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Geo Python16 keynote

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Romeo Kienzler
Romeo Kienzler

Keynote: Artificial Intelligence Methods for Time Series Forecasting and Classification of Real-Time IoT Sensor Data Streams, Romeo Kienzler, Chief Data Scientist - IBM Watson IoT WW, IBM Academy of Technology

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Why IoT (now) ? • 15 Billion connected devices in 2015 • 40 Billion connected devices in 2020 • World population 7.4 Billion in 2016
Machine Learning on historic data Source: deeplearning4j.org
Online Learning Source: deeplearning4j.org
online vs. historic • Pros • low storage costs • real-time model update • Cons • algorithm support • software support • no algorithmic improvement • compute power to be inline with data rate • Pros • all algorithms • abundance of software • model re-scoring / re- parameterisation (algorithmic improvement) • batch processing • Cons • high storage costs • batch model update
DeepLearning DeepLearning Apache Spark Hadoop
Neural Networks
Neural Networks
Deeper (more) Layers
Convolutional
Convolutional + =
Convolutional
Learning of a function A neural network can basically learn any mathematical function
Recurrent
LSTM “vanishing error problem” == influence of past inputs decay quickly over time
LSTM
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
•Outperformed traditional methods, such as •cumulative sum (CUSUM) •exponentially weighted moving average (EWMA) •Hidden Markov Models (HMM) •Learned what “Normal” is •Raised error if time series pattern haven't been seen before
Learning of an algorithm A LSTM network is touring complete
Problems • Neural Networks are computationally very complex •especially during training •but also during scoring CPU (2009) GPU (2016) IBM TrueNorth (2017)
IBM TrueNorth •Scalable •Parallel •Distributed •Fault Tolerant •No Clock ! :) •IBM Cluster • 4.096 chips • 4 billion neurons • 1 trillion synapses •Human Brain • 100 billion neurons • 100 trillion synapses •1.000.000 neurons •250.000.000 synapses
DeepLearning the future in cloud based analytics Storage Layer (OpenStack SWIFT / Hadoop HDFS / IBM GPFS) Execution Layer (Spark Executor, YARN, Platform Symphony) Hardware Layer (Bare Metal High Performance Cluster) GraphXStreaming SQL MLLib BlinkDB DeepLearning4J
 
 ND4J R MLBase H2O Y O U GPUAVX Intel Xeon E7-4850 v2 48 core, 3 TB RAM, 72 GB HDD, 10Gbps, NVIDIA TESLA M60 GPU (cu)BLAS jcuBLAS S T R E A M S
bit.ly/gpy16
•IBM Cloud Free Tier •http://ibm.biz/joinIBMCloud •IBM GeoSpatial Service •https://new-console.ng.bluemix.net/docs/services/ geospatial/index.html#geospatial •Google TPU •http://www.recode.net/2016/5/20/11719392/google-ai-ch tpu-questions-answers •IBM Neuromorphic Chip •http://www.research.ibm.com/articles/brain-chip.shtm •Recoding of the Talk •https://www.youtube.com/watch?v=h5_NH3sL0Qw •Contact Romeo Kienzler on Twitter: @romeokienzler

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