10 Things Every Entrepreneur Needs to Know About Artificial Intelligence
- 1. 10 THINGS EVERY ENTREPRENEUR NEEDS TO KNOW ABOUT ARTIFICIAL INTELLIGENCE
- 2. Lay a foundation for discussions around A.I. Hopefully, spark some ideas around how to apply it. And, build the case for starting now. TODAY’S GOALS
- 3. #1: What it is #2: Why you should care #3: What powers it #4: It’s here to stay #5: What's different now OUR JOURNEY #6: How to think about it #7: How it works #8: Different approaches #9: Where it’s used #10: How to get started
- 4. #1: WHAT IS A.I.?
- 5. “The science of making machines do those things that would be considered intelligent if they were done by people.” ~ Marvin Minsky, ‘Father of Artificial Intelligence’ A NEBULOUS CONCEPT
- 6. So let’s dig in a little deeper...
- 7. #2: WHAT POWERS IT?
- 8. “Field of study that gives computers the ability to learn without being explicitly programmed.” ~ Arthur Samuel, 1959 MACHINE LEARNING analyticsvidhya.com/blog/2015/07/difference-machine-learning-statistical-modeling
- 9. Training your computer to do stuff, just like you would train a pet. IN OTHER WORDS...
- 10. SIMILAR TO HOW WE LEARN Data System Output Model Question Answer Emotions Mindset Algorithm The reference data pattern (decision-making stuff) Process the computer uses to ‘learn’ the model The model is built from historical data Training data Life experience Perspective Algoritm
- 11. AT THE END OF THE DAY... Machine learning is all about pattern recognition.
- 12. AND... A.I. is all about applying and combining machine learning systems in creative and useful ways.
- 13. BUT STILL A MURKY LANDSCAPE Artificial Intelligence Machine Understanding (?) Pattern recognition Classification Prediction Can only do one thing Brute-force approach Autonomous decisions Universally applicable Intuition approach Google DeepMind Amazon Machine Learning Natural language processing Computer vision Optimization IBM Watson Classic learning Multi-tiered deep learning neural networks Deep learning neural network Explicit ProgrammingHandwritten Machine Learning logiccomplexity
- 14. #3: WHY SHOULD I CARE?
- 15. SOFTWARE IS EATING THE WORLD Everything is becoming code. Software automates, simplifies, and accelerates business.
- 16. BUT... Someone needs to write the code/logic.
- 17. THE TRADITIONAL WAY Handwritten logic. If / Then / Else
- 18. THE PROBLEM Complex situations require complex2 software logic.
- 19. THE SOLUTION Trained logic using historical data. Model tripID hasEggs eggsBough t milkBought 1 1 6 1 2 0 0 2 3 1 6 1 4 1 8 1 5 0 0 1 6 1 6 1 7 0 0 3 0011001101011101 0101100111010 11001001101 Stored as a mathematical model. Finds patterns in the data.
- 20. WHAT IT LOOKS LIKE console.aws.amazon.com/machinelearning/home?region=us-east-1#/datasources
- 21. A.I. IS EATING THE SOFTWARE All applications are becoming “smart” — with unprecedented complexity in logic. Just like software automates, simplifies, and accelerates business... Artificial Intelligence automates, simplifies, and accelerates software.
- 22. #4: IS IT HERE TO STAY?
- 23. As Everything is becoming software, it is fueled by... ● Limitless computing ● Limitless storage ● Limitless data (IoT = massive need) ● Deep learning ● Targeted machine learning SaaS (easy access) WHY NOW?
- 24. Massive strides in the past couple of years. Just in the past few months… ● Google open sources natural language processing platform ● Amazon open sources deep learning platform ● Google announces quantum computing works ● IBM offers access to quantum computer ● Google’s DeepMind beats Go champion WHAT’S NEW
- 25. WILL IT STICK THIS TIME? The Internet gave us big data (greater need). The cloud gave us massive computing (more horsepower). And it’s getting much, much bigger…
- 26. BIG DATAx
- 27. MASSIVE COMPUTINGx Google’s New Chip Is a Stepping Stone to Quantum Computing Supremacy The search giant plans to reach a milestone in computing history before the year is out. ~Hartman Nevet Head of Google’s Quantum AI Lab via: technologyreview.com via: researchgate.net
- 28. A.I. IS ON A PATH TO UBIQUITY “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” ~Mark Weiser Scientific American, 1991
- 29. IN JUST 4 YEARS Predicted for 2020... ● 13% of US households own consumer robots 1 (robotics) ● 30% of new cars will have a self-driving mode 2 (auto) ● 70% of mobile users access devices via biometrics 2 (security) ● We interact with 150+ smart devices (IoT) every day 2 (lifestyle) All are underpinned by A.I. 1 roboticstrends.com/article/13_of_us_households_to_own_consumer_robots_by_2020 2 weforum.org/agenda/2015/02/5-predictions-for-technology-in-2020
- 30. ADDING FUEL TO THE FIRE Think global. tractica.com/newsroom/press-releases/artificial-intelligence-for-enterprise-applications-to- reach-11-1-billion-in-market-value-by-2024
- 31. THE GOLDEN AGE OF AI We’ve hit the tipping point. Watching AI get smarter is like watching a bullet train. The moment you see it coming, it’s already blown past you.
- 32. #5: WHAT'S DIFFERENT NOW?
- 33. DRIVEN BY BUSINESS Since the 1950’s A.I. research has been driven by academia. Today, businesses are driving the research and breakthroughs. Which has helped flush out what works and what doesn’t...
- 34. #6: HOW SHOULD I THINK ABOUT IT?
- 35. MICRO A.I. Just like software development focuses on microservices, A.I. solutions should be focused on micro vs. monolithic. Academia was focused on... Business now focuses on... General A.I. Narrow A.I.
- 36. I.A. Use A.I. to build Intelligent Assistance products.
- 37. Artificial flowers have the same relationship to natural flowers as artificial intelligence has to natural intelligence. Useful but not the same.
- 38. #7: HOW DOES IT WORK?
- 39. IT’S ALL CLASSIFICATION via: wjscheirer.com
- 40. “Features” Points of differentiation within the data. How would you teach a child to recognize the differences? ● Distance between eyes ● Width of nose ● Shape of cheekbones ● etc. HOW DOES IT CLASSIFY?
- 41. “Probability” Each potential answer gets a numeric probability calculated for it. Higher probability means greater confidence. HOW DOES IT MAKE DECISIONS?
- 42. ● Supervised learning — Labeled training data ● Unsupervised learning — Unlabeled training data ● Transfer learning — Applying aspects across models ● Reinforcement learning — Reward-based training TRAINING gym.openai.com
- 43. #8: WHAT ARE THE DIFFERENT APPROACHES?
- 44. REMEMBER... Data System Output Model Question Answer Emotions Mindset Algorithm The reference data pattern (decision-making stuff) Process the computer uses to ‘learn’ the model The model is built from historical data Training data Life experience Perspective Algoritm
- 45. Who wants to be a data scientist? ENDLESS ALGORITHMS docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice machinelearningmastery.com/a-tour-of-machine-learning-algorithms
- 49. (SIMPLE) NEURAL NETWORK Each layer performs a discrete function ≥ 1 input neurons ≥ 1 output neurons ≥ 1 hidden layers Output “fires” if all weighted inputs sum to a set “threshold” Each connection applies a “weighted” influence on the receiving neuron Layers build on each other (iterative) Each input can be a separate “feature” Each neuron takes in multiple inputs Hidden layers can’t directly “see” or act on outside world cs231n.github.io/neural-networks-1
- 50. HOW MUCH IS A HOUSE WORTH? Decisions based on combinations. 3 bedrooms 37 years old 1450 ft2 $191,172 Is it “old” or “historic?” Is it “small” or “open floor plan?” $32,108 per bedroom $64,251 per acre Need a lower weight for “old” Apply initial abstractions Set values cs231n.github.io/neural-networks-1
- 51. #9: WHERE IS IT USED?
- 52. ENDLESS USES ● Classifying DNA sequences ● Economics ● Fraud detection ● Medical diagnosis ● Search engines ● Speech recognition ● Job search ● Spam filtering ● Risk prediction ● Visual product search ● Create art / music ● Industrial design ● Image caption generation ● Facial recognition ● Colorization of b&w images ● Adding sound to silent movies ● Language translation ● Image editing ● Vehicle navigation ● Error detection
- 53. IN THE WILD Recommender (pick from list) Classifier (binary) Visual recognition (deep learning)
- 54. A.I. will drive all of it.
- 55. #10: HOW DO I GET STARTED?
- 56. ● You don’t need a supercomputer ● You don’t need to write a ton of code ● You don’t need to invest massive amounts of time ● You don’t need a data science degree ● You don’t need to be a math whiz ● You don’t need mountains of data MYTH BUSTING
- 57. ● Amazon Artificial Intelligence ● Google Cloud Machine Learning ● Microsoft Cognitive Services ● IBM Watson * ● DiffBot * - PHP library is 3rd-party SaaS OPTIONS
- 58. ● TensorFlow * ● Amazon DSSTNE * ● H2O * ● PredictionIO ● Apache Mahout ● Scikit Learn ● Caffe * OPEN SOURCE OPTIONS ● Microsoft CNTK * ● Torch * ● Theano * ● MXnet * ● Chainer * ● Keras * ● Neon * * ANN / Deep learning
- 59. ● archive.ics.uci.edu/ml ● deeplearning.net/datasets ● mldata.org ● grouplens.org/datasets ● cs.toronto.edu/~kriz/cifar.html ● cs.cornell.edu/people/pabo/movie-review-data ● yann.lecun.com/exdb/mnist (handwriting) ● kdnuggets.com/datasets/index.html (long list) ● image-net.org (competition) OPEN SOURCE DATASETS
- 60. CLOSING
- 61. DON’T SWEAT THE MATH Forget theory, just do it.
- 62. Intro blog posts: ● Artificial Intelligence 101 (the big picture) ● Machine Learning 101 (what you’ll actually use) New ‘How to Apply A.I. in Your Business’ blog series: ● Voice-Powered Products w/ Amazon Alexa ● Predictive Social Media w/ IBM Watson(live) ● Image Recognition w/ Google Cloud ● Recommendation Engine w/ Microsoft Azure GO DEEPER
- 63. UNLEASH YOUR BUSINESS EMBRACE EXPONENTIAL 10xnation.com