Raskar Emtech2010 Mar Final

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  • Ramesh Raskar Associate Professor MIT Media Lab http://raskar.info http://cameraculture.info
  • Kodak DCS400 in Nikon F3 body in early 90’s Commendable first 1.3MP digital but film cartridge still there! (First one in 1991 but even in 1995 the space for cartridge) Quote from Jack Tumblin Digital photography is like a caged lion that is uncaged in a jungle after years .. The lion stays in place rather than rushing out to explore Billion cameras but they all look like human eye KODAK Professional Digital Camera DCS-100: a camera back and camera winder fitted to an unmodified Nikon F3 camera
  • Wishlist by consumers and companies today .. i.e. what is NOT available today but they wish it was So, I am not including Wifi, GPS, face detection etc in the list here. But let us dream beyond this list.
  • 4 blocks : light, optics, sensors, processing, (display: light sensitive display) + 5 th element: Network
  • Lets dream big .. Can we look around at something beyond the line of sight?
  • Can photos become emotive abstract renderings ?
  • How to exploit Sharp’s photosensing LCD originally designed for touch sensing and convert into a large area flat camera Since we are adapting LCD technology we can fit a BiDi screen into laptops and mobile devices.
  • CPUs and computers don’t mimic the human brain. And robots don’t mimic human activities. Should the hardware for visual computing which is cameras and capture devices, mimic the human eye? Even if we decide to use a successful biological vision system as basis, we have a range of choices. For single chambered to compounds eyes, shadow-based to refractive to reflective optics. So the goal of my group at Media Lab is to explore new designs and develop software algorithms that exploit these designs.
  • currently we solve the human visual perception problem by simply reproducing what the eye would see. (even for 3D, we show stereo pair) But this makes it difficult to understand or manipulate for computers. (machine readable rep)
  • Platforms = optics, illum + Applications + Social Impact
  • So how will the next billion cameras in people pocket change us? Will optically smart sensor help disabled people, portable devices improve social stability and pixel-coordinated activities harness the power of crowdsourcing for image-based commerce?
  • (add pic here .. Camera is simplified .. Medical imaging is not)
  • Very complex or very rudimentary
  • Beautiful theory but strikingly simple implementation .. Combination of simple optics and some intelligent software Based on wavefront manipulation
  • Moving towards penny diagnostics Click Diagnostics Hardware app store not just software app store, we have seen this with Wii and Guitarhero
  • How to exploit Sharp’s photosensing LCD originally designed for touch sensing and convert into a large area flat camera Since we are adapting LCD technology we can fit a BiDi screen into laptops and mobile devices.
  • So here is a preview of our quantitative results. I’ll explain this in more detail later on, but you can see we’re able to accurately distinguish the depth of a set of resolution targets. We show above a portion of portion of views form our virtual cameras, a synthetically refocused image, and the depth map derived from it.
  • photos will be computed rather than recorded Comp photo will be there It will change the workflow, just with digital many pipeline have turned upside down and we will even more At the same time with cameras that understand our world better, there will be a lot of new opportunities http://scalarmotion.wordpress.com/2009/03/15/propeller-image-aliasing/
  • Pioneered by Nayar and Levoy Synthesis Minimal change of hardware Goals are often opposite (human perception) Use of non-visual data And Network
  • = Material index and compute bounces (real vs fake) = Automatic 3D, phototourism, and 3D awareness (look around a corner) = Find relationship (network) between all photos = Understand the world (recognize, categorize, make world smarter bokode)
  • If you can look around a corners, firefighters can use such a device for planning rescue of trapped people without actually having to go in the line of fire
  • Bokode.com
  • = Material index and compute bounces (real vs fake) = Automatic 3D, phototourism, and 3D awareness (look around a corner) = Find relationship (network) between all photos = Understand the world (recognize, categorize, make world smarter bokode)
  • Very challenging but a new trend is emerging: in using the power of the people.
  • What is the key resource we have in India ..
  • The camera phone provides an easy interface to fill in and verify government forms. The paper form is printed with 2D bar-codes which are decoded by camera phone and info is transmitted to a central location.
  • Microsoft Photosynth and U-Washington’s Phototourism software takes a large collection of photos of a place or an object, analyzes them for similarities, and then displays the photos in a reconstructed three-dimensional space , showing you how each one relates to the next. New options on Google Maps allows users to post and view populated map with geo-tagged photos provided by Panoramio .
  • Israeli Information Center for Human Rights in the Occupied Territories captures photos of events. From their website: “Goal is to Document and educate Israeli public and policymakers about human rights violations in Occupied Territories. Second goal is to combat phenomenon of denial prevalent among Israeli public. We hope to create human rights culture in Israel.”
  • So how will the next billion cameras in people pocket change us? Will optically smart sensor help disabled people, portable devices improve social stability and pixel-coordinated activities harness the power of crowdsourcing for image-based commerce? Not outsourcing but image-based crowdsourcing
  • So how will the next billion cameras in people pocket change us? Will optically smart sensor help disabled people, portable devices improve social stability and pixel-coordinated activities harness the power of crowdsourcing for image-based commerce?
  • Ramesh Raskar Associate Professor MIT Media Lab http://raskar.info http://cameraculture.info
  • Six ways of coming up with new ideas based on an idea ‘X’. Ramesh Raskar Associate Professor MIT Media Lab http://raskar.info http://cameraculture.info http://raskar.info http://cameraculture.info
  • X up: Airbags for car, for helicopter
  • Beautiful theory but strikingly simple implementation .. Combination of simple optics and some intelligent software Based on wavefront manipulation
  • Moving towards penny diagnostics Click Diagnostics Hardware app store not just software app store, we have seen this with Wii and Guitarhero
  • Shielded by screening pigment. The visual organ provides no spatial information, but by comparing the signal from 2 organs or by moving the body, the worm can navigate towards brighter or darker places. It can also keep certain body orientation. Despite lack of spatial vision, this is an evolutionary forerunner to real eyes.
  • http://raskar.info http://cameraculture.media.mit.edu
  • Raskar Emtech2010 Mar Final

    1. 1. Camera Culture Ramesh Raskar Camera Culture MIT Media Lab http://raskar.info http://cameraculture.info Ramesh Raskar Associate Professor Future of Imaging
    2. 2. Film-like Digital Photography
    3. 4. Cameras Phones are Everywhere Kush R. Varshney, “ Jagannath Temple ,” Puri, Orissa, India, Dec. 2007.
    4. 5. Wish List Today <ul><li>Consumers </li></ul><ul><ul><li>Super-human vision </li></ul></ul><ul><ul><li>Microscope like resolution </li></ul></ul><ul><ul><li>High speed (burst, video, no blur) </li></ul></ul><ul><ul><li>See inside the body (health) </li></ul></ul><ul><ul><li>Auto-trigger </li></ul></ul><ul><ul><li>Battery life </li></ul></ul><ul><ul><li>Zero start or shutter delay </li></ul></ul><ul><ul><li>Keep only ‘good’ pics </li></ul></ul><ul><ul><li>Find ‘relevant’ pics and better archiving/access </li></ul></ul><ul><ul><li>Put photographer back in photo! </li></ul></ul><ul><li>Companies </li></ul><ul><ul><li>Cost </li></ul></ul><ul><ul><li>Resolution </li></ul></ul><ul><ul><li>Low-light sensitivity, HDR </li></ul></ul><ul><ul><li>( For cameraphone ) Size and depth </li></ul></ul><ul><ul><li>Stereo and 3D </li></ul></ul><ul><ul><li>Mecha-free zoom/focus </li></ul></ul><ul><ul><li>Auto-tagging for sharing </li></ul></ul><ul><ul><li>Recognition </li></ul></ul>
    5. 6. Computational Photography Computational Illumination Computational Camera Scene : 8D Ray Modulator Display Generalized Sensor Generalized Optics Processing Ray Bender Ray Sampler Ray Reconstruction Generalized Optics Recreate 4D Lightfield Light Sources Modulators 4D Incident Lighting 4D Light Field
    6. 7. Can you look around a corner ?
    7. 9. Convert LCD into a big flat camera ?
    8. 11. Fernald, Science [Sept 2006] Shadow Refractive Reflective
    9. 12. Mimicking the Human Eye Lens Detector Pixels Image Slide by Shree Nayar Reproduce for the eye
    10. 13. Where are the ‘camera’s?
    11. 14. Where are the ‘camera’s?
    12. 15. <ul><li>We focus on creating tools to better capture and share visual information via a new class of imaging platforms </li></ul>Ramesh Raskar Ramesh Raskar http://raskar.info
    13. 16. What is the Media Lab? <ul><li>A Graduate Program in the Media Arts & Sciences Houses ~150 students and 30 PIs </li></ul><ul><li>A Research Lab that spans across disciplines and academic/ industrial lines 65 sponsor companies </li></ul><ul><li>Sponsors get free, non-exclusive licenses for ML IP </li></ul>Founded in 1985 by Nicholas Negroponte and Jerome Wiesner
    14. 17. Media Lab Vision Biomechatronics Neuroengineering Smart Fabrics Rethinking Cameras Human 2.0 Multimedia Ubiquitous Computing Social Media Computer Vision Sensor Networks Software Agents Bits Atoms People 1990s Body Brain Technology 2007+ Publishing Broadcast Computer 1980s
    15. 18. Close Ties With Industry 03/08/10 R E S E A R C H S T R A T E G Y Our 65 corporate sponsors include some of the most creative companies in the world
    16. 19. Center for Future Storytelling <ul><li>Launched with 7-year, $25M funding with Plymouth Rock Studios </li></ul><ul><ul><li>Opportunities for participation by other organizations </li></ul></ul><ul><ul><li>Satellite lab facility to open at Plymouth Rock in 2010 </li></ul></ul><ul><li>3 Co-Directors </li></ul><ul><ul><li>Michael Bove (Object Based Mediaand Displays) </li></ul></ul><ul><ul><li>Cynthia Breazeal (Sociable Robots) </li></ul></ul><ul><ul><li>Ramesh Raskar (Cameras, Performance Capture) </li></ul></ul><ul><ul><li>+ Technical/Creative Advisor (Glorianna Davenport) </li></ul></ul><ul><li>Making stories more interactive, improvisational, social </li></ul><ul><li>Managing the tension between the “master storyteller” and a dynamic, pervasive viewing/listening environment </li></ul><ul><li>Developing new production, distribution, and display technologies </li></ul>MIT Media Laboratory
    17. 20. Smart Cities What if cars could stack like shopping carts in cities? Ryan Chin and Bill Mitchell
    18. 21. ADAPTABILTIY Hyper - Adaptability Bio- Mechatronics Music/Mind/ Health People - Sense Sociable Robots Neuro- Media HUMAN ADAPTABILITY Bio- Mechatronics Neuro- Media Sociable Robots People - Sense Music/Mind/ Health
    19. 22. Camera Culture Ramesh Raskar Ramesh Raskar Associate Professor, MIT Media Lab http://raskar.info New Emerging Technologies Medical Imaging Entertainment User Interfaces Industrial Vision The impact of Next Billion Cameras Movie-making, news reporting Social stability Visual Social Computing Image-based commerce Future of Imaging
    20. 23. Medical Imaging Today .. <ul><li>http://info.med.yale.edu/intmed/cardio/imaging/techniques/ct_imaging/ </li></ul>
    21. 24. Self Evaluation of Eye
    22. 26. Self Evaluation of Eye
    23. 30. What are the problems with ‘real’ photo in conveying information ? Why do we hire artists to draw what can be photographed ?
    24. 31. Shadows Clutter Many Colors Highlight Shape Edges Mark moving parts Basic colors
    25. 32. Cartoon Camera Using Depth Edges
    26. 37. Canny Intensity Edge Detection Our Method Photo Result
    27. 39. Convert LCD into a big flat camera ? Beyond Multi-touch: 3D Gestures
    28. 40. Large Virtual Camera for 3D Interactive HCI and Video Conferencing Matthew Hirsch, Henry Holtzman Doug Lanman, Ramesh Raskar Siggraph Asia 2009 BiDi Screen
    29. 41. Touch + Hover using Thin, Depth Sensing LCD Sensor
    30. 42. Sensing Depth from Array of Virtual Eyes in LCD
    31. 43. Photos of tomorrow: computed not recorded http://scalarmotion.wordpress.com/2009/03/15/propeller-image-aliasing/
    32. 44. Synthesis Low Level Mid Level High Level Hyper realism Raw Angle, spectrum aware Non-visual Data, GPS Metadata Priors Comprehensive 8D reflectance field Computational Photography Digital Epsilon Coded Essence Computational Photography aims to make progress on both axis Camera Array HDR, FoV Focal stack Decomposition problems Depth Spectrum LightFields Human Stereo Vision Transient Imaging Virtual Object Insertion Relighting Augmented Human Experience Material editing from single photo Scene completion from photos Motion Magnification Phototourism Resolution
    33. 45. Camera Culture Ramesh Raskar <ul><li>Understand the World </li></ul><ul><li>Identify/recognize Objects </li></ul><ul><li>3D Awareness </li></ul><ul><li>Interact with embedded information </li></ul>
    34. 46. Can you ‘see’ around a corner ?
    35. 47. Femto-Photography: Higher Dimensional Capture FemtoFlash UltraFast Detector Computational Optics Serious Sync
    36. 48. Application: Rescue and Planning
    37. 50. Bokode
    38. 52. Defocus blur of Bokode
    39. 53. Coding in Angle Mohan, Woo, Smithwick, Hiura, Raskar [Siggraph 2009]
    40. 54. <ul><li>circle of confusion  circle of information </li></ul>camera Bokode (angle) Quote suggested by Kurt Akeley Encoding in Angle , not space, time or wavelength sensor
    41. 55. <ul><li>Product labels </li></ul>Street-view Tagging
    42. 56. cell-phone camera close to the Bokode (10,000+ bytes of data)
    43. 57. Camera Culture Ramesh Raskar <ul><li>Understand the World </li></ul><ul><li>Identify/recognize Objects </li></ul><ul><li>3D Awareness </li></ul><ul><li>Interact with embedded information </li></ul>
    44. 58. Visual Computing <ul><li>Forward (Synthesize) </li></ul><ul><ul><li>Special Effects, Image Processing, Design </li></ul></ul><ul><li>Inverse (Understand) </li></ul><ul><ul><li>Human-like performance </li></ul></ul><ul><ul><li>Recognizing/finding objects, image search </li></ul></ul><ul><ul><li>Challenging ! </li></ul></ul>
    45. 59. Power of the Processor Power of the Network Power of People 1985 1995 2005 2015 Powershifts
    46. 60. Crowdsourcing http://www.wired.com/wired/archive/14.06/crowds.html Amazon Mechanical Turk: Steve Fossett search ReCAPTCHA=OCR
    47. 61. Visual Social Computing <ul><li>Image-based commerce </li></ul><ul><ul><li>By the people, for the people, of the people </li></ul></ul><ul><li>Next Trend in India ? </li></ul><ul><ul><li>Harnessing human power beyond language barriers </li></ul></ul><ul><li>Business Model ? </li></ul><ul><ul><li>Outsource -> Image-based Crowdsource </li></ul></ul>
    48. 62. Cameras in Developing Countries http://news.bbc.co.uk/2/hi/south_asia/7147796.stm Community news program run by village women
    49. 63. Developing Countries: CAMForms <ul><li>Paper forms with barcodes </li></ul><ul><li>83-bit 2D codes (including seven bits of error correction) </li></ul>Parikh (2005)
    50. 64. User Generated Content Visualization <ul><li>Google Map overlayed with geo-tagged photos </li></ul><ul><li>Image-based Mashups </li></ul>http://phototour.cs.washington.edu/ Photo Synth
    51. 65. Socio-Political Goals: B’Tselem From websiteof : Israeli Information Center for Human Rights in the Occupied Territories
    52. 66. Truth in Images From Hany Farid LA Times March’03
    53. 67. Vision thru tongue http://www.pbs.org/kcet/wiredscience/story/97-mixed_feelings.html Solutions for the Visually Challenged http://www.seeingwithsound.com/
    54. 68. Camera Culture Ramesh Raskar How will the next billion cameras change the social culture ? How can we augment the camera to support best ‘image search’ ? How will camera improve trust and social stability ? How will movie-making, news reporting change ? Next model for image-based commerce ? Visual Social Computing
    55. 69. MIT Media Lab Workshops <ul><li>Session before lunch (12:15 to 1pm): </li></ul><ul><li>How to Invent? </li></ul><ul><li>Health, Entertainment and User Interfaces </li></ul><ul><li>Session after lunch: (by Invitation Only) </li></ul><ul><li>How to innovate </li></ul><ul><li>Brainstorming </li></ul>
    56. 70. Camera Culture Ramesh Raskar Ramesh Raskar Associate Professor, MIT Media Lab http://raskar.info New Emerging Technologies Medical Imaging Entertainment User Interfaces Industrial Vision The impact of Next Billion Cameras Movie-making, news reporting Social stability Visual Social Computing Image-based commerce Future of Imaging
    57. 71. END Part 1
    58. 72. Start Part 2
    59. 73. Camera Culture Ramesh Raskar Camera Culture MIT Media Lab http://raskar.info http://cameraculture.info Ramesh Raskar Associate Professor
    60. 74. After X , what is ne X t How to Invent? Ramesh Raskar, MIT Media Lab
    61. 75. X d X++ X X+Y X X ne X t Ramesh Raskar, MIT Media Lab
    62. 76. Simple Exercise .. <ul><li>Image Compression </li></ul><ul><ul><li>Save Bandwidth and storage </li></ul></ul>What is ne X t
    63. 77. Strategy #1: X d <ul><li>Extend it to next dimension (or some other) dimension </li></ul><ul><ul><li>Flickr to Youtube </li></ul></ul><ul><ul><li>Wikipedia to .. ? </li></ul></ul><ul><li>Text, Audio (Speech), Image, Video .. Whats next ? </li></ul><ul><ul><li>CD .. </li></ul></ul><ul><li>Images to infrared, sound, ultrasound </li></ul><ul><li>Macro scale to microscale </li></ul><ul><ul><ul><li>Airbag for car to airbag for .. ? </li></ul></ul></ul>
    64. 78. Strategy #2: X+Y <ul><li>Fusion of the dissimilar </li></ul><ul><ul><li>More dissimilar, more spectacular the output </li></ul></ul><ul><li>Example </li></ul><ul><ul><li>Scientific imaging + Photography </li></ul></ul><ul><ul><ul><li>Coded aperture </li></ul></ul></ul><ul><ul><ul><li>Tomography </li></ul></ul></ul>
    65. 79. Prototype camera <ul><li>4000 × 4000 pixels ÷ 292 × 292 lenses = 14 × 14 pixels per lens </li></ul>Contax medium format camera Kodak 16-megapixel sensor Adaptive Optics microlens array 125 μ square-sided microlenses
    66. 80. Example of digital refocusing
    67. 81. Imaging in Sciences: Computer Tomography <ul><li>http://info.med.yale.edu/intmed/cardio/imaging/techniques/ct_imaging/ </li></ul>
    68. 82. Self Evaluation of Eye
    69. 84. Strategy #3: X Do exactly the opposite <ul><li>Processing, Memory, Bandwidth </li></ul><ul><ul><li>In Computing world, in any era, one of this is a bottleneck </li></ul></ul><ul><ul><li>But overtime, they change. You can often take an older idea and do exactly the opposite. </li></ul></ul><ul><ul><li>E.g. bandwidth is now considered virtually limitless </li></ul></ul><ul><li>Business Process Reengineering (BPR) </li></ul><ul><ul><li>Michael Hammer, James Champy, 1990s </li></ul></ul><ul><li>In imaging: </li></ul><ul><ul><li>SLR: Faster mirror flip or no mirror flip </li></ul></ul><ul><ul><ul><li>Companies spent years improving mirror flip speed </li></ul></ul></ul><ul><ul><ul><li>Why not just remove it? </li></ul></ul></ul><ul><li>More computation </li></ul><ul><li>Less light </li></ul>
    70. 85. <ul><li>Toll Free calls </li></ul><ul><li>Reverse Auction </li></ul>
    71. 86. Power of the Processor Power of the Network Power of People 1985 1995 2005 2015 Powershifts
    72. 87. Larval Trematode Worm
    73. 88. Strategy #4: X <ul><li>Given a Hammer .. </li></ul><ul><ul><li>Find all the nails </li></ul></ul><ul><ul><li>Sometimes even screws and bolts </li></ul></ul><ul><li>Given a cool solution/technique/Opportunity </li></ul><ul><ul><li>Find other problems </li></ul></ul><ul><ul><li>(Where to find them?) </li></ul></ul><ul><li>Examples </li></ul><ul><ul><li>Peltier effect: </li></ul></ul><ul><ul><ul><li>Create a jacket that keeps you warm or cold </li></ul></ul></ul><ul><ul><li>Mobile phone opportunity </li></ul></ul>
    74. 89. Strategy #5: X <ul><li>Given a nail, </li></ul><ul><ul><li>Find all hammers </li></ul></ul><ul><ul><li>Sometimes even screwdrivers and pliers may work </li></ul></ul><ul><li>Given a problem, </li></ul><ul><ul><li>Find other solutions </li></ul></ul><ul><ul><li>(Where to find them?) </li></ul></ul><ul><li>Examples </li></ul><ul><ul><li>App store (Apple) .. Open platform for all devices </li></ul></ul><ul><ul><li>.. </li></ul></ul>
    75. 90. Strategy #6: X++ <ul><li>Pick your adjective .. </li></ul><ul><li>Making it faster, better, cheaper </li></ul><ul><li>neXt = adjective + X </li></ul>
    76. 91. X++ : Add your favorite adjective <ul><li>Context aware, </li></ul><ul><li>Adaptive </li></ul><ul><li>(temporally) Coherent, </li></ul><ul><li>Hierarchical, </li></ul><ul><li>Progressive </li></ul><ul><li>Efficient </li></ul><ul><li>Parallelized </li></ul><ul><li>Distributed </li></ul><ul><li>Good example: Image or video compression schemes </li></ul>
    77. 92. X++ : Add your favorite adjective <ul><li>Good example: Image or video compression schemes </li></ul><ul><li>But X++ is a sign </li></ul><ul><ul><li>The field is maturing in terms of research but booming in business impact </li></ul></ul><ul><li>Kaizen </li></ul><ul><ul><li>Small incremental changes </li></ul></ul><ul><ul><li>Japanese Management styles (6sigma, Kanban) </li></ul></ul><ul><ul><li>Mainly to save money/time/resources. Not everyone can do it. GM, 0.84 suggestions per employee vs Toyota 18. GM accepted 23%, Toyota 90% </li></ul></ul>
    78. 93. Hexagon Corners for Different Stages <ul><li>[Bruce Tuckman, 1965] </li></ul><ul><li>Forming </li></ul><ul><li>Storming </li></ul><ul><li>Norming </li></ul><ul><li>Performing </li></ul><ul><li>Adjourning </li></ul>
    79. 94. Pitfalls <ul><li>These six ways are only a start </li></ul><ul><li>They are a good mental exercise and will allow you to train as a researcher </li></ul><ul><li>Great for projects </li></ul><ul><li>But </li></ul><ul><ul><li>Maynot produce radically new ideas </li></ul></ul><ul><ul><li>Sometimes a danger of being labeled incremental </li></ul></ul><ul><ul><li>Could be into ‘public domain ideas’ </li></ul></ul>
    80. 95. What are Bad ideas to pursue <ul><li>X then Y (then Z) </li></ul><ul><ul><li>X+Y is great with true fusion, fusion of dissimilar is best </li></ul></ul><ul><ul><li>But avoid a ‘pipeline’ systems, where the output of one is THEN channeled into the input of the next stage, and non of the components are novel </li></ul></ul><ul><li>Follow the hype (too much competition) </li></ul><ul><li>Do because it can be done </li></ul><ul><ul><li>(Why do we climb a mountain? because it is there! ) </li></ul></ul><ul><ul><li>But only the first one gets a credit. </li></ul></ul><ul><ul><li>May make you strong, and give you a sense of achievement but not a research project. </li></ul></ul>
    81. 96. X d X++ X X+Y X X ne X t Ramesh Raskar, MIT Media Lab
    82. 97. End Part 2
    83. 98. Camera Culture Ramesh Raskar Camera Culture MIT Media Lab http://raskar.info http://cameraculture.info
    84. 99. Extra Slides
    85. 100. CREATIVITY Civic Media Next Billion Network Siftables Scratch Hyper - Creativity High- Low Tech CREATIVE ANARCHY Civic Media Next Billion Network Siftables Scratch High- Low Tech
    86. 101. AWARENESS New Media Medicine Smart Cities Sense-able Societies Hyper- Awareness Human Speechome X-Reality TOTAL AWARENESS Human Speechome Sense-able Societies X-Reality Smart Cities New Media Medicine
    87. 102. Today a billion plus people enjoy the benefits of a digital lifestyle BUT The deep impact of technology on individuals, society and business lies just ahead. Frank Moss, MIT Media Lab
    88. 103. Future of the Individual Future of Society Future of Business
    89. 104. Future of the Individual Future of Society Future of Business

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