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Machine Learning: Understanding the Invisible Force Changing Our World
This document discusses the rise of machine learning and artificial intelligence. It provides quotes from industry leaders about the potential for AI to improve lives and build a better society. The text then explains what machine learning is, how it works through supervised, unsupervised and reinforcement learning, and some of the business applications of AI like product recommendations, fraud detection and machine translation. It also discusses the increasing investment in and priority placed on AI by companies, governments and researchers. The document encourages readers to consider the ethical implications of AI and ensure it is developed and applied in a way that benefits all of humanity.
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Machine Learning: Understanding the Invisible Force Changing Our World
- 1. Understanding the InvisibleForce Changing Our World Machine Learning KEN TABOR @KenTabor
- 2. “I think thatAI will lead to a low cost and better quality life for millions of people. Like electricity, it’s a possibility to build a wonderful society.” Andrew Ng
- 3. "We were earlyin machine learning and are already seeing significant dividends coming out. Many of the XXXXXXXX companies are already using this technology and are planning to use it even more." Larry Page
- 4. Machine learning drives …product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Jeff Bezos
- 5. XXX XXXXXXX laidout a development plan on Thursday to become the world leader in A.I. by 2030, aiming to surpass its rivals technologically and build a domestic industry worth almost $150 billion. State Council of China
- 6. “Artificial intelligence is thefuture, not only for XXXXXX, but for all humankind. … Whoever becomes the leader in this sphere will become the ruler of the world.” Vladimir Putin
- 7.
- 8. What it’s Not MLis something different.
- 9. It’s Not Big data “Copyyour stats into the lake tonight.”
- 10. It’s Not Data science “Anylocal users old enough to buy?” SELECT * from users WHERE age BETWEEN 35 AND 55 AND country = 'UK';
- 11. It’s Not A rulesengine if (currentProductId === A_BOOK_ID) { RenderBookProduct(SIMILAR_BOOKS); } “Show her related book titles.”
- 12. Machine Learning Algorithms (coderecipes) That can learn behavior Without writing more code
- 13. Machine Learning Through patternmatching By classifying things Offering % predictions
- 14. Using data tocreate knowledge
- 15. Data Structured Unstructured { "user": { "name":"Ken Tabor", "title": "Principal Software Architect", "company": "Sabre", "contact": { "email": "hello@kentabor.com", "phone": "8675309", "twitter": "@kentabor" }, "eatsChocolate": true } } Ʃ Ƭ ƫ
- 16. Learning how? 1. Supervisedlearning 2. Reinforcement learning 3. Unsupervised learningǠ
- 17. 1. Supervised You feedthe algorithm data You tag the data with labels Algorithm creates a model of understanding (learns)
- 18.
- 19. Can we trickour customers into helping us?
- 20. User clicks aretraining
- 21.
- 22. 2. Reinforcement You havea trained ML model You let it run Give it a reality-check function ǽ
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- 24.
- 25. 3. Unsupervised You feedthe algorithm data It discovers hidden patterns Clusters based on similarity of features Ǡ
- 27. Orange 10 Brown 4 Yellow9 Blue 12 Green 11 Red 8 Total 54
- 28. Orange 10/14 Brown 4/4 Yellow9/6 Blue 12/12 Green 11/8 Red 8/11 Total 54/55
- 29. Orange 10/14/15 Brown 4/4/1 Yellow9/6/9 Blue 12/12/8 Green 11/8/11 Red 8/11/10 Total 54/55/54
- 30. “I could dothat!” You don’t scale Humans are error prone People get bored
- 31. Questions How much doI regret eating all of the M&Ms? Do people consume brighter colors faster? Ship more brown because dye is cheaper? Production glitch - expected a repeatable amount? Why do the red ones taste better?
- 32.
- 33. Increase in computepower & data storage Ȑ your phone your laptop companies the cloud
- 34.
- 35. Why are theyjust giving it away for free?
- 36. Good will Show offorce Talent recruitment I’m guessing
- 37. Are they notafraid of my competition?
- 38.
- 39. Data Compute cycles Brain power Theyhave more The smart get smarter
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- 44. GM and Lyftannounced the joint venture on Monday, which also includes a $500 million investment by GM in Lyft. January 4, 2016
- 45. IBM will spend$240 million over 10 years to develop an artificial intelligence research lab with the Massachusetts Institute of Technology… September 6, 2017
- 46. Databricks, a bigdata analytics platform … announced that it has raised a $140 million Series D round… August 22, 2017
- 47.
- 48. Neural Machine Translation November15, 2016 English/French German Spanish Portuguese Chinese Japanese Korean Turkish Total of eight language pairs to/from English and French.
- 49. 2010 - 2015 Creditcard transactions +48% Debit card transactions +46% Electronic transactions +45% Annual transactions +34.2bill July 27, 2017
- 50. Quarterly Earnings Stories 5,300public companies 2013 - wrote 318 stories (6%) 65 (human) reporters 2015 - 3,700 stories (70%) AI system April 14, 2017
- 51.
- 52. …Google will nowshow you a silent six-second clip…decided to use some of its machine learning smarts to enable this feature… algorithm actually analyzes the whole video and then decides which six-second clip to pick. August 18, 2017
- 53. Assisting Pathologists inDetecting Cancer with Deep Learning…prediction heatmaps produced by the algorithm had improved so much that the localization score (FROC) for the algorithm reached 89%, which significantly exceeded the score of 73% for a pathologist with no time constraint. March 3, 2017
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- 58. “People like youalso shopped for…” Auto-generate Personas
- 59. Don’t give me200 results. Offer the 3 best choices. Personalized Data
- 60. Like a robotLike a person Humanize AI with Awesome UI “flat” “skeuomorphic”
- 61.
- 62.
- 63. “Knows machine learning” “Experiencedwith Adobe CS2 Suite” is the new
- 64. Find the data (representingusers) Machines lack empathy and ethical frameworks for passing humane judgements on humans.
- 65. Racist AI? Mindfully considertraining data Unconscious bias is tough Make diversity a priority Choose inclusion by default
- 66. Momma’s Gotta DieTonight Body Count 1992 See more:
- 67.
- 68. Past computer/human interface(CHI) Command Line Windows AR/VR 1D 2D 3D
- 69. Voice recognition Face recognition Touch-screensecondary API response refinement Future of CHI is 0D? Chatbot scripts
- 70. Trusting corporate MLalgorithms Representing your brand well
- 71. Explain nuances Surface assumptions Confirmsafe context “Why is your list different than mine?” Zero-D UX
- 72.
- 73. “If you feelsafe in the area you’re working in, you’re not working in the right area.” - David Bowie
- 74.
- 75. Invest another yearinto being a learning machine
- 76. Find a problem Discover data Code like acaveman Test solutions Daydream ☼
- 77. Share what Ilearn @KenTabor hello@kentabor.com www.KenTabor.com AvailableonAmazon
- 78.