The document provides an overview of intelligent content and the evolution of content on the web. It defines intelligent content as content that expresses meaning in an open way such that data, information and knowledge can be accessed by people and applications. The document discusses how content on the web has evolved from documents with links to include more user-generated content and social aspects. Examples of intelligent applications that intersect multiple data sets are mentioned. The talk concludes by discussing different types of structured content and how the internet is evolving into a complex system with the web providing the basis for the nervous system.

1. Opening Keynote Address Salim Ismail January 29, 2009

2. Objectives for today Intelligent Content is… a definition Content on the web… an evolution evolution of data/infrastructure on the internet Social Media Examples of intelligent applications Where else is content intelligent? (off-web) Back to the Web – what’s next? Q&A

3. Who Am I? India/Canada/Europe/U.S (a decade each) Degree in theoretical physics Five years large scale database design Five years restructuring large European orgs Seven early stage companies Six years of internet startups Five of my own (PubSub, Confabb, Ångströ…) One year as VP Yahoo!, Head of Brickhouse Internal VC fund, analyzed 3000 ideas Sponsored four Yahoo Pipes (prototype), BravoNation, Y!Live, Fire Eagle Next gig to be announced in four days

4. What is ‘Intelligent Content ’ Content Data of some kind Wrapper Associated metadata The dream of the Semantic Web “ the semantics of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content . It derives from World Wide Web Consortium director Sir Tim Berners-Lee 's vision of the Web as a universal medium for data , information , and knowledge exchange.” All data, fully self-described for whatever purpose is required (slightly utopian… rather difficult)

5. What is ‘ Intelligent Content’ Many types of intelligence: Gardner: logical , linguistic , spatial , musical , kinesthetic , naturalist , intrapersonal , interpersonal and social intelligence Sternberg: Practical / Contextual - deals with the mental activity involved in attaining fit to context ” Uses adaptation, shaping, and selection Experiental / Creative Experience breaks down into novel/automated Componential / Analytical Break down problems and intuit solutions

6. For our purposes… Intelligence: The ability to take a pattern from one frame of reference an apply it to another Chess program Pagerank Cross data sets Conditional Intuitive (emotional) Sophisticated/complex Sophisticated (intuitive) PM Contextual/Conditional Pattern Matching Basic (Numerical) Pattern Matching

7. Two definitions of “Intelligent Content” Content can be considered intelligent when it expresses, in an open way, the full meaning underlying a communication such that the data, information and knowledge being expressed can be easily accessed and effectively leveraged by both people and the software applications that support them. - Joe Gollner Structurally rich and semantically aware, and is therefore automatically discoverable, reusable, reconfigurable and adaptable - Ann Rockley

8. More on intelligence Pattern Recognition is a key aspect of intelligence, but you also have Pattern creation? Patterns interacting with other patterns create new patterns (in other words, mashups : ) Stephen Wolfram – “A New Kind of Science” Class 1 Automata

9. Digressing to ‘Repetition’ Class 1 Class 2 Class 3

10. More on Repetition (recursive PM) Class 4 Reflections on Stephen Wolfram's "A New Kind of Science" http :// www. kurzweilai.net/meme/frame.html?main =/articles/art0464.html

11. Intelligent Content – Summary Context is key Who, what, where, when… (why) Pattern management across frames of reference In documents, SGML > XML > DITA > ?? Eventually, pattern management (intelligence) will be embedded in the content (document) W- ar- goin- on ho-iday w-th som- f-iends n-x- we-k Se- -ou wh-n w- ge- ba-k 25% of letters missing, yet meaning is conveyed

12. More pattern matching

13. Next topic… Intelligent Content is… a definition Content on the web… an evolution evolution of data/infrastructure on the internet Social Media Examples of intelligent applications Where else is content intelligent? (off-web) Back to the Web – what’s next? Q&A

14. Content on the Web What’s going on ?? What is the 50,000 foot view? (15,240 m ) How we got here – some history/context Information flows Technology Framework Internet 3.0 The Social Media wave Examples Image: Kevin Kelly

15. There’s lots going on today… ! Yahoo/Google/Microsoft/AOL/Facebook Blogs/feeds/RSS/ATOM//microblogging/lifestreaming Friendster/LinkedIn/Myspace/Bebo/Facebook Facebook Feed/FriendFeed/FriendFeedFilter/FeedaFriend Adsense/Panama/Beacon/adCenter Twitter/Twhirl/Tweetdeck/Twitterfone/Twitscan/TwitSnooze OpenID/IdentityCommons/IdentityGang/IdentityWoman Dataportability/Data Availability/OpenSocial… nottoosocialyet/FriendConnect/FacebookConnect/everybodybloodyconnect Icannotakeanymoreofthispleasestop

16. And a few companies…

17. Some call it Web 2.0 According to Tim O’Reilly…. Fundamentally: user participation (UGC) + Syndication

18. Led by Blogs Last few years - blogs exploded in use With the rise of the web in the 90s, we had millions of readers, but relatively few publishers (e.g. CNN, CNet) Now, due to the ease of publishing with blogs and other tools, we now also have millions of publishers 1m in 01/04, 10m in 01/05, 50m in 2006 and over 100m today Blogs now ‘overtaken’ Top Bloggers: stratifying into self-publishing journalists/pundits Flickr, MySpace, FaceBook, Bebo et al covering the rest User Generated Content splashing about everywhere Morphing into micro-blogging/lifestreaming

19. Other Web 2.0 Descriptors Services (as opposed to data) UGC (User Generated Content).. e.g. blogs Manipulate and ‘use’ data, not just presentation Low latency (real time) Data aggregation Social Networking Examples Flickr, Buzznet, Dabble Wordpress, Typepad, Drupal Eventful, Upcoming, Edgeio YouTube, MySpace, FaceBook Web 2.0: Facilitate Consumer to Consumer

20. Aggregators Blogs RSS Readers RSS Feeds Closed Syndication or Branding Browsers Web Pages Search Engines Internet Information Flows Open Syndication or Branding Pings Create/Publish/Discover Syndicate/Aggregate Read/Consume/Process Web 2.0 Web 1.0 Walled Garden DBs

21. Blogs Aggregators RSS Feeds Browsers Web Pages Search Engines Internet Information Flows Pings Open Syndication or Branding craigslist Closed Syndication or Branding Create/Publish/Discover Syndicate/Aggregate Read/Consume/Process Web 2.0 Web 1.0 Walled Gardens

22. Four key drivers… Syndication XML Low Latency Great UX

23. Web 2.0 = Internet 3.0 Web 2.0 = Internet 3.0 Structured Data (XML) Syndication is starting to take hold Event-based (Publish/Subscribe) Participatory UX = consumer to consumer Latest wave in internet companies (Facebook, YouTube)

24. Internet 3.0 We are increasingly watching … Messaging Request Response Publish Subscribe What’s your email address? Sending What’s your Website? Searching What’s your Blog/FB/Tw Watching Information Exchange Patterns Evolution of the Internet 80s Email 90s Web Browser 00s RSS Aggregator

25. Social Media Arrives Web – started as documents with linking Documents with links became nodes XML added metadata Lots of arbitrary data (about you) can now be added to the web Location Identity Friends Preferences Assets Purchases

26. Why is Social Media big? We are biological creatures Driven by social constructs “Who you know” drives a lot of what we do Context drives 80% of information value Who speaks gives a lot of context

27. Social Contexts Who do I know (Social Networks) : Flickr, Upcoming, MySpace, Bebo, FaceBook What am I doing (status updaters) : Twitter, Identi.ca, Skype, FB, Plaxo Where am I (location brokers) Fire Eagle, Plazes, Loopt, Brightkite Gov’t Data Enterprise Data E-commerce Transactional data (reviews/ratings) (offers for sale) (events) Self-publishing (where am I) (what am I doing) SN Profiles Blogs (about me) HTML Create/Publish/Discover Syndicate/Aggregate Read/Consume/Process Web 2.0 Web 1.0 Aggregators Blogs RSS Readers RSS Feeds Closed Syndication or Branding Browsers Web Pages Search Engines Walled Garden DBs Open Syndication or Branding Pings Social Networks Mini Feeds Closed Syndication or Branding Social Networks (email)

28. Create/Publish/Discover Syndicate/Aggregate Read/Consume/Process Web 2.0 Web 1.0 Aggregators Blogs RSS Readers RSS Feeds Closed Syndication or Branding Browsers Web Pages Search Engines We are just at the beginning Open Syndication or Branding Pings Social Networks Mini Feeds Closed Syndication or Branding Social Networks (email) Twhirl EMAIL !!! Walled Garden DBs craigslist

29. A web of data – Tom Coates http://www.plasticbag.org/files/native/native_to_a_web_of_data.pdf

30. Web Architecture – Dion Hinchcliffe

31. Thoughts on Social Media SM has become the starting point for consumers But, computers are binary, relationships aren’t 80/20 rule SNs will hold who’s connected to whom, but not more Leverage with other types of information

32. Evolution of a Platform New paradigm appears Some companies launch One gets traction and quickly dominates Walled Gardens take hold The competition fights back and force openness Walled Gardens collapse kicking and screaming Saturation New Paradigm appears (go to step 1) Social Media

33. Problem Spaces in Social Media Overload filtering Underload discovery Disambiguation identity Duplication (republishing)

34. Example: Ångströ News published by people on your social networks has proven to be “intriguing and addictive” News published about professionals in your network is actionable and often business critical A board member raises a new fund A colleague gets promoted An investor is featured in the news Google Alerts is about the only tool…

35. A Social Graph (400

36. News About Your Network Enter LinkedIn credentials Analyze bios and profiles Crawl news sources Apply algorithms Rank stories by: Recency KudoRank NameSense Source Quality User Feedback Newspaper interface User comments & votes Discover and share business-critical news

37. The News Cycle Discovery is increasingly happening via SM Mary Meeker

38. Example: Confabb

39. Live Traffic

40. Yelp Intersecting multiple data sets Users Review Comments Feedback Ratings

41. Intelligent Yelps Doctors Service Types Bars Location Mechanical Turk Restaurants Ratings Comments Reputation Favorites Sophisticated (intuitive) PM Contextual/Conditional Pattern Matching Basic (Numerical) Pattern Matching

42. Next topic… Intelligent Content is… a definition Content on the web… an evolution evolution of data/infrastructure on the internet Social Media Examples of intelligent applications Where else is content intelligent? (off-web) Back to the Web – what’s next? Q&A

43. Layered Intelligence GPS GPS Navigation RFID Robotics Mechanical Turk Sensors Supply Chain Mechanical Turk Crowd-Sourcing Sophisticated (intuitive) PM Contextual/Conditional Pattern Matching Basic (Numerical) Pattern Matching

44. AI & Robotics Ubiquitous computing and more-powerful computers Huge amounts of data from the Internet and physical sensors Algorithms that learn and improve over time Software that's able to deal with uncertainty, incompleteness and surprises Software agents that can weigh costs and benefits Integration of separate fields such as speech, vision, robotics, sensors and machine learning Computerworld.com – 1/26/09 - http://bit.ly/ts5Q

45. Not far away… 3D printing of organs Artificial Retinas Christopher deCharms http://abcnews.go.com/Technology/story?id=1603783&page=1

46. Next topic… Intelligent Content is… a definition Content on the web… an evolution evolution of data/infrastructure on the internet Social Media Examples Where else is content intelligent? (off-web) Back to the Web – what’s next? Q&A

47. Internet 3.0 – The Nervous System The internet is evolving into a complex organism Search is the memory Web 2.0 provides the basis for the nervous system The key question is: “ Whenever…”

48. Types of Structured Content Gov’t Data Enterprise Data E-commerce Transactional data (reviews/ratings) (offers for sale) (events) Self-publishing (where am I) (what am I doing) SN Profiles Blogs (about me) HTML Create/Publish/Discover Syndicate/Aggregate Read/Consume/Process Web 2.0 Web 1.0 Aggregators Blogs RSS Readers RSS Feeds Closed Syndication or Branding Browsers Web Pages Search Engines Walled Garden DBs Open Syndication or Branding Pings Social Networks Mini Feeds Closed Syndication or Branding Social Networks (email) 80s Email 90s Web Browser 00s RSS Aggregator Messaging Request Response Publish Subscribe What’s your email address? Sending What’s your Website? Searching What’s your Feed/Blog? Watching Information Exchange Patterns Evolution of the Internet

49. CONTENT ON THE WEB Crowd-sourcing ideas Artist: Aaron Koblin

50. The next 5000 days on the web http://splicd.com/yDYCf4ONh5M/137/354 Today…

51. Layered Intelligence Gov’t Data Enterprise Data E-commerce Transactional data (reviews/ratings) (offers for sale) (events) Self-publishing (where am I) (what am I doing) SN Profiles Blogs (about me) HTML Sophisticated (intuitive) PM Contextual/Conditional Pattern Matching Basic (Numerical) Pattern Matching

52. Thanks…. http://salimismail.com ww.twitter.com/salimismail