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Classification with Support Vector Machines
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Machine Learning and AI, by Helena Deus, PhD
The document discusses machine learning and artificial intelligence, highlighting its ability to learn from data without explicit programming. It provides a brief history of AI, technical insights into deep learning, and applications in various fields, especially healthcare. Elsevier's gender distribution shows a higher representation of women in tech roles compared to industry averages.
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Machine Learning and AI, by Helena Deus, PhD
- 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. | 2Elsevier Labs Machinelearning 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. | 3Elsevier Labs AboutElsevier • 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. | 4Elsevier Labs Genderdistribution 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. | 5Elsevier Labs Aboutme • 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. | 6Elsevier Labs Overview •History of AI and ML • Technical Deep Dive • AI applications • Concerns
- 7. | 7Elsevier Labs Historyand Hype
- 8. | 8 A briefhistory 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 1982: Recurrent Neural Nets (JohnHopfield) 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. | 10Elsevier Labs Big“Structured” Data 2 billion: number of facebook users 82 million: amazon reviews 14 million: labelled ImageNet
- 11. | 11 (Slightly) TechnicalDeep Dive
- 12. | 12 How gradientdescent works NEEDS IMPROVEMENT ACCEPTABLE IDEAL KEEP TRYING Cost or Loss Function: How far from reality is the prediction
- 13. | 13 Regression(s) If theyvisited 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 Classification withSupport Vector Machines New flower has [6.2, 2.9, 4.3, 1,3] – can you tell me the species?
- 15. | 15Elsevier Labs Neuralnetworks 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. | 16Elsevier Labs TheAI revolution
- 17. | 17Elsevier Labs DeepLearning - 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.
- 19. | 19Elsevier Labs “AIwon’t replace doctors. But doctors who use AI will replace doctors who don’t”
- 20. | 20Elsevier Labs AIand Transportation https://fossbytes.com/tesla-self-driving-car-video/
- 21. | 21Elsevier Labs Howabout text? Today, you should bring an umbrella
- 22. | 22Elsevier Labs WordEmbeddings 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. | 23Elsevier Labs Allmice 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.
- 25. | 25Elsevier Labs Whyyou should be concerned about AI “Ill-conceived mathematical models now micromanage the economy, from advertising to prisons.”
- 26. | 26Elsevier Labs AIis only as good as the data used to train it
- 27. | 28Elsevier Labs Policy Math Visualization Development Foropen positions: Patrick Irwin (p.irwin.1@elsevier.com), https://www.elsevier.com/about/careers/technology-careers