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DeepLearning and Advanced Machine Learning on IoT@romeokienzler
• Intro (what I do, what we do, why we do IoT)
• Latest advancements in AI based ML
• Demo
• Hands on
Why IoT (now) ?• 15 Billion connected devices in 2015
• 40 Billion connected devices in 2020
• World population 7.4 Billion in 2016
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
1. API
2. pre-trained model
3. existing pipeline with your data
4. create own pipeline / model
abstraction levels
IBM Watson Personality Insights
IBM Watson Natural Language Classifier
DeepLearningDeepLearning
Apache Spark
Hadoop
Neural Networks
Neural Networks
Deeper (more) Layers
AutoEncoders
AutoEncoders
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
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
DeepLearningthe 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 BlinkDBDeepLearning4J ND4J
R MLBase H2OY 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
• IBM Cloud Free Tier• http://ibm.biz/joinIBMCloud
• Google TPU• http://www.recode.net/2016/5/20/11719392/google-ai-chip-tpu-questions-answers
• IBM Neuromorphic Chip• http://www.research.ibm.com/articles/brain-chip.shtml
• Recoding of the Talk• https://www.youtube.com/watch?v=h5_NH3sL0Qw
• Contact Romeo Kienzler on Twitter: @romeokienzler
