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Dr. Alfred Z. SpectorVP, Research and Special InitiativesGoogle, Inc.WWW 2009, Madrid, 24 April 2009
The ContinuingThe ContinuingMMeettaammoorrpphhoossiiss
Of the WebOf the Web
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Outline
1. Federation, Reach, and Evolution
2. Extraordinary Achievements of Note
3. The Evolutionary Path Forward
4. 3 Major Extraordinary Advances Brewing:
A. Totally Transparent Processing
B. The Rule of Distributed Computing
C. Hybrid, not Artificial, Intelligence
5. Some Research Challenges
6. Conclusions
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Google's Mission and Google Research
Organizing the world’s information andMaking it universally accessible and useful.
A research organization optimized for in situ work
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Federation, Reach, and Evolution
• The simplicity of the early web standards were genius
– Federated name space
– Access (HTTP)
– Simple data format (HTML)
– Extensibility!
• Not over-architected in any dimension
• Brilliant omissions (or at least, mostly so )
– Security
– Read-write data
– Semantics
• Interesting contrast to wide-area file systems work like AFS
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A Semi-Random Walk to Extraordinary Achievements
• The virtuous circle
– Initial simplicity begat data and usage
– Usage generated more data and transactions
– Data modalities diversified
– User experience blossomed
• Architectural limitations were addressed as needed
• A bottom-up architectural evolution repeatedly favoring local optimization has resulted in truly momentous results.
– The virtual Library of Alexandria
– The search engine
– The serving of the long tail
– Vast changes in business models
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Additional, Architectural Achievements
• Network
– The Web became the catalyst for the rapid internet build-out
• The High Performance Cluster (old word, “multi-computer”)
– The federated architecture, perhaps strangely, did not obviate the need for large processing complexes.
• Some workloads require high throughput, low latency, massive data, albeit, embarrassingly parallel
• The answer: parallel clusters of O(105) CPUs & O(1017) storage
• Note “An order of magnitude is a qualitative difference!”
• Browser
– With a few plug-ins, the application programming model of choice
– Perhaps, the key client operating system functionality
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The Evolutionary Path Forward to New Accomplishments
• Application mix will continue to grow in unpredictable ways:
– Four areas in flux today: publishing, education, healthcare and government
• Systems will evolve: ubiquitous high performance networking, distributed computing, new end-user devices, ...
• Three truly big results brewing:
1.Totally Transparent Processing
2.Ideal Distributed Computing
3.Hybrid, Not Artificial, Intelligence
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Totally Transparent Processing
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Totally Transparent Processing
D: The set of all end-user
access devices
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L: The set of all human
languages
M: The set of all modalities
C: The set of all corpora
Personal Computers
Phone
Media Players/Readers
Telematics
Set-top Boxes
Appliances
Health devices
…
Current languages
Historical languages
Other forms of human notation
Possible language specialization
Formal languages
…
Text
Image
Audio
Video
Graphics
Other sensor-based data
…
The normal web
The deep web
Periodicals
Books
Catalogs
Blogs
Geodata
Scientific datasets
Health data
…
dD, lL, mM, cC
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Types of Transparent Processing
• Search, of many forms
• Navigation and Suggestion
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• Transformational Communication
• Information Fusion
• Some Google Examples:
• Universal search
• Voice Search
• Find Similar, applied to images
• Google Translate, particularly in mash-ups
• Combining images and maps
• Audio transcription
• Images and 3d models
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Fluidity Among the Modalities
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Last two arrows are easily conceivable.
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The New Frontier of Web Search – Better/Faster Queries
Query completion before: Used a fixed dictionary, e.g., in emacs, bash, T9, etc.
Query suggestion today: Model queries with query logs, serve them dynamically
Technical challenges:
• response-time, coverage, freshness, corpus dependency (YouTube, image, mobile)
• domain dependent: rea -> real madrid good suggestion in Spain
• diversity (danger of popularity), filtering out duplicates, inappropriate results, etc.
Impact: Made possible by scale,
• the richer the query log corpus, the better
• the faster the response time, the better
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Voice Search
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Challenges and Rationale for Success
Technically this is very challenging:o Huge vocabulary o Variability in accento Background noise
What makes this possible:o Scalable technologyo Data inputs: Query logs, voice logso Compute power
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Transcriptions in Google Voice
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Lots of data: utterance ROC curves: incl. rejection)
combination
speech transcriptions
web queries
business listing databases
(better)
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The Benefits of Unsupervised Training
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Google Translate
2001 - 2004 2006
2001-2004 2006RBMT – 3rd Party2001• French• Italian• Spanish• Portuguese• German2004• Chinese• Japanese• Korean
SMT - Google• Chinese• Arabic• RussianRBMT – 3rd Party• French• Italian• Spanish• Portuguese• Japanese• Korean
2009
2009
2007
2007SMT - Google• Arabic • Chinese (S)• Chinese (T)• Dutch• French• German• Greek• Italian• Korean• Japanese• Russian• Spanish• Portuguese
RBMT – Rules-based machine translation
SMT – Statistical (data-driven) machine translation
2008
2008SMT - Google• Arabic • Bulgarian • Catalan• Chinese (S)• Chinese (T)• Croatian• Czech• Danish• Dutch• Filipino• Finnish• French• German• Greek• Hebrew• Hindi• Indonesian• Italian• Japanese
• Korean• Latvian• Lithuanian• Norwegian• Polish• Portuguese• Romanian• Russian• Serbian• Slovak• Slovenian• Spanish• Swedish• Ukrainian• Vietnamese
SMT - Google• Albanian• Arabic • Bulgarian • Catalan• Chinese (S)• Chinese (T)• Croatian• Czech• Danish• Dutch• Estonian• Filipino• Finnish• French• Galician• German• Greek• Hebrew• Hungarian• Hindi• Indonesian• Italian• Japanese
• Korean• Latvian• Lithuanian• Maltese• Norwegian• Polish• Portuguese• Romanian• Russian• Serbian• Slovak• Slovenian• Spanish• Swedish• Thai• Turkish• Ukrainian• Vietnamese
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Web Translation
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Web Translation Feedback
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Cross-language search
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23 Google Confidential
Cross-language search
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Translate My Page Gadget
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YouTube Caption Translation
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47.5
48.5
49.5
50.5
51.5
52.5
53.5
75M15
0M30
0M60
0M1.2B
2.5B 5B 10
B18
B
+weblm
AE BLEU[%]
Impact of data - More data is better …
+weblm = LM trained on 219B words of web data
News data
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Challenges in Image processing
• Recognition (people, landmarks, objects)
• Machine Learning
Visual Semantics
• Ego-motion estimation
• Multi-view stereo
Geometry
• Matching images and videos
• Image mosaicing
Correspondence
• Maps from aerial imagery
• OCR in all the world’s languages
Image Processing
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Image Analysis in Image Search
o [flowers], line drawings, clip art, photo, face o [porsche] , red, green, yellow, orange, ...
• Image Search helps users find the image they want quickly.• Understanding the actual content of an image is critical.• We've been focusing more and more on analyzing images• This has been rolling out over the last year.
• Both as user visible filters• Behind the scenes in our back-ends.
• Genre filters like clip art / line drawings / color are great examples
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Similar Images in Image Search• Google has just launched a "Similar Images" feature.
• Accessed by clicking on the similar images link under an image.
• It can also be accessed via preview thumbnails in the result frame.
• We think this will create a major shift in how to search for images.
• Searching for images can now become a navigational experience, where the text (or voice) query acts as a starting point
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Similar Images in Image Search
Refine by the content of a specific image.
Different "faces" of Paris...
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Similar Images in Image Search• A variety of features are used to determine visual similarity.
colortextureshape
colortextureshape
visualsimilarity
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Information Fusion across Modalities
Get lists of people names from the web by name detection techniques (NLP)
Scrape image search results using those candidate names as queries
Detect faces in the images and generate face signatures
Apply consistency learning to learn face models
Localize known people in Image/Video
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Totally Transparent Processing In-Process…
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Last two arrows are easily conceivable.
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Ideal Distributed Computing
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From AZS Pres. To US National Research Council Study on Dependability, May 18, 2004, after a late 80’s talk at Univ. of Michigan
Orthodox Architecture of 70’s and 80’s
ThreadsThreads
RPC/Method InvocationRPC/Method Invocation
TransactionalRPC
TransactionalRPC
Operating SystemOperating System
Managem
ent System
Dist. F
ile Sys.
Other
Objects
Security/T
ime
Nam
e/D
irectory
Application Server/Distributed Operating SystemApplication Server/Distributed Operating System
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What Wasn’t Internalized Very Well
• The application mix
• The true nature of global, open systems:
– Implications on systems, applications, mix and match.
• The implications of operations at true scale
– E.g., work on programming & runtimes predominated system mgmt.
• The complexity of the architecture that would result
– We tend to assume, if we can conceive it, it’s okay.
• The collection of further abstractions that would build on fundamentals then known
• In summary there was a limitation of understanding of (truly) large-scale, open integrated distributed systems
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Converging Progress
Distributed computingOpen systems approaches
Programming Languages & methodologies
Capability
Distrib.Computing
Human Interface Technologies
(broadly construed)
Security Technologies
Algorithms and Theoretical Results
Networking
OperatingSystems
Web technologies
Time
Long Term Geometric Growth in Processing, Network, Storage
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Ja
n-0
4
Ma
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Ju
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Terminological Evolution
Utility computing
Grid computing
Cloud computing
2004
2005
2006
2007
2008
Sear
ch te
rm p
opul
arit
y
Credit to Luiz André Barroso presentation on: A Case for Energy Proportional Computing
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Cloud Computing Architecture
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Internet
Operating System
Search
Maps
Apps,…
Tran
slate
ComputerCluster
Operating System
Search
Maps
Apps,…
Tran
slate
ComputerCluster
ComputerCluster
Operating System
Search
Maps
Apps,…
Tran
slate
All manner of networking hardware
Distributed Computing Infrastructure
Billionsof
endpoints
The “Cloud”
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Excitement in Distributed Systems
• Size of user community
• Storage Scale (requiring various characteristics)– E.g., security, privacy, availability,
• Processing Scale– High performance batch processing
– High throughput
– Low latency
• Rapid dynamics
• Highly variable end-user devices
• Communication Scale– Bandwidth
– Endpoints
• Efficiency– Equipment
– Communication
– Power
– Management
• Extensibility
• Compliance
• And more to come, no doubt
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Ideal Distributed Computing
• Arbitrarily high volume transactions
• And, various, partitionable batch process for learning, fusion, etc.
• Network
– Response-time and bandwidth as needed
• Cluster Processing, or “Cloud Computing” growing ever larger
– Massive parallelism to hit sweet spot of capital & operating efficiency
• Distributed computing
– Data sharing, function shipping, as needed
– Connected and disconnected operation, as seamless as possible
– Auto balancing of loads between client device and cloud elements
– Emphasis on manageability (newly, to handle consumers’ many endpoints)
• Significant efficiency gains
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Large networked clusters grow in a fully distributed world
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Hybrid, Not Artificial, Intelligence
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Hybrid, not Artificial, Intelligence
• “Artificial Intelligence” aimed at having computers as capable as people, often in very broad problem domains
• It has proven more useful for computers rather:
– To extend the capability of people, not in isolation
– To focus on more specific problem areas
• Aggregation of user responses has proven extremely valuable in learning
• Examples
– Feedback in Information Retrieval; e.g., in ranking or spelling correction
– Machine learning; e.g., image content analysis, speech recognition with semi-supervised learning
• Another example of bottom up successes
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Key: Holistic Approach To Design
VastComputer Services
Vast #of Users
But: Focus
Computer ServiceUser
Not:Focus
• Implications• Users and computers doing more than either could individually.• Virtuous circle from: Data and Processing, Reach, Feedback in a virtuous circle.
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1.3 searches per user
480,000,000 total pageviews
$4.78 RPM2,800,000,000 views
17.35
9995.55
56.76%
0.55060108 milliseconds
6.551607.44
-0.54
$7,660,400$0.303 CPA
2.7M
108
425,440.01
10,400
108 seconds/search
Empiricism - Let Measurement & Feedback Rule
-0.0000339
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My Long-held View on Semantics, Syntax, & Learning
• Large scale learning has proven surprisingly effective
• Learning is occurring over increasingly variegated features:
– Both Semantic
– And Syntactic, and generated in multiple ways
• In my WWW 2002 (Architecting Knowledge Middleware) and Semantic Web 2005 Keynotes, I referred to this as The Combination Hypothesis
• Today, I would refine this as the combination of approaches and learning from people.
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Research Challenges
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Challenges in Transparent Computing & Hybrid Intelligence
• Endless applications, with very new user interface implications
• Addressing limits to data
• Techniques to integrate user-feedback in acceptable fashions
• Approaches to new signal (e.g., annotations)
• Explanation, scale, and variance minimization in machine learning
• Information fusion/learning across diverse signals – The Combination Hypothesis, more generally
• Usability: devices and subpopulations
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Research Challenges in Ideal Distributed Computing
• Alternative designs that would give better energy efficiency at lower utilization
• Server O.S. design aimed at many highly-connected machines in one building
• Unifying abstractions for exploiting parallelism beyond inter-transaction parallelism and map-reduce
• Latency reduction
• A general model of replication including consistency choices, explained and codified
• Machine learning techniques applied to monitoring/controlling such systems
• Automatic, dynamic world-wide placement of data & computation to minimize latency and/or cost, given constraints on
• Building retrieval systems that efficiently and usably deal with ACLs
• The user interface to the user’s diverse processing and state
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3 Interesting Challenges
• Security and Privacy Technologies and Policy
• Application of technologies to health
• Applications to Government
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Conclusions
• The Web’s brilliant initial design lead to a series of local optimizations with extraordinary results
• Evolutionary advances continuing
• They are aggregating into at least 3 major advances:
A. Totally Transparent Processing
B. The Rule of Distributed Computing
C. Hybrid, not Artificial, Intelligence
• Challenges for academics and industrial researchers/engineers abound
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Thank you very much!
¡Muchas Gracias!