smartdata webinar: applying neocortical research to streaming analytics
TRANSCRIPT
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APPLYING NEOCORTICAL ALGORITHMS TO STREAMING ANALYTICS
SmartData Webinar September 10, 2015
Subutai Ahmad [email protected]
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Revenue Forecasting Customer Story
10pm:
Team of 10 analysts
5 am: “Dear CEO, today’s revenue forecast is $63.4M.”
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Objectives for next generation: Generate predictions every 15-minutes Track all product categories and geographies (hundreds of thousands) React rapidly to changes
Problems:
Cumbersome data infrastructure Algorithm approach completely unclear
Revenue Forecasting Customer Story
10pm:
Team of 10 analysts
5 am: “Dear CEO, today’s revenue forecast is $63.4M.”
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“How Machine Learning Is Done”
Data Prep
Craft Input Features
Training Methodology
Choose Algorithm
Test & Validate
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“How Machine Learning Is Done”
Data Prep
Craft Input Features
Training Methodology
Choose Algorithm
Test & Validate
Deploy
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Streaming data
Automated model creation Continuous learning Temporal inference
Predictions Anomalies Actions
The Future of Data Analytics
Solution:
Streaming data infrastructure
New algorithm approach
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Numenta History
2005 – 2009 § Hierarchical Temporal Memory theory
§ First generation algorithms § Vision Toolkit
2002
2004
2009 – 2014 § 2nd generation HTM algorithms
§ Sequence & cont. learning § Streaming data applications
§ HTM open source project
§ Grok 1.0 for anomaly detection
2014 – Today § Streaming applications
§ Grok for Stocks § Research on 3rd generation algs
§ Sensorimotor
§ Feedback
2005
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Properties Of The Neocortex
retina
cochlea
somatic
data stream
motor control
“Hierarchical Temporal Memory” (HTM)
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Properties Of The Neocortex
1) Hierarchy of nearly identical regions - common algorithm
retina
cochlea
somatic
2) Sparse Distribution Representations - common data structure
data stream
3) Regions are mostly sequence memory - inference - motor motor control
4) Every region is continually learning - fully automated
“Hierarchical Temporal Memory” (HTM)
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HTM Learning Algorithm
Models a small slice of cortex 1) High capacity memory-based system 2) Models complex high-order temporal sequences 3) Makes predictions and detects anomalies 4) Continuously learning 5) No sensitive parameters 6) Runs in real time on a laptop
Basic building block of neocortex and Machine Intelligence Whitepaper and full source code available: github.com/numenta
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HTM
Encoder SDR
Prediction Point anomaly Time average Historical comparison Anomaly score
Metric(s)
System Anomaly Scores
& Predictions
HTM Engine For Streaming Analytics
HTM
Encoder SDR
Prediction Point anomaly Time average Historical comparison Anomaly score
SDR Metric N
.
.
.
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GROK Server anomalies
Rogue human behavior
Geospa6al tracking
Stock & market anomalies
Applications Of The HTM Engine
Social media anomalies (Twi?er)
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Grok: Anomaly Detection For Amazon Web Services
§ Unique value of HTM algorithms § Automated model creation: configure hundreds of models in minutes § Continuously learning: automatically adapts to changes § Detects sophisticated temporal anomalies
Continuous learning Unpredictable data Temporal anomalies
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HTM for Stocks: Detecting Unusual Market Behavior
Companies sorted by unusual behavior
Stock price Stock volume Twitter chatter
Tweets reveal cause
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Anomaly Detection in Geospatial Tracking Data
HTM
Encoder SDRs Prediction Anomaly Detection Classification
GPS+ Velocity
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Anomaly Detection in Geospatial Tracking Data
HTM
Encoder SDRs Prediction Anomaly Detection Classification
GPS+ Velocity
Trick: convert GPS coordinates into an SDR After input is encoded as an SDR, learning algorithm is agnostic
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Learning Normal Behavior
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Learning Normal Behavior
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Learning Normal Behavior
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Geospatial Anomalies
Deviation in path Change in direction
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Multiple paths are OK Unusual change in speed
Geospatial Anomalies
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These HTM Applications Use Exact Same Code Base
HTM learning algorithms Identical learning parameters Wide applicability across sensor types
GROK Server anomalies
Rogue human behavior
Geospa6al tracking
Stock & market anomalies
Social media anomalies (Twi?er)
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Benchmarking Streaming Anomaly Detection
Traditional benchmarks don’t apply: – Don’t incorporate -me, e.g. favor early
detec-on over later detec-ons – Usually batch format – Very few benchmarks with real world
data Numenta Anomaly Benchmark (NAB)
– Scoring methodology favors early detec-on
– Incorporates con-nuous learning (learning a new normal baseline)
– Labeled real world data streams – Different “applica-on profiles”
HTM tested against 3 algorithms
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Benchmarking Streaming Anomaly Detection
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Benchmarking Streaming Anomaly Detection
HTM detects anomaly earlier
Other algorithms
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Numenta Community & Partnerships
- NuPIC
- Open source community at numenta.org
- > 3,000 Github followers, > 160 contributors
- Cortical.io - Natural Language Processing
- IBM - Core HTM research
- Novel hardware architectures for HTMs
- Avik Partners - HTM Grok anomaly detection and analytics for IT
- grokstream.com
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Future of Data is Streaming Data
- High velocity sensory streams with rapidly changing statistics - Massive number of models
- Problem: existing batch algorithms cannot scale
Cortical Algorithms Show The Way - Proof that systems can:
Automatically create models
Continuously learn Model sophisticated temporal streams
- HTM learning algorithms implement cortical principles - Can demonstrate working applications today