Love & Innovative technology presented by a technology pioneer and an AI expert Meetup 28.11.16

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This document discusses the rise of connected devices and machine learning. It notes that the number of connected devices is expected to grow from 15 billion in 2015 to 40 billion in 2020. It then covers various machine learning techniques including machine learning on historic data, online learning, neural networks, convolutional neural networks, recurrent neural networks, LSTM networks, and IBM's TrueNorth neural network chip. The document argues that neural networks can learn mathematical functions and algorithms and have outperformed traditional methods for problems like anomaly detection. However, neural networks are also computationally complex.

Technology

Unless stated otherwise all images are taken from wikipedia.org or openclipart.org

https://www.youtube.com/watch?v=W9BjRIddG6o

https://www.youtube.com/watch?v=W9BjRIddG6o
http://moenchberg-praxis.de/index.php?
tinymceimg=gehirn_neuronen.jpg

http://www.gridgit.com/postpic/2014/07/neuron-synapse-diagram_267957.png

https://ka-perseus-images.s3.amazonaws.com/c2792e65f78b25734f78a5f34cd296104a2e5d86.png

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

Learning of a function
A neural network can basically learn any
mathematical function

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

http://www.smithsonianmag.com/science-nature/the-truth-about-pheromones-100363955/

Love & Innovative technology presented by a technology pioneer and an AI expert Meetup 28.11.16

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1. Unless stated otherwise all images are taken from wikipedia.org or openclipart.org

3. https://www.youtube.com/watch?v=W9BjRIddG6o

4. https://www.youtube.com/watch?v=W9BjRIddG6o http://moenchberg-praxis.de/index.php? tinymceimg=gehirn_neuronen.jpg

5. http://www.gridgit.com/postpic/2014/07/neuron-synapse-diagram_267957.png

6. https://ka-perseus-images.s3.amazonaws.com/c2792e65f78b25734f78a5f34cd296104a2e5d86.png

10. Why IoT (now) ? • 15 Billion connected devices in 2015 • 40 Billion connected devices in 2020 • World population 7.4 Billion in 2016

11.

12.

13.

14.

15.

16. Machine Learning on historic data Source: deeplearning4j.org

17. Online Learning Source: deeplearning4j.org

18. Neural Networks

19. Neural Networks

20. Deeper (more) Layers

21. Convolutional

22. Convolutional + =

23. Convolutional

24.

25. Learning of a function A neural network can basically learn any mathematical function

26. Recurrent

27. LSTM “vanishing error problem” == inﬂuence of past inputs decay quickly over time

28. http://karpathy.github.io/2015/05/21/rnn-effectiveness/

29.

30.

31.

32. • 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

33.

34.

35. Learning of an algorithm A LSTM network is touring complete

36. Problems • Neural Networks are computationally very complex •especially during training •but also during scoring CPU (2009) GPU (2016) IBM TrueNorth (2017)

37. 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

38.

39.

40. Bake A Family P1 P2 match

41. http://www.smithsonianmag.com/science-nature/the-truth-about-pheromones-100363955/

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