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Machine Learning and AI, by Helena Deus, PhD

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Dr. Helena Deus gave this presentation at the Women in Tech Summit Northeast April 14, 2018, in Philadelphia.

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Machine Learning and AI, by Helena Deus, PhD

  1. 1. Machine learning and AI Dr. Helena F. Deus Women in Tech Summit Philadelphia, April 2018 Photo by François-Dominique / CC BY-SA 4.0
  2. 2. | 2Elsevier Labs Machine learning is a field of computer science that gives computer systems the ability to "learn" with data, without being explicitly programmed. Deep Learning Machine Learning AI
  3. 3. | 3Elsevier Labs About Elsevier • 130 year old company, HQ in Amsterdam • 2500 scientific journals (e.g. Cell, Lancet) and 30 000 e- books (e.g. Gray’s Anatomy) • Today, a global information analytics business with a mission to 1) advance healthcare; 2) enable open science and 3) improve professional performance Only great science shall pass
  4. 4. | 4Elsevier Labs Gender distribution at Elsevier 35% 68% 54% 63% 31% 45% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Technology Non Technology All Gender distribution at Elsevier Female Male NA Elsevier's FTE gender distribution is: Female: 54% Male: 45% Tech industry average is 25% Elsevier has a unique market position with over 10% more women in tech roles than industry average. This can be used for recruitment purposes. For open positions: Patrick Irwin (p.irwin.1@elsevier.com), https://www.elsevier.com/about/careers/technology-careers
  5. 5. | 5Elsevier Labs About me • Data Scientist • BS in Biology, PhD in Bioinformatics • Deep learning user for a little over a year • Passionate about using AI for solving health care WHAT MY FRIENDS THINK I DO WHAT I REALLY DO
  6. 6. | 6Elsevier Labs Overview • History of AI and ML • Technical Deep Dive • AI applications • Concerns
  7. 7. | 7Elsevier Labs History and Hype
  8. 8. | 8 A brief history of machine learning and AI 1840:Comput ers can be programmed (Ada Lovelace) 1950: Turing test (Alan Turing) 1952: English-like programming languages (Grace Hopper) 1956: "Artificial intelligence" is coined (John McCarthy) 1957: First artificial neural network (Frank Rosenblatt) 1958: Logistic regression (David Cox) 1969: Apollo 11 - learn low and high priority tasks (Margaret Hamilton) 1970: “AI winter” caused by inflated hype
  9. 9. | 9 1982: Recurrent Neural Nets (John Hopfield) 1993: Modern Support Vector Machines (Corinna Cortes) 1999: Convolutional Neural Nets (Yann LeCun) 2006: ImageNet (Fei- Fei Li) 2011: IBW Watson beat humans in Jeopardy 2012: Coursera AI course (Daphne Kohler, Andrew Ng) 2014: Facebook publishes DeepFace 2016: Google's AlphaGo beats humans in Go A brief history of machine learning and AI
  10. 10. | 10Elsevier Labs Big “Structured” Data 2 billion: number of facebook users 82 million: amazon reviews 14 million: labelled ImageNet
  11. 11. | 11 (Slightly) Technical Deep Dive
  12. 12. | 12 How gradient descent works NEEDS IMPROVEMENT ACCEPTABLE IDEAL KEEP TRYING Cost or Loss Function: How far from reality is the prediction
  13. 13. | 13 Regression(s) If they visited 200 times, how much cash would they spend? Regression: pick the line that minimizes the distance between the points and the line http://scikit-learn.org/ http://colab.research.google.com/
  14. 14. | 14 Classification with Support Vector Machines New flower has [6.2, 2.9, 4.3, 1,3] – can you tell me the species?
  15. 15. | 15Elsevier Labs Neural networks are easy with linear algebra A B C D E A B C D E A B C D E Back Propagation! distance from target is 0.6 0.2 0.2 0.2 https://keras.io/
  16. 16. | 16Elsevier Labs The AI revolution
  17. 17. | 17Elsevier Labs Deep Learning - neural networks with a lot of layers https://www.cs.toronto.edu/~frossard/post/vgg16/ Convolutional Neural Networks (CNN) Generative Adversarial Networks (GAN) “car” https://towardsdatascience.com/gan-introduction-and-implementation-part1-implement-a- simple-gan-in-tf-for-mnist-handwritten-de00a759ae5c For you to Google: MNIST CNN Keras Good website: https://machinelearningmastery.com/
  18. 18. | 18Elsevier Labs https://www.nytimes.com/interactive/2018/01/02/technology/ai-generated-photos.html
  19. 19. | 19Elsevier Labs “AI won’t replace doctors. But doctors who use AI will replace doctors who don’t”
  20. 20. | 20Elsevier Labs AI and Transportation https://fossbytes.com/tesla-self-driving-car-video/
  21. 21. | 21Elsevier Labs How about text? Today, you should bring an umbrella
  22. 22. | 22Elsevier Labs Word Embeddings and Neural Networks I am having a lovely time here in Philadelphia positive negative 0.1 0.9 Word2Vec https://erikbern.com/2015/09/24/nearest-neighbor- methods-vector-models-part-1.html
  23. 23. | 23Elsevier Labs All mice were maintained in a temperature controlled (22 ± 2 °C) environment 12-h light 12-h dark photocycle and fed rodent chow meal . The mice were individually placed into an acrylic cylinder (25 cm height 10 cm diameter) containing 8 cm of water maintained at 22–24 °C Cold mice and Cancer Research Deus et al 2017, IEEE Training set: 480 sentences ; Train/Test split: 70/30; <1 min training time Matching phrases (eg mice .. kept): 24.6% False Discovery Rate Using Neural Networks: 4% False Discovery Rate
  24. 24. | 24Elsevier Labs
  25. 25. | 25Elsevier Labs Why you should be concerned about AI “Ill-conceived mathematical models now micromanage the economy, from advertising to prisons.”
  26. 26. | 26Elsevier Labs AI is only as good as the data used to train it
  27. 27. | 28Elsevier Labs Policy Math Visualization Development For open positions: Patrick Irwin (p.irwin.1@elsevier.com), https://www.elsevier.com/about/careers/technology-careers

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