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Machine Learning and AI in Plain Language
- 1. NCTA Short Version: Machine Learning and Artificial Intelligence in Plain Language Romina Marazzato Sparano languagecompass
- 2. Romina Marazzato Sparano Languages
- 3. Today’s Goals Intelligence Artificial Intelligence Programming Human vs. Machine Learning Types of Deep Learning NLP & Machine Translation Examples & Applications © Romina Marazzato Sparano 2019
- 4. True Love: variation of Tolstoy’s “Anna Karenina” written in the style of Japanese author Haruki Murakami 4 . Who wrote this?
- 5. Intelligence is the ability to… • Learn get, keep, use knowledge & skills • Recognize problems that need solving • Solve problems applying knowledge creatively © Romina Marazzato Sparano 2019
- 6. Intelligence Today Classify Use logic on abstractions Consider the hypothetical © Romina Marazzato Sparano 2019
- 7. How do we measure Intelligence? • IQ Tests & the Bell Curve • Flynn Effect & Reversal © Romina Marazzato Sparano 2019
- 8. Multiple Intelligences? No scientific backing! Same G applied to multiple interests & fields Intelligence ╪ Learning Style © Romina Marazzato Sparano 2019
- 9. Language & Knowledge Awareness of Self & Other > Theory of Mind © Romina Marazzato Sparano 2019
- 10. Linguistic Understanding Double articulation: meaningful & meaningless units “bed” = & “bed” = /b/ + /e/ + /d/ Distinctive features Building blocks of speech sounds Voiced > b ð d g ʒ dʒ l m n ŋ r v z + vowels Voiceless > f k p s t θ ʃ tʃ Building blocks of writing: /l/ = l [el] but /l + ¯/ = t [tee] Arbitrariness bed / cama / lit / etc. © Romina Marazzato Sparano 2019
- 11. Use of Metaphor Conceptual metaphors Structure most basic understanding of experience Shape perceptions & actions without us noticing Metaphors We Live By - Lakoff & Johnson • Time is Money: spend/save time • Status has height: lofty/lowly position Teaching thinking by analogy • Explain how MRI works © Romina Marazzato Sparano 2019
- 12. Programming Encoding an algorithm Algorithm: sequence of instructions for solving a problem (performing a computation) Programming language: notation for code (╪ markup languages like HTML, XML, Markdown) Code: instructions that can be executed by a computer © Romina Marazzato Sparano 2019
- 13. Computer Talk Algorithms & Logic Flow Charts Formulas Algebra Calculus © Romina Marazzato Sparano 2019
- 14. Guessing Game in Python © Romina Marazzato Sparano 2019
- 15. Mix ingredients Spread in pan Bake at 350° Test with fork Remove from oven Let cool Not Ready Ready Cut & eat Coding for Chocolate Cake Variables: ¾ cup Chocolate 1 cup Butter 2 Eggs 2 tsp Vanilla 2 cups Sugar 1 ¾ cups Flour © Romina Marazzato Sparano 2019
- 16. Stir dry ingredients Add & mix wet ingr. 2 min Bake at 350° Test with Fork Remove from Oven Let Cool Not Ready Ready Cut & Eat Re-coding for Chocolate Cake © Romina Marazzato Sparano 2019 Preheat oven to 350° Grease & Flour Pan Pour evenly into pan
- 17. languagecompass 1950s 1980s 2010s Techniques to mimic human behavior ARTIFICIAL INTELLIGENCE Techniques to perform without being explicitly programmed MACHINE LEARNING Techniques for computation of multi-layer neural networks DEEP LEARNING © Romina Marazzato Sparano 2019
- 18. Machine Learning Programming computers so they can learn from data Increase P on T from E P: performance T: task E: experience Tom Mitchell, Carnegie Mellon University © Romina Marazzato Sparano 2019
- 19. Human Learning Increase P on T from E Acquire new skills & knowledge from E, vicarious E, & study Change behaviors, values, preferences, & perceptions of the world >>Transformative process © Romina Marazzato Sparano 2019
- 22. Artificial Neurons Functions or Algorithms • Inputs • Weights • Bias • Activation Function • Output
- 23. Artificial Neurons at Work • Inputs: Flour, Sugar, Eggs, etc. • Weight: 1 unit of each, 2 units, etc. • Bias: add x units of chocolate • Activation Function: Test w/ fork • Output: Remove from oven
- 24. Hidden Layers Inner layers b/ input & output Each artificial neuron: inputs > weights > bias > activation function > outpu Input Layer Hidden Layers Output Layer “Neurons” “Synapses” © Romina Marazzato Sparano 2019
- 25. Hidden Layers Flour Sugar Eggs © Romina Marazzato Sparano 2019 How much? Black Forest Chiffon Cake Flourless Cake
- 26. Types of Learning Supervised Unsupervised Discrete CLASSIFICATION CLUSTERING Continuous REGRESSION REDUCTION © Romina Marazzato Sparano 2019
- 27. Types of Learning: Supervised & Unsupervised © Romina Marazzato Sparano 2019
- 28. Supervised Learning: Cooking with a Chef © Romina Marazzato Sparano 2019 Unsupervised Learning: Cooking Blindfolded
- 29. Programming vs. Supervised & Unsupervised Learning © Romina Marazzato Sparano 2019
- 31. Supervised Learning © Romina Marazzato Sparano 2019 Neural Network > >
- 32. Unsupervised Learning © Romina Marazzato Sparano 2019 Neural Network > >
- 33. Classification Training 70,000 labeled images of hand-written digits Labeled Data --Categorical-- Neural Network > > Learned Patterns 0 1 2 3 4 5 6 7 8 9 © Romina Marazzato Sparano 2019
- 34. Novel Data Neural Network Output > > This is a 9 Classification Task: predict a label Which category does this observation belong to? 95% certainty © Romina Marazzato Sparano 2019
- 35. What does the system see? Does it see features?
- 36. What does the system see?
- 37. Fooling AI The system recognizes non-images as real objects 98% Guitar 99% GuitarSki Mask
- 38. Fooling AI Handwritten digits & non-images BOTH recognized as numbers 0 1 2 3 4 5 6 7 8 9
- 39. Fooling AI: The Risks Graffiti & stickers trick a neural network into “thinking” STOP sign is SPEED LIMIT 45 m/h sign!
- 40. Human Perspective Language complex system of communication & cognition Experience & Social Learning direct exposure + study + observation + vicarious reinforcement Gestalt whole/parts & foreground/background Knowledge: mental cognition Skills: physical application Attitudes: emotional value © Romina Marazzato Sparano 2019
- 41. Data + Human Input + Processing >> Output Data + Processing >> Output Machine Learning: Supervised & Unsupervised © Romina Marazzato Sparano 2019
- 42. Dimensionality Reduction The Curse of Dimensionality + dimensions + processing power Many algorithms don’t work Feature Selection: filtering irrelevant/redundant features Feature Extraction: creating a smaller set of new features Setting © Romina Marazzato Sparano 2019
- 43. Unsupervised Learning: the Caveat Needle in a haystack: Not everything but the kitchen sink © Romina Marazzato Sparano 2019
- 44. When AI creates recipes… Meat Chocolate Pie Cabbage Pot Cookies Artichoke Gelatin Dogs Crockpot Cold Water © Romina Marazzato Sparano 2019
- 45. Applications You edit the text, Descript edits the audio.
- 46. Finding an Answer I was born in Italy and, although I lived in Argentina and the US most of my life, I still speak fluent ________. Who is the Prime Minister of England? Amerigo Vespucci discovered America. True or False?
- 47. NLP: Natural Language Processing Parts of Speech Tagging Named Entity Recognition Parsing Trees / Dependency Parsing Term Frequency Inverse Document Frequency N-grams Alignment Word Math
- 48. Neural Networks for NLP Analogy Task Woman is to man like • Queen is to…. Einstein is to scientist like • Messi is to… • Mozart is to… • Frida Kahlo is to… Japan is to sushi like • Germany to… • France to… king midfielder violinist painter bratwurst tapas
- 49. Words as vectors
- 50. 600-word AI College Entrance Essay Discuss the rise and fall of the maritime trade in East and Southeast Asia in the 17th Century, taking into account the trade policies of East and Southeast Asian countries and the activities in the region of European powers.
- 51. How NLP Works Multiple Choice Amerigo Vespucci discovered America. True or False? Factoid/Jeopardy Style Question This person discovered America. Google, Wikipedia, Libraries
- 52. How AI Works Ranking ╪ False 0 1 2 3 4 5 6 7 8 9 10 Colombus Vespucci Clovis Pre-Clovis Confidence
- 53. How an AI creation disappears: > Princple of graceful degradation https://boingboing.net/2019/06/04/watch-an-ai-generated-human-fa.html
- 56. • Lift pump out from plastic tray. • Wipe all surfaces clean with a damp cloth.
- 57. THANK YOU!!! Follow me on @LanguageCompass Image Attributions • For-fee accounts at 123rf.com & Clipart.com • Collaboration with illustrators • Logos and Trademarks belong to their owners
Editor's Notes
- Comparing the potential for normal performance in terms of intelligence and the health literacy numbers shows us that we have room for improvement. As you may know, intelligence refers to the ability to classify the world, use logic on abstractions, and consider the hypothetical in order to solve problems. Intelligence, like any natural phenomena, follows a Gaussian distribution that takes the shape of a bell curve. The crown and shoulders of the curve represent most people’s intelligence and the tapering ends represent below and above normal intelligence. Even when we take into account the reversal of the Flynn, IQ scores still show room for improvement of health literacy. The Flynn Effect refers to the increase in IQ scores throughout the 20th century, while formal education, nutrition, spread of scientific thinking, and a modern life changed how we perceived the world. In the last 4 decades, developed countries have seen a plateauing and even a decrease in scores. Perhaps the return to a concrete, individualistic, and utilitarian mentality, has affected our ability to think logically.