out what is happening in the world with the huge number of complex digital agents.
➤ More concerns on context are required when ruling digital agents.
➤ The assumption on context can go wrong.
➤ How to prevent it?
➤ It is the age of automation now.
➤ Many digital agents are type of ‘Set it and forget it.’
➤ online subscriptions
➤ bill-pay withdrawals
➤ How to manage those digital agents?
➤ Can we really forget it?
➤ Will we need agents for keeping track of our agents?
ONTOLOGIES
➤ Computers do not need human-understandable context to function within and among themselves.
➤ Computers need ontology to understand the human context in order to execute complex processes.
➤ Word is not good enough to describe the human context.
➤ The Ontology describes concepts and relations among them in a specific domain.
Which Java?
EXAMPLE OF ONTOLOGY: THE “PROGRAMME” ONTOLOGY OF BBC
EXAMPLE OF ONTOLOGY: RDF IN REAL
<rdf:RDFxmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Descriptionrdf:about="http://en.wikipedia.org/wiki/Tony_Benn"><dc:title>TonyBenn</dc:title><dc:publisher>Wikipedia</dc:publisher></rdf:Description></rdf:RDF>
EXAMPLE OF ONTOLOGY: CONCEPT MAP
➤ Ontology is still under developing.
➤ As designers, who are making user-facing environments, ontology is about establishing the understandable semantic functions that solve the contextual gap between person and machine.
➤ Google’s service ‘Buzz’ failed because it adopted the concept of friends improperly in their service.
➤ Boss, lover, and family are not the “friends.”
CHAPTER 13.
DIGITAL INTERACTION
INTERFACE AND HUMANS
➤ Digital interface has many layers of abstraction, so it is necessary to provide environmental elements that human can recognise and understand.
➤ Any digital systems require learning(or relying on learned convention) for an artificial user interface of some kind, because they will always be the need to translate the abstraction of digital information into invariants that users can comprehend, whether simple buttons, voice commands, mere labels, or sensor-triggered gestures.
➤ We need to establish interfaces that are part of the environment we inhabit and are themselves smaller environments nested within the larger ecological context.
➤ Digital interfaces have gone through a rapid evolution but always require translation.
SEMANTIC FUNCTION OF SIMULATED OBJECTS
➤ This is not a pipe.
SEMANTIC FUNCTION OF SIMULATED OBJECTS
➤ This is not a pipe.
We don’t go around separating semantic and physical information.
We often use them as if they were interchangeable.
➤ This is not a button.
➤ This is not a button.➤ You cannot smoke the pipe, but you
can “press” these buttons on the screen.
➤ This is a semantic function, simulating physical affordance.
➤ Skeuomorphic vs flat design
➤ These are not a binary choice; the design approach is on more of a spectrum.
➤ Consider user’s learned environmental convention, just like the meaning of words and sentences to do right design.
➤ Unlike our interactions with physical objects, we never directly control what software does.
➤ Email software can have an “inbox” and “outbox”, but there are no physical boxes.
➤ Digital technology takes the physical affordance for better understanding: Extended cognition
➤ But there is a limitation too.(Simulation is simulation.)
MODE AND MEANING
➤ “Mode” is a condition that changes the result that the same action would have under a different condition.
➤ Required to accomplish more things with the same number of controls. So, the mode is unavoidable even though with its risk.
➤ Useful, but challenging because mode can cause serious troubles.
➤ On PC, unexpected “caps lock” mode makes you type again.
➤ On Airplane, unexpected mode can take all passengers’s lives.
➤ “Mode” should be expressed very clearly.
➤ When users are aware of modes and motivated to learn about them, the negative effect can be minimal.
➤ The iPhone turned the phone into an almost entirely modal device: a slab of glass that can be just about anything, depending on what application it is running.
➤ AppleWatch is using its “crown” for different functions depending on the currently active application.
➤ We need digital agents to control “mode” of other digital agents.
➤ We simply cannot manage all the complexity.
➤ We need digital agents to help manage our digital agents.(WTF?)
CHAPTER 14.
DIGITAL ENVIRONMENT
VARIANT MODES AND DIGITAL PLACES
➤ The Rule-Driven Modes and simulated affordances of interfaces are also the objects, events, and layouts that function as places, whether on a screen alone or in the ambient digital activity in our surroundings.
➤ Changing the mode of object can affects the mode of places as well, especially when objects work as parts of interdependent systems.
➤ Software-based places
When in the shopping tab, search results are driven by different rules, which you can see by clicking the “Why these products?” link
The invariant features of the environment need to make the difference more clear by using semantic functions to establish context better.
FORAGING FOR INFORMATION
➤ When we use information environments, we are not paying explicit attention to a lot of factors.
➤ Humans look for information by using behavior patterns similar to those used by other terrestrial animals when foraging for food.
➤ We take action in digital-semantic environments using the same bodies that we evolved to use in physical environments.
➤ Environments made mostly of semantic information lack most of the physical cues our perceptual systems evolved within
INHABITING TWO WORLDS AT ONCE
➤ There are on-screen capabilities that change the meaning of physical places, without doing anything physical to those places.
➤ Adds a layer of digital behaviour that changes, if only slightly, the functional meaning of one physical context versus another.
➤ Our smartphones make everyplace we inhabit potentially “smart.”
Refresh podcasts by geolocation in the downcast app
➤ What is the My Store?
➤ The user didn’t select any store by herself. System assumed it using a clue of information. But is it right? What if the user is in her office or a cafe?
➤ Do not fake simplicity but to embrace the complexity and clarify it by making it more understandable.
AMBIENT AGENTS
➤ The networked objects of the urban landscape transform what cities are to us.
➤ Rooms and buildings will henceforth be seen as sites where bits meet the body — where digital information is translated into visual, auditory, tactile, or otherwise perceptible form, and, conversely, where bodily actions are sensed and converted into digital information.
➤ Our inhabited places are fundamentally different, whether online or offline, through the emergence of networked consumer technology and government infrastructures.
➤ Not only do hardware and software objects have agency, but the “space(=places)” we inhabit can have agency, as well.
➤ Robots aren’t only in the form of objects that behave like people or animals; entire building and cities can be “robots” of a sort.
➤ Everyware
➤ In the near future, everything around us will be based on the planned choices of the human being.
➤ Systems use light-speed decision logic based on rules that are increasingly written by the systems themselves.
➤ It’s important that bridging the “black box” dimension of the digital agency with the human-scale world that people can actually perceive and understand.
➤ Digital agents need to be transparent about their limitations rather than present a simplified front that inaccurately promises human-like coherence.