This document discusses big data analytics, intelligent applications, and the future of artificial intelligence. It covers the current state of the art in machine learning, data mining, and data science. It also addresses challenges in scaling artificial intelligence to meet future data generation rates, which will outpace computation and communication speeds. New distributed computation models that move code to data instead of vice versa will be needed. Fundamental limits of physics, networks and computation must also be considered in architecting intelligent systems of tomorrow.

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Architecting Intelligence
Big Data Analytics and
Building Intelligent Applications

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Our discussion today
Big Data Analytics and Intelligent Applications
• The puzzle, the hype, the customer?
• Man-machine collaboration
State of the Art
• Practical AI, Machine Learning, Data Mining
• Data Science, Data Games
What does the future guarantee?
• Physics, Networks and Computation
• New computation models?

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Big Data, the Meme

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Big (Data Analytics) Distraction

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Big (Data) Crowd

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Start unraveling the complexity
What do I want to communicate that currently requires a
significant amount of time and energy to analyze, interpret,
and share?
• Stuart Frankel, “Data Scientists Don’t Scale”, Harvard Business Review,
May 2015
What economic value will my customer gain from Big Data?

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Flytxt
Our vision is to create >10% measurable economic value for Mobile
Enterprises through Big Data Analytics
Flytxt’s solutions create incremental revenues from new and existing
sources, optimize margins and enhance customer experience
Dutch company with corporate office in Dubai, global delivery centres in
India and regional presence in Mexico City, Johannesburg, Singapore,
Dhaka and Nairobi.
Sample text
Awards and Recognitions

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Benefits delivered to customers
PartnersOperators
Proven across many Countries, Brands and Logos
Brands
IIT DELHI
4%
Increase
in Gross Revenue
30%
Growth
in Mobile Money users
10%
Growth
in Data Users
105%
Increase
in Special offer Sales
300%
Increase
in Store Footfall
25%
Drop
in Churn

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Big Data Technology Architecture – the Flytxt example

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Our discussion today (Part 2)
Big Data Analytics and Intelligent Applications
• The puzzle, the hype, the customer?
• Man-machine collaboration
State of the Art
• AI, Machine Learning, Data Mining
• Data Science, Data Games
What does the future guarantee?
• Physics, Networks and Computation
• New computation models?

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Practical AI: Personalized Driving
Navigator suggest alternative route
due to traffic congestion
System identifies primary/secondary driver
Bluetooth
connection to Car
Systems
Car system
connects with
database to access
unique ID info
Infotainment
settings are
modified
(language
preference, radio
station…)
Navigator presents favorite
destinations
Connected office identifies next meeting
happens in 10 min and offers re-scheduling

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Practical AI: Pattern Classification in Location
Analytics
Automatic classification of venues / routes
based on their features
 Each venue/route is represented by a set
of features
 Labeled examples corresponding to
various venue types / route types which
represent classes
 Learn a decision boundary that separates
the classes & then make predictions

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Customer
Marketing
Program Product Rol
Infra-
structure
Location
Descriptive
Exploratory
Heuristic
Predictive
Prescriptive
Visualization
CLV Monitoring
Opportunity
Identification
Behavioral
Variations
Action Prediction
Personalized
Recommendation
Effectiveness
Measurement
Program Reach
Analysis
Business impact
Analysis
Outcome
Forecasting
Impact Optimization
Product Popularity
Monitoring
Product Promotion
Analysis
Product Association
Profitability
Simulation
Product Promotion
Recommendation
Business Health
Monitoring
KPI Impact Analysis
Business Impact
measurement
Impact Forecasting
Yield Optimization
Utilization
Monitoring
Challenge
Identification
Cost Benefit
Analysis
Event Prediction
Optimization
Recommendation
Geo-Spatial
Reporting
Location Affinity
Analysis
Location- Behavior
Association
Location based
Forecasting
Location based
Recommendation
Roots of Practical AI: Analytics Models built by Data
Scientists

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Data Sciences: State of the Art
KDD Cup: organized by ACM Special Interest Group on
Knowledge Discovery and Data Mining
2010: Predict student performance on mathematical
problems from Intelligent Tutoring System logs
2011: Recommending Music Items based on the Yahoo! Music
Dataset
2012: Predict which information sources one user might
follow in Weibo (Chinese “twitter”)
2013: Determine whether an author has written a given paper
2014: Predict funding requests that deserve an A+ (for
DonorsChoose.org)
2015: Predict student dropout on a Massive Open Online
Course platform (XuetangX)

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Our discussion today (Part 3)
Big Data Analytics and Intelligent Applications
• The puzzle, the hype, the customer?
• Man-machine collaboration
State of the Art?
• AI, Machine Learning, Data Mining
• Data Science, Data Games
What does the future guarantee?
• Physics, Networks and Computation
• New computation models?

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Memoirs from the Past: Hilbert’s Program
In 1900, David Hilbert, a very influential universal
mathematician, announced a grand search for a complete and
consistent set of axioms for all mathematics
In 1931, Kurt Gödel announced his discovery of the
Incompleteness Theorem: There will always be statements
about the natural numbers that are true, but that are
unprovable within the system
Hilbert probably dedicated his life trying to prove his
hypothesis, which Gödel proved cannot be true!
However, Gödel’s work inspired Alan Turing and Alonzo
Church, and in 1936, they mathematically defined
“computation”

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The future AI platform is a network!
Courtesy: Maulik Kamdar, Stanford University

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Future AI agents
AI agents will compute; with data that gets generated on
many devices
2025: 100 billion connected devices, 175 zeta bytes of data
per year (Huawei)
Data volumes will grow faster than any network or computer
can be sized
How will you scale the AI of tomorrow?

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Practical AI: Moving data, moving code
Code must meet data to compute – code moves and/or data
does, across a (wireless) network
History: All data moved to where the code was
Near past: Parallel and distributed computation – partition
code & data
Now: (approximately) Move code to where the data is
(Hadoop etc)
Future: Determine the code-data match and optimize
movement?
• Is there is a computational model for this?

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Physics, Networks, Computation – immutable laws
Energy dissipation in radiation (Gauss’s / Coulomb’s Laws)
• Low energy reception implies higher decoding error (Shannon’s Limit)
• How fast can memory-to-memory transfers happen?
Capacity of a wireless network is constrained by interference (e.g.
see Gupta & Kumar, 2000)
• Spectrum (# channels) available will remain finite
• Channel allocations will be dynamic, but how fast can two interfering pairs
find free channels?
Are there limits to local computation? (e.g. see works by Ning Xie,
Shai Vardi)
• Moving code or data implies “local” processing
• How much AI can be computed, and at what cost?

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Example: High-dimension Clustering
Basic machine learning algorithm to group nodes (users, people, devices)
by state (behavior)
• Each node produces a vector describing current state
• Nodes are clustered together by some measure of vector similarity
“Moving code” distributed implementations available today (on
Hadoop/Spark)
Future: Rate of change of state will outpace speeds of computation and
communication
Is the solution hierarchical, is the paradigm divide and conquer?
How will network & algorithm design and implementation change?
• Can all clustering problems be solved “locally”?

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Discussion summary
Big Data Analytics and Intelligent Applications
• Build for customer value, build simple solutions
State of the Art
• Practical AI and Data Sciences
What does the future guarantee?
• Need to scale AI compute: Data generation rates faster than compute
/ communication rates

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Thank You
www.flytxt.com