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Machine Learning for Non-technical People


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Machine learning is one of the most promising and most difficult to understand fields of the modern age. Here are the slides from Slater Victoroff's (CEO of indico) talk at General Assembly Boston for non-technical folks on how to separate the signal from the noise -- stay tuned for the next time he speaks:

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Machine Learning for Non-technical People

  1. 1. Machine Learning for Non-technical People Slater Victoroff
  2. 2. Designed by YOU! The non-technical audience interested in Learning about Machine Learning! Who is this talk for?
  3. 3. Who am I? • Slater Victoroff • Olin College of Engineering • Typical young hoodie, flip-flop wearing entrepreneur • Someone who cares very deeply about machine learning • CEO of indico
  4. 4. What is Machine Learning?
  5. 5. Such a big buzzword. Here’s what it comes down to in a human definition: A class of computer algorithms and mathematical models that allow machines to perform general tasks, like identifying human faces in photos. The models are used to make predictions and decisions, which you can then use to solve real world problems, such as understanding how your customers feel about your brand across various social media channels. The neat thing is that instead of hiring 100 people to analyze 1,000 data points each, you could get a single machine to do it in a fraction of the time.
  6. 6. Quick Poll Can you use machine learning in the following industries?
  7. 7. Factories
  8. 8. Smart Phones
  9. 9. Robots
  10. 10. Human Robots
  12. 12. Machine Learning is Blurry
  13. 13. Language is blurry — sarcasm, etc. Where there’s a gray area, machine learning can solve the issue. Computers are bad at the world when there is inconsistency.
  14. 14. Say you’re a brand and you want to know what people are saying about your brand. You look through everyone talking about your brand on Twitter, Facebook, etc.. Now you want to look at how popular those people are to find your influencers. And finally, you want to know… what are they talking about? In the old spreadsheet way, we have always just ignored these problems as they were in a gray area we couldn’t access. A social media example
  15. 15. Machine learning is born in very ordinary circumstances
  16. 16. • Marty McFly ended up in 1955 which is the same year that the first branch of ML came out (AI movie to come later) • Georgetown and IBM Cold War found ML to be useful as they wanted to translate a large amount of Russian text to analyze • MIT went after the image side, teaching computers to recognize objects and scenes. They tried to teach the computer to look at a picture and determine a bird or a plant.
  17. 17. Machine Translation will be a Solved Problem in Three to Five Years - Optimistic Researcher 1954
  18. 18. CSAIL • The Computer Science and Artificial Intelligence Laboratory – known as CSAIL is the largest research laboratory at MIT and one of the world’s most important centers of information technology research. • Founded in the 1940’s by Marvin Minsky
  19. 19. We’re pretty sure we bit off more than we can chew here - ALPAC 1966
  20. 20. • Committees were spun up to precise translation and recognition. • In one solid decade, we effectively made no progress. We had one-off ML systems. • We could teach a computer to understand one sentence by showing it that one sentence. • We made no progress, spent a lot of money, and cut the research. It was the death of an era. During that time…
  21. 21. Time Passes
  22. 22. Arnold brings us back!
  23. 23. Machine Learning Goes Mainstream
  24. 24. Thumbs up? Sentiment classification using machine learning techniques. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan.
  25. 25. Sentiment analysis = determine if a piece of text is positive or negative. How do we do it? Well, we map each word to its sentiment and give the words a score. AKA: A Lexicon-based approach Sentiment Analysis
  26. 26. Word Positivity Great 0.9 Terrible 0.1 Alright 0.6 Mediocre 0.4
  27. 27. This sandwich isn’t bad
  28. 28. Words Positivity Isn’t bad 0.6 Isn’t good 0.3 Ain’t half-bad 0.73 Above average 0.7
  29. 29. “I have to say, that while most of my experiences at tourists traps have been horrendous, the one I recently went to broke the pattern.” • Many humans can’t figure out the sentiment of this sentence • Gray areas of language = why sentiment analysis is quite a difficult problem for computers to solve
  30. 30. How do we know how well we’re doing?
  31. 31. How do we know how good AI is?
  32. 32. • Well, it’s hard • Take a spreadsheet • Label each piece of text for positive vs. negative • Guess which words made it positive or negative • Train the model on half of the spreadsheet and then make predictions on the other half Then what.
  33. 33. Train Test
  34. 34. Still, it’s not that simple Performance metrics Overfitting
  35. 35. Customer Did they buy? 1 No 2 No 3 No 4 No 5 No 6 Yes 7 No 8 No 9 No 10 No 11 No 12 Yes 13 No 14 No Performance Metrics
  36. 36. - Accuracy isn’t necessarily the best performance metric - Predicting sentiment is a very different problem depending on whether the text you’re making predictions on consists of Amazon reviews, tweets, or medical journals - It also depends on how much data you’ve got - When you teach a computer what sentiment is, you end up showing it a huge number of examples. Depending on the data you’ve got, the number of examples you might use range from a few hundred to hundreds of millions - It’s not fair to use those examples to check your model’s accuracy — you already know the answers Performance Metrics
  37. 37. Learn more about sentiment analysis and performance metrics: What Even Is Sentiment Analysis?
  38. 38. Precision: fraction of retrieved instances that are relevant Recall: fraction of relevant instances that are retrieved Precision vs Recall
  39. 39. Overfitting This product left me with a deep feeling of regret. This film left me with a deep feeling of regret, love, and hopelessness for a life not lived. I #love these new @nike shoes
  40. 40. Overfitting • Overfitting means you “fail to generalise to examples outside of your training set” • In other words…you’re living under a rock. You’re great at recognizing everything under your rock, but you don’t understand the rest of the world • Domain is a factor — there are so many different kinds of text (scientific journal articles vs. tweets) • No one model is going to be the best at every kind of text
  41. 41. KNOWLEDGE = POWER Email us: