AI Software: Artificial Neural Networks Presentation

1. AI Software Artificial Neural Networks Presentation at AIBrain, Inc. Jeff Shomaker July 6, 2016

2. Introduction • Neural network software has been open-sourced so it can be usedwidely. • I’ll discuss/demonstrate the following: • Neural networks – what are they? • Uses of Neural Networks • TensorFlow • Torch • CNTK • Caffe

3. Neural Networks Neural networks are a paradigm for processing information loosely based on the idea of neurons that communicate information in the brain and spinal cord. 2) Source: 1) Raschka, S. (2016). What is the difference between deep learning and ‘Regular’ machine learning. www.kdnuggets.com, Diagram accessed 7-1-16. 2) Geoffrey Hinton, et al (2012). Neural networks for machine learning course. U of Toronto, Coursera.com, Oct 2012. Accessed 2013.

4. Examples of Neural Network (NN) Use • Medicine • Per IOM (Institute of Medicine, 2015) one of ten patient deaths in the US is due to misdiagnosis. • NNs can be used in diagnosis of multiple sclerosis, colon cancer, pancreatic disease, gynecological diseases, diabetes, coronary artery disease, breast/thyroid cancer and others. 1) • Finance • In 2014, card not present fraud was $2.9B in US – expected to be $6.4B by 2018. • NNs can be used for credit card fraud detection along with other machine learning approaches such as Support Vector Machines, K-nearest neighbor, etc. 2) • Network Security • The direct annual loss in 2011 from global cyber crime was $114B. • Authors propose a Artificial Immune System that uses neural networks as detectors. 3) • Energy Efficiency • During the next 10 years, electricity demand expected to grow by 13% to 15% per year. • Authors describe a system using neural networks that can communicate with electricity grids. • Expected to reduce energy loss from 16% to between 3% -- 5%. 4) • 1) Amato, F., et al (2013). Artificial neural networks in medical diagnosis. J Applied Biomedicine. 11:47-58. • 2) Deshpande, PM, et al., (2016 Jan). Applications of data mining techniques for fraud detection in credit-debit card transactions. ISJRD, Conference on Technological Advancement and Automatization in Engineering. 339-345. • 3) Komar, M., et al (2016). Intelligent cyber defense system. ICTERI, Kyiv, Ukraine, June 21-24 meeting, 534-549. • 4) Buyuk, OO, et al (2016). A novel application to increase energy efficiency using artificial neural networks. IEEE. 1-5.

5. TensorFlow • What is it: • Neural networks software for numerical computation - uses data flow graphs for computation • Developed at Google’s machine intelligence research organization • What can it be used for: • Any machine neural network problem • Video Demonstration • Six minute video introduction on TensorFlow on youtube. • Further information: • www.tensorflow.org • https://www.youtube.com/watch?v=bYeBL92v99Y

6. Torch • What is it: • Torch is a scientific computing framework for machine learning. • The goal is to be flexible and allow the building of scientific algorithms quickly - contains neural network and optimization libraries • What can it be used for: • Machine learning neural network problems • Video Demonstration • Three minute introduction on youtube. • Further information: • http://torch.ch/ • https://www.youtube.com/watch?v=uxja6iwOnc4&list=PLjJh1vlSEYgvGod9wWiydumYl8hOXixNu&index=19

7. CNTK • What is it: • CNTK stands for Computational Network Toolkit - created by Microsoft. • Designed for use with CPUs or GPUs (ie, graphical processing units) • What can it be used for: • Can be used for image classification problems, video analysis, speech recognition and natural language processing. • Video Demonstration • A two minute introduction on youtube. • Further information: • https://www.cntk.ai/ • https://www.youtube.com/watch?v=-mLdConF1EU

8. Caffee • What is it: • Caffee is a deep learning framework designed to be modular and fast – used with CPUs or GPUs. • Developed by Berkeley Vision and Learning Center (BLVC) and community contributors. • What can it be used for: • Originally developed for machine vision; but, now able to handle speech and text problems. • Video Demonstration • A three minute introduction on youtube. • Further information: • http://caffe.berkeleyvision.org/ • https://www.youtube.com/watch?v=bOIZ74rOik0

9. Further References What is a neural network – Episode 2 in Deep Learning Simplified, DeepLearning.TV, www.youtube.com. Zhang, Zhongheng (2016). A gentle introduction to artificial neural networks. Ann Translational Med. 1-5. Soniya, et al (2016). A review on advances in deep learning. IEEE, 1-6. Andrew Ng. Machine Learning Course, Stanford University, Coursera.com. https://www.coursera.org/learn/machine-learning Yaser Abu-Mostafa. Learning from Data: Introductory Machine Learning Course. CalTech. April 2012. Available on youtube. https://www.youtube.com/watch?v=mbyG85GZ0PI Geoffrey Hinton. Neural Networks for Machine Learning Course, University of Toronto, Coursera.com, October 2012. https://www.coursera.org/learn/neural-networks

10. Contacts • Jeff Shomaker– Founder/President 21 SP, Inc. • jshomaker@21spinc.com • http://www.21spinc.com • 21 SP, Inc. is a small privately held startup working in the area of genetic-based personalized medicine. The company's mission is to reduce the use of traditional trial-and-error medicine by using pharmacogenetics and other evidence-based data, such as the results of high quality clinical trials, to improve decision making in the medical clinic.