The Future of Search
•
2 j'aime•5,432 vues
View notes here: http://www.slideshare.net/JasonDonaldRowley/notes-on-46898401 This presentation was prepared for a 5-minute O'Reilly Ignite-style talk delivered for Professor James Evans's Internet and Society course at the University of Chicago. It details the current search landscape, some of the challenges facing incumbents like Google, and some of the products innovating on the core search experience.
Recommandé
Contenu connexe
Similaire à The Future of Search
Similaire à The Future of Search (20)
Plus de Jason D. Rowley
Plus de Jason D. Rowley (11)
Dernier
Dernier (11)
The Future of Search
- 1. The Future of Search jrowley@uchicago.edu & danya@uchicago.edu Organizing Principles Challenges 2 Potential Ways Forward: - Google’s Knowledge Graph - Microsoft Cortana & Google Now
- 2. Organizing Principles • Reduction of Interactional Friction • Connecting Intent with Action
- 3. “Flights from Chicago to NY” “UChicago to Billy Sunday”
- 4. The Billy Sunday Problem • Simply searching for “Billy Sunday” returns a mess of results • Google returns what it “thinks” is most relevant to me, even if the content isn’t relevant to me • This is illustrative of a bigger point
- 5. The problem with strings • String-based search is old. • Results in increased interactional friction
- 6. More Problems with String- based Search • The previous approach leaves users with two choices: • Parse the hodgepodge • Append the query with modifying terms • Both choices increase interactional friction == bad UX
- 7. Things > Strings • Across web platforms, we’ve seen a paradigm shift from “string thinking” to graph- based approach • See, for example: Facebook’s Social Graph & OGP, Google’s Knowledge Graph, LinkedIn’s Professional Graph, Wolfram’s Computational Knowledge Engine, &c.
- 8. Unpacking Google’s Knowledge Graph • Google’s attempt to structure its data the way humans structure knowledge internally. • Humans interpret the world in terms of things, not strings • The Knowledge Graph is a more “humane” way of interfacing with information
- 9. Back to Billy Sunday • Again, the string-based search approach yields messy results • “Billy Sunday” belongs to many different concepts • Ex. Billy Sunday is in the set of “Swanky Chicago Cocktail Bars”, the set of “Temperance Preachers” and “Professional Baseball Players”
- 11. Attacking the Context Problem • Google is attempting to address this context problem with the semantic Knowledge Graph • The Knowledge Graph is a growing, morphing database that can measure relationships between entities in 100-dimensional space • This is the core of Google’s search strategy going forward
- 12. Search in a multiplatform ecosystem • The arrival of third, fourth and fifth screens raises a number of questions about the future of search. • People behave differently on mobile devices • Desktop search UX does not map well onto small screens
- 13. Search as Background Process • Search is morphing from an active behavior to a passive one • Relevant information is represented in new ways and delivered through novel channels
- 15. Structured Data • Search engines use metadata to interpret data and give users relevant results • We’ve already seen some examples of structured data at work in search results • Some platforms (like Facebook and Wolfram|Alpha) are more structured than others
- 16. Delivering on Ambient Location Awareness • Passive streaming of your location helps search engines better infer intent • Some search engines use location data to trigger information discovery events for the user
- 17. Microsoft Cortana Google Now
- 19. Search as Conversation with Future AI Systems • The chat window may replace the search bar • Friendly voices may replace graphical user interface on keyboard-free devices • We already see this happening with “AI” services like DigitalGenius • And human-powered ones like Magic
- 21. Discussion Questions • What are the major defining features of the Knowledge Graph Optimization described in this presentation, and how do they relate to previous attempts to organize information that we have covered in the lecture and the readings? • How does the structure of information influence the user’s process of gathering information? How does it effect the production of future knowledge, and/or human action? Is someone searching for Billy Sunday who finds a different result than they were looking for likely to change their mind or behavior? • To what extent is search determined by structure, and to what extent is it determined by user? • Is “friction” entirely bad in search? Do we lose anything when we automate the suggestion process instead of leaving room for surprise?
Notes de l'éditeur
- Hi there This talk has three broad “movements” Organizing principles of Search UX Challenges facing Search 2 possible ways forward: Google’s Knowledge Graph (Contextual search) & Microsoft Cortana / Google Now (Seamless, background search)
- General IxD principle: Reducing “interactional friction” between the user and the thing being used. In search, the guiding principle is connecting a user with an intent to an action.
- Google is designed to reduce this interactional friction by giving me actionable results. Example: searching for “Flights from Chicago to New York” returns a little module through which I can book my flights Searching “UChicago to Billy Sunday” returns transit directions from UChicago to my favorite swanky cocktail bar
- What if I wanted to learn more about the baseball player turned evangelical preacher who vociferously argued against consuming alcohol? Simply typing “Billy Sunday” into the search bar gives me a hodgepodge of results, mostly favoring the bar. Google is returning what it thinks is most relevant to me. Given my love of swanky cocktail bars, they’d likely be right, but not in this case. This conflict is illustrative of a major problem facing search today.
- Strictly string-based search analyzes the concordance between a given search query and an index of content gathered on the web.
- This approach left users with one of two choices: Parse through the mixed results to find the contextually correct information Append the query with modifiers, like “Billy Sunday preacher” Both of these approaches violate the rule of reducing friction.
- Paradigm shift in web platforms from strings to things Network analysis and Structured data is the new reigning framework for understanding information on the web
- Knowledge Graph is Google’s attempt to mimic the way humans structure information We think in terms of things, not strings. Knowledge Graph is a more “humane” way of understanding information.
- The string based approach returns many kinds of results for the query “Billy Sunday” “Billy Sunday” belongs to many different sets. The set of ‘Swanky Chicago bars’ and set of ‘Temperance preachers’
- Google is attempting to divine from unstructured text structured relationships between entities. This allows Google to deliver users more relevant search results At least in theory
- We now see how Google is attempting to address the context problem by implementing the semantic Knowledge Graph But there’s another problem looming on the horizon: search as we know it is changing
- What does a search experience look like across 2-5 screens in simultaneous use? What happens when the keyboard shrinks or disappears? People’s search behavior is different on mobile Mobile platforms which rely on search as their core interface mechanism are often dead on arrival
- Search is morphing from an active behavior to a background process that surfaces information when it’s most pertinent This information is delivered in “cards” and mobile push notifications, not a set of links
- This new mode of search interaction is enabled by two factors: Use of structured data (like calendar events with locations) built atop existing information infra Mobile devices which stream location data Shifts search from reactive to anticipatory, from an active process to a passive one
- Search engines use structured data and metadata to deliver contextually relevant, highly actionable results Examples include Google’s Flights and Directions results. New platform like Facebook were built on incredibly well-structured data from the start, which gives them a potential search advantage in the future
- Passive streaming of your location will help search engines better infer intent. For example, the swanky cocktail bar may show up as the top result for searches made in Chicago, but the preacher may be the top result in Ames, IA where he was born
- “In platforms like Microsoft Cortana and Google Now, information is surfaced based not on a change in keyword, but a change in state.” - Stefan Weitz (link) Quick example: I receive notifications for when to leave my house based on the location of my calendar events and Google’s persistent traffic updates.
- As the kinds of devices we use changes, new input methods are required Voice-to-text is an increasingly popular way of performing queries, but it carries some problems with accuracy and computability (e.g. NLP is hard) Image and gesture-based search are other possibilities for the future
- The chat window may replace the search box We see this happening at small scale with narrowly scoped AI systems like my friend’s company, DigitalGenius And with human-powered digital assistants and concierge services like Magic.
- Search applications have migrated from our desks, onto our laps, into our pockets, onto our wrists and now our faces with platforms like Google Glass. Direct neuronal interface is without question on the horizon, but the timeline is uncertain. What happens when we have frictionless total informational awareness? Is it a good or bad thing that we’re outsourcing so much of our cognitive function to search companies?