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The good the bad and the ugly: Getting started doing AI

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With all the market interest in artificial intelligence, it’s no surprise that many are asking about the best way to learn more about it. What should I read? What should I watch? There’s so much material out there. But, before one can properly answer those types of questions, it’s useful to take a step back and consider what “doing AI” even means because it turns out that AI can mean a lot of different things depending upon what you’re trying to accomplish.

In this talk, Gordon Haff will provide you with both a high-level roadmap and specific pointers for adding AI smarts to your toolbox. He’ll distinguish between research AI and applied AI, discuss how AI intersects with data science more broadly, and look at some of related research and practice areas that will help you understand AI beyond just machine learning. Armed with this knowledge, you will be better prepared to chart out a program for learning AI that targets your specific needs and objectives rather than wasting time on topics that are not interesting or relevant to you.

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The good the bad and the ugly: Getting started doing AI

  1. 1. 1 The good the bad and the ugly: Getting started doing AI Gordon Haff Technology Evangelist @ghaff
  2. 2. 2 Who am I? ● Evangelist for emerging technologies and practices at Red Hat ● Author of How Open Source Ate Software, etc. ● Former IT industry analyst ● Former big system guy ● Website: http://www.bitmasons.com
  3. 3. 3 Is AI… Artificial General Intelligence (AGI) or “Strong AI”?
  4. 4. 4 Is AI… The stuff we haven’t figured out how to do yet?
  5. 5. An AI Map Research Applied Machine Learning Deep Learning Brain science & Cognitive psychology Linguistics & NLP Human/machine interactions Supervised learning Unsupervised learning Reinforcement learning Domain expertise Robotics Data anonymization Data science & statistics AI
  6. 6. 6 Research AI ● Math heavy (linear algebra, calculus, optimizations, probability) ● Essentially university curriculum ● Can touch many adjacent areas ● Not necessarily primarily programming/working with data
  7. 7. 7 Research AI resources ● Many MOOCs/university courses/text books ○ AI, Machine Learning, Deep Learning ○ Foundational courses such as linear algebra and calculus ○ Adjacent fields such as cognitive psychology and linguistics ● Other open educational resources (e.g. MIT OpenCourseWare) ● Research papers
  8. 8. 8 Applied AI ● Applications that solve today’s problems ● Background in relevant statistics and algorithms ● Programming ● Data science stuff (data cleansing, presentation, etc.) ● Primarily makes use of ML/DL
  9. 9. 9 Machine Learning Machine learning is a method of data analysis that automates analytical model building. It is based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. https://www.geeksforgeeks.org
  10. 10. 10 Deep Learning ● Sub-set of machine learning that uses multi-layer neural networks ● Has been the primary approach that has led to so many recent “AI” advances ● Beneficiary of increased computation/data, including accelerators such as GPUs
  11. 11. 11
  12. 12. 12 Reinforcement learning
  13. 13. 13 Some impressive results
  14. 14. 14 But reinforcement learning limits ● Learn from mistakes ● Physical world versus models ● Exploration versus exploitation ● Real-world environments change ● States can be poorly defined
  15. 15. 15 Source: MathWorks
  16. 16. 16 Unsupervised learning ● Clustering ● Reduce dimensionality
  17. 17. 17 Supervised learning https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d
  18. 18. 18 Amazing stuff since ~2010 ● Voice recognition: Siri, Alexa, Cortana, Google ● IBM Watson wins Jeopardy ● Computer vision classification can beat humans ● Autonomous driving research ● Ubiquitous bots ● Lots of unsexy predictive analytics, trading, optimization, and analysis
  19. 19. 19 Heathcare Example: ChRIS ● Real-time Web-based MRI Data Collection, Analysis, and Sharing ● Cloud-based platform developed as part of a collaborative effort between Boston Children’s Hospital, Red Hat, Boston University, and the Open Cloud (MOC) ● Began as a way to facilitate the organization, 3D visualization, and collaboration around medical imaging amongst researchers
  20. 20. 20 Supervised learning challenges NO PHYSICAL WORLD CONTEXT ● Lack of real world context ● Interpretability ● Dependent on large training sets ● Sensitive to small changes
  21. 21. 21 The basics ● Programming & programming environment ○ Programming for Everyone, UMich (Python)https://online.umich.edu/courses/programming-for-everybody-getting-started-with-python/ ○ Introduction to Computer Science and Programming using Python, MIT https://www.edx.org/course/introduction-to-computer-science-and-programming-using-python-2 (Text is Introduction to Computation and Programming using Python by John Guttag) ○ Anaconda distribution (Python/R/TensorFlow/data science libraries/Jupyter notebooks) ○ SQL https://www.khanacademy.org/computing/computer-programming/sql ○ Sabermetrics 101: Introduction to Baseball Analytics on edX is a fun and gentle introduction to data analysis
  22. 22. 22 Data Science: Working with data ● Python for Data Analysis, O’Reilly ● Kaggle ● MicroMasters in Statistics and Data Science, edX (MIT) https://www.edx.org/micromasters/mitx-statistics-and-data-science ● CS109 Data Science, Harvard http://cs109.github.io/2015/pages/videos.html http://blog.operasolutions.com/bid/384900/what-is-data-scienc
  23. 23. 23 Deep Learning ● Deep Learning by Ian Goodfellow et al. https://www.deeplearningbook.org/ ● Good list of deep learning resources https://blog.floydhub.com/ ● More practically-grounded courses (MOOC/YouTube/fast.ai), e.g. MIT 6.S094: Deep Learning for Self-Driving Cars
  24. 24. 24 “Democratized” AI ● Cloud AI/ML services like Google Cloud AutoML (and cloud generally) ● “Cookbooks,” e.g. O’Reilly Deep Learning Cookbook: Practical Recipes to Get Started Quickly ● Python libraries, Jupyter notebooks
  25. 25. 25 Keep your eye on ● Value/utility of data vs. privacy: MPC, homomorphic encryption, etc. ● Ownership of data ● Voice interfaces ● Explainability and bias ● Beyond current deep learning ● Multidisciplinary work
  26. 26. CONFIDENTIAL Designator linkedin.com/company/red-hat youtube.com/user/RedHatVideos facebook.com/redhatinc twitter.com/RedHat 26 Red Hat is the world’s leading provider of enterprise open source software solutions. Award-winning support, training, and consulting services make Red Hat a trusted adviser to the Fortune 500. Thank you

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