Uploaded byLee Schlenker
Newcastle Quantified Self 2015
The document discusses productivity in the context of the Quantified Self movement. It presents an agenda with 9 sections covering topics like digital workspaces, building blocks, value propositions, and conclusions. The goal of the Quantified Self is to use data to gain self-knowledge and act on real-time information to improve productivity. The document examines how technology can help quantify individual performance and optimize work.
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Newcastle Quantified Self 2015
- 1. The Quantified Self Productivity isa measure of your ability to act on real-time information
- 2. Agenda I. CHAT II. TheNew World of Work III. Productivity today IV. Digital Workspaces V. The Building Blocks VI. The Value Proposition VII. Market metaphores VIII. The value architect™ IX. Conclusion and Perspectives Intro Technology CasesDomains ©2014 L. SCHLENKER
- 3. Intro Technology CasesDomains ©2014L. SCHLENKER
- 4. ©2013 L. SCHLENKER FocusImprove Knowledge Leverage Mesure Organization Processes Explicit Transactions Efficiency Networks Relationships Emerging Interactions Innovation Mobility Context Embedded Proximity Relevancy People Presence “Dasein” Little Data Pertinence Intro Value CasesImpact
- 5. The author suggeststhat the "Quantified Self" movement is about self knowledge. What does he want to know about himself? Describe one of the applications described in the article (audience, data sources, interface, use scenarios, observations). The article points out the fallacy of "magical thinking". What does this mean and how does this this apply to the Quantified Self? The article concludes that the goal isn't to do more work, but to do better work. What does this mean to you? Richard J. Anderson Intro Technology CasesDomains ©2014 L. SCHLENKER
- 6. ©2013 L. SCHLENKER http://intranet- matters.de/resources/intranet-maturity- models/ I.Inputs II. States III. Performance IV. Self-knowledge Intro Technology CasesDomains ©2014 L. SCHLENKER
- 7. ©2013 L. SCHLENKER •Yoursearch history •Your search trends •Your contacts •Your location •Your interests and demographics www.google.com/dashboard ©2014 L. SCHLENKER Intro Technology CasesDomains
- 8. ©2013 L. SCHLENKER •Self –knowledge – what am I like? • Cognitive, affective, executive self • Physical, social and physiological worlds • Impact on how experience is encoded Intro Technology CasesDomains ©2014 L. SCHLENKER
- 9. • The mechanicalclock • Harder, better, faster… • Mechanized productivity • Knowledge productivity • Continuous Productivity Intro Technology CasesDomains ©2014 L. SCHLENKER
- 10. ©2013 L. SCHLENKER •Mental, physical, emotional states • Psychiatry – an indication of one health • Cognitive psychology – thinking and feeling • Buddhism – our ability to “color” the mind Intro Technology CasesDomains ©2014 L. SCHLENKER
- 11. ©2013 L. SCHLENKER I.The Quantified Self II. The Qualified Self III. Managerial Perspective Intro Technology CasesDomains ©2014 L. SCHLENKER
- 12. •Data mediates theexperience of reality. •Quantimetric self-tracking and wearable computers •Quantimetric self-sensing •Gary Wolf - the Quantified Self Early prototype of "Quantimetric Self-Sensing" apparatus, 1996 Intro Technology CasesDomains ©2014 L. SCHLENKER
- 13. • Pythagoras ofSamos - number is the key to reality • Immanuel Kant - reality is comprehensible through categories of significance (schemata) • Michel Foucault: - technologies of the Self • Martin Heidegger - care of the self before care of others • Timothy Leary – «turn on, tune in, drop out” • Steve Mann: - Souveillance vs. Surveillanc QS as way of sharing meaning rather than quantifying the individual? Intro Technology CasesDomains ©2014 L. SCHLENKER
- 14. ©2013 L. SCHLENKER I.Health and Well-being II. Personal and Group Productivity III. Education IV. Social Interaction Intro Technology CasesDomains ©2014 L. SCHLENKER
- 15. • Mobile devicesand embedded sensors can track heart rate, blood sugar, caloric intake, sleep quality…. . • Capters can beam data to cloud databases, which send advice to consumers • There is a real need in health- care to cut down the number of unnecessary medical visits • Google has funded 23andMe Inc., Fitbit has drawn $43 million from investment firms Intro Technology CasesDomains ©2014 L. SCHLENKER
- 16. ©2013 L. SCHLENKER •What if the devices “completely go away to be absorbed into the fabric of our lives?” • Smart pills are an example of ambient technologies that integrate into our environments • These Invisibles will create a world in which we don’t see technology or sensors • Can technology become human – reacting to what makes each one of us unique? http://youtu.be/-hhOtjdkU34 Intro Technology CasesDomains ©2014 L. SCHLENKER
- 17. • People havebeen keeping checklists and to do’s for decades • Ask the right question and then find the right mix between curiosity and measurable data • RescueTime led writer Gina Trapani to switch to a standing desk and WordPress creator Matt Mullenweg do impose new email rules. •Mint for tracking where every Euro and cent goes. •MoodPanda for noting on a simple 1- 10 scale how you’re feeling •PlaceMe, for automated location tracking system Intro Technology CasesDomains ©2014 L. SCHLENKER
- 18. ©2013 L. SCHLENKER •Acts as a health and fitness tracker, runs apps and communicates with the iPhone. • Logs biometric information using 10 different sensors • Supports Siri - Apple's voice-controlled "personal assistant“ • «Our most personal product » - yet only one technology • Apple Pay – a portable cash registerhttp://youtu.be/rwv-3wZocK4 Intro Technology CasesDomains ©2014 L. SCHLENKER
- 19. • Track whatyou read – when, what, where you stop, what you highlight, what you annotate • Track what you write - how many words, how many pages, what and when you write…. • Track how you learn - who you listen to , what you say, how you search…. • Technology can enable real-time feedback •Santa Monica College’s Glass Classroom, Stanford’s Multimodal Learning Analytics • Do something with what you discover http://www.edudemic.com/learning-analytics-in- education/ http://glassclassroom.blogspot.fr/2012/12/the-glass- classroom-big-data.html Intro Technology CasesDomains ©2014 L. SCHLENKER
- 20. ©2013 L. SCHLENKER •A language-learning and crowdsourced text translation platform • Tracks progress on a dozen languages • Proposes a gamified skill tree that displays each user’s progress • Users gain "experience points" that they can compare •The system measures which questions the users struggle with and what sorts of mistakes they make • Data-driven approach - 34 hours equals a college semester of study •Crowd sourced business model (CNN, Buzz Feed) http://youtu.be/WyzJ2Qq9Abs Intro Technology CasesDomains ©2014 L. SCHLENKER
- 21. ©2013 L. SCHLENKER •Using data for personal meaning challenge our ideas about human connection • Social networks like Facebook and Twitter transform our social interactions into quantifiable data streams • Social Graph - interactions between people in a social network • Is it possible to track emotions, passions and memories? • Could QS help us live together in a sustainable way? Will our communities be looking after us, taking care, encouraging us, as well as discipline us? Joerg Blumtritt Intro Technology CasesDomains ©2014 L. SCHLENKER
- 22. ©2013 L. SCHLENKER •A cooperative project between the Apenheul Foundation and TNO. • Create a richer “visitor” experience in this zone that hosts half of the world’s mountain gorillas • Sensors (electronic eyes, ears and nose) are used for non-intrusive research and education • Mapping of the hierarchical relationships within the community. • Capture the gorilla’s activity or sleep patterns • Why not let the Apes “twitter their status” ? http://youtu.be/Omya8i4QTtQ Intro Technology CasesDomains ©2014 L. SCHLENKER
- 23. ©2013 L. SCHLENKER I.Big Data, Little Data II. Cloud Computing III. Open Data IV. Sensors and Capters V. Visualisation Intro Technology CasesDomains ©2014 L. SCHLENKER
- 24. ©2013 L. SCHLENKER •Examples •Walmart : 1 million transactions/hr •BBC: 7 PB video served/month • Big Data definition: data sets on social interactions that are too complex for traditional DBMS (volume, velocity, variety) • Little Data : data sets on individual rather collective behavior • Structured and unstructured data Source: Mary Meeker, Internet Trends, Intro Technology CasesDomains ©2014 L. SCHLENKER
- 25. ©2013 L. SCHLENKER •Computing as service rather than a product • Focuses on maximizing shared resources • Public, private or hybrid • Infrastructure as a service (IaaS) • Platform as a service (PaaS) • Software as a service (SaaS) Intro Technology CasesDomains ©2014 L. SCHLENKER
- 26. • The ideathat certain data should be freely available to everyone to use • Facts cannot legally be copyrighted, but aggregated data can be privately owned. • Journal publication is an implicit release of the data to the Commons • Midata, the UK government’s initiative to give consumers access to data about them that is held by brands Anja Jentzsch ©2014 L. SCHLENKER Intro Technology CasesDomains
- 27. ©2013 L. SCHLENKER •TheInternet of things: Physical objects linked by the Internet that interact through web services •Usual gadgetry (e.g.; smartphones, tablets) and now everyday objects: cars, food, clothing, appliances, materials, parts, buildings, roads •Embedded microprocessors in 5% human-constructed objects (2012)1 1Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012. http://singularitysummit.com/schedule Melanie Swan Intro Technology CasesDomains
- 28. ©2013 L. SCHLENKER •Study of abstract data to improve human cognition • Lévi-Strauss – the world has become so complex that we must “simplify it” to understand it •Goal of data visualization is to communicate information clearly and efficiently • Visualization is today a critical component in scientific research, data mining, finance, and market studies Intro Technology CasesDomains ©2014 L. SCHLENKER
- 29. ©2013 L. SCHLENKER •Tennis is stats heavy : serve percentages, forehand winners, aces, unforced errors • Give the amateur some way of assessing his or her game • Hitting the sweet spot 100 % of the time doesn’t mean you’ll win • Data is often misleading, but winning is often about fractions. • It does have the potential to change the way we think about coaching Intro Technology CasesDomains ©2014 L. SCHLENKER
- 30. ©2013 L. SCHLENKER FocusImprove Knowledge Leverage Mesure Organization Processes Explicit Transactions Efficiency Networks Relationships Emerging Interactions Innovation Mobility Context Embedded Proximity Relevancy People Presence “Dasein” Little Data Pertinence Intro Value CasesImpact
- 31. ©2013 L. SCHLENKER OtherCases I. Babolat II. Sensory Fashion III. Asthmapolis IV. Sleep on It Intro Technology CasesDomains ©2014 L. SCHLENKER
- 32. ©2013 L. SCHLENKER •Sleep is the third pillar of health (with diet and exercise) • Works on the basis of actigraphy, which is the detection and analysis of muscular movement •Sleep data reveals how your sleep is related to factors such as diet, stress, and physical activity • The physical and psychological benefits have yet to be proven ©2014 L. SCHLENKER Intro Technology CasesDomains
- 33. ©2013 L. SCHLENKER •Uses fashion and IT to create responsive clothes offering therapeutic value •Scents as tools to improve mental and physical wellbeing • A localized ‘scent cloud’ is released to fit specific moods • Goal is unlock emotional memories and to complement mood monitoring tools for the ‘Quantified Self’ Dr Jenny Tillotson Intro Technology CasesDomains ©2014 L. SCHLENKER
- 34. ©2013 L. SCHLENKER •8.4% of Americans, or over 25 million people, suffer from asthma. • Third-leading cause of death in the US with $50 billion associated annual healthcare costs • Helps researchers pinpoint environmental triggers and monitor the population of asthma suffers • Attaches to an asthma inhaler and logs the time and geographic location each time its used • Uncontrolled asthma declines by 50 percent http://youtu.be/6CH1IxzmwUs Intro Technology CasesDomains ©2014 L. SCHLENKER
Editor's Notes
- #10 Technics and Civilization, the historian Lewis Mumford