Latest advancements in AI based ML and DeepLearning on IoT devices
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Latest advancements in AI based ML and DeepLearning on IoT devices
- 1. Unless stated otherwise all images are taken from wikipedia.org or openclipart.org DeepLearning and Advanced Machine Learning on IoT @romeokienzler
- 2. • Intro (what I do, what we do, why we do IoT) • Latest advancements in AI based ML • Demo • Hands on
- 4. Why IoT (now) ? • 15 Billion connected devices in 2015 • 40 Billion connected devices in 2020 • World population 7.4 Billion in 2016
- 16. Machine Learning on historic data Source: deeplearning4j.org
- 18. 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
- 19. 1. API 2. pre-trained model 3. existing pipeline with your data 4. create own pipeline / model abstraction levels
- 21. IBM Watson Natural Language Classifier
- 24. Neural Networks
- 25. Neural Networks
- 27. AutoEncoders
- 28. AutoEncoders
- 29. Convolutional
- 31. Convolutional
- 33. Learning of a function A neural network can basically learn any mathematical function
- 34. Recurrent
- 35. LSTM “vanishing error problem” == influence of past inputs decay quickly over time
- 40. •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
- 43. Learning of an algorithm A LSTM network is touring complete
- 44. Problems • Neural Networks are computationally very complex •especially during training •but also during scoring CPU (2009) GPU (2016) IBM TrueNorth (2017)
- 45. 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
- 46. 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
- 50. bit.ly/toa16
- 53. •IBM Cloud Free Tier •http://ibm.biz/joinIBMCloud •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