Download A Systemic Artificial Intelligence (AI) Approach

A Systemic Artificial Intelligence (AI)
Approach to Difficult Text Analytics Tasks
Text Analytics World, Boston, 2013
Lars Hard, CTO
Agenda
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Difficult text analytics tasks
Feature extraction
Bio-inspired computational models
Systemic AI
Feature extraction – in a broad sense
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Definitions
“Systemic refers to something that is spread throughout,
system-wide, affecting a group or system, such as a
body, economy, market or society as a whole.”
Artificial Intelligence (AI), in a broad sense spanning over
machine learning, big data, bio-inspired computing
(neural networks, evolutionary computing). Often
”computational intelligence” is more appropriate
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• Large volumes of unstructured data
• Low quality metadata makes supervised training hard
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Non-existing
Contradictions
Duplicates
Errors
Manipulation
Etc.
• Dynamical content (constant change)
• Keyword search phrases = sparse text analytics problem
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• Many text analytics problems can be translated into
optimization problems (systemic and local model level)
• In the end, it is all about ”separation”
• There is no ONE model, regardless if it is feature
extraction, classification, etc.
• Verticalization is always good but requires connections
between multiple models
• Hard optimization problems are best approached with
bio-inspired models
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Verticalization
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Unstructured Data – Feature Extraction
- AI Powered problem specific feature
extraction arrays
- Rapid modeling combined with evolution
- Dynamical organization allowing inference
driven feature extraction based on actual
data
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Numerical features from any data
• Whole framework for effective pre-processing of data
for rapid extraction of numerical features
• Features are easy to process computationally – one
example is treating texts as a matrix of numbers with
multiple factors describing the texts
• Features can be transformed easily
• Features can be normalized
• Features can be extectuted dynamically (even driven by
inference) as they are needed or when more
information is available. Features can be optimized by
an evolutionary process, to adapt to certain types of
problems or difficulties
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Probabilistic decision tree – one typical model
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More metadata
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Objectives
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Data mining
Recommendations
Discovery / exploration
Diagnosis
Estimation
Classification
Etc.
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Multipe AI Modules
Diagnostics
(troubleshooting, medical diagnosis:
deterministic, probabilistic and hybrid)
Recommendation
(product recommendation based on soft
parameters, weight systems, feedback,
filters, etc.)
Estimation
(provide numerical predictions based on
artificial neural networks)
Configuration
(combine multiple components)
Optimization
(finding the best solution for highly
complex problems)
Image recognition
(specialized domains and/or
hierarchical recognition)
Graphs
Density & distance
calculations
Text classification
(automated metadata extraction,
hierachical classification)
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Multiple sources
Analytics, transformations and
domain modelling
Automated feature
extraction
Semi-automatic model designed and trained
manually
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A systemic model
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“Desktop AI” tools approach for insights and modeling
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Computational Intelligence (CI)
EVOLUTION AS A MODELFREE APPROACH TO AI
BRAIN AS AN INSPRIATION
Genetic Algortihms
Genetic Programming
Cellular Automata
Gene Expression
Programming
etc.
Bio-Inspired computing
COMPUTATIONAL INTELLIGENCE
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Multiple scoring models for factor importance
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After Evolutionary-Fuzzy Complexity Reduction
• Reducing 100K entries downto a rule-set of 6 simple rules
using only four dimensions. This rule-set is capable of correct
96% separation.
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Publishing Server
Publishing Server
(Machine
(Machine
Learning)
Machine
Learning
Learning)
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Data Store
ExpertMaker
ExpertMaker
AI/CI High-Speed
AI/CI High-Speed
AI
Processing
Processing
PROCESSING
Sync
Log Store
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❻
Load
Balancer
Data Mining
Data Mining
Server
Servers
Load
Balancer
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❷
❼
API
Model design
Admin
Interface
Monitoring
Application
back-end
Platform
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Client
Application
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Summary
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Rapid approach, quickly generate test models
Multiple attack points and multiple solutions
No advanced NLP
Manual training sets (supervised) can often be derived
from modeling process
• Ontologies are good if we need to understand, but most
problems cannot be understood given many features (=
high dimensionality)
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