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Artificial Intelligence and Machine Learning for Business Angels

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This slide deck was the base for an interactive talk on AI and ML for Business Angels so that they can make better investment decisions regarding startups in this space.

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Artificial Intelligence and Machine Learning for Business Angels

  1. 1. Artificial Intelligence & Machine Learning Henrico Dolfing BAS Academy 2019
  2. 2. About me > Dutch > Independent project recovery consultant > Selection committee Business Angels Switzerland > Background in: - Software Engineering - Machine Learning & Artificial Intelligence > www.henricodolfing.com > http://ch.linkedin.com/in/henricodolfing
  3. 3. So what is intelligence?
  4. 4. Brain and Neuropsychology (Brain Imaging) Computer Science (Artificial Intelligence) Differential Psychology (Construct and Structure of Intelligence) Psychometric (Measurement of Intelligence)
  5. 5. “Field of study that gives computers the ability to learn without being explicitly programmed”. Arthur Samuel, 1959
  6. 6. Artificial Intelligence Machine Learning Deep Learning Supervised Learning Unsupervised Learning Reinforcement Learning
  7. 7. “Difference between machine learning and AI: If it is written in Python, it's probably machine learning. If it is written in PowerPoint, it's probably AI” – Mat Velloso
  8. 8. Our challenge for today.
  9. 9. Predicting a good startup to invest in.
  10. 10. How would you describe a startup?
  11. 11. How would you describe good?
  12. 12. Where do you get that data?
  13. 13. How do you clean that data?
  14. 14. Distances between data points.
  15. 15. High dimensionality.
  16. 16. Artificial Intelligence Machine Learning Deep Learning Supervised Learning Unsupervised Learning Reinforcement Learning
  17. 17. Confusion Matrix
  18. 18. Artificial Intelligence Machine Learning Deep Learning Supervised Learning Unsupervised Learning Reinforcement Learning
  19. 19. Artificial Intelligence Machine Learning Deep Learning Supervised Learning Unsupervised Learning Reinforcement Learning
  20. 20. Artificial Intelligence Machine Learning Deep Learning Supervised Learning Unsupervised Learning Reinforcement Learning
  21. 21. In the end it is all about TRUST
  22. 22. State of the industry
  23. 23. ML/AI expertise is not the problem (Really! It's not.)
  24. 24. The importance and complexity of domain knowledge is highly underrated
  25. 25. Industry ML is not a Kaggle competition but a fit of Statistics/ML to an industry problem with a lot of boundaries (data, systems, organization)
  26. 26. Data that is not there cannot be magically conjured (There's less/worse data than one might think... always)
  27. 27. Specialized platforms might work nicely for a case but I'd have to have very good reasons to make our IT cloud infrastructure more complex for just one case
  28. 28. So ... wisdom of the day: Scalability and simplicity over niche solutions and (minor) improvements in model quality.
  29. 29. Questions to ask before investing > where do you get your training and test data from? > do you obtain it legally? > what is the quality of this data? > what is the recall of your classification/prediction? > what is the precision of your classification/prediction? > what is more important for your customers? recall OR precision > do you need to understand your model (legal & compliance) or not? (When yes, you cannot use neural networks)
  30. 30. Examples of AI use
  31. 31. Questions?

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