Introduction to Artificial Intelligence

Introduction to Artificial Intelligence

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Ali Akbar S.

October 01, 2019
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Transcript

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    TABLE OF CONTENTS WHAT IS AI? Why do people talk

    about it? IN DEPTH What is deep learning? Why is it different from machine learning? 01 03 02 04 05 06 AI in INDONESIA Where are we now? ETHICAL AI Is AI innately unbiased? FUTURE of AI What’s next for AI? AI for EVERYONE How to prepare individuals and companies for AI era?
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    Alan Turing, Computing Machinery and Intelligence I propose to consider

    the question, “Can machines think?” This should begin with definitions of the meaning of the terms “machine” and “think.”
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    • Can machines be more intelligent than humans? • If

    so, what will happen to humanity? • How can we measure human intelligence? SOME PHILOSOPHICAL QUESTIONS
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    AI DESIGNS • Rational actions • Mathematical and empirical evaluation

    • Psychology, neuroscience • Optimization theory, statistics, game theory, etc.
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    IN DEPTH 02 What is deep learning? Why is it

    different from machine learning?
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    LEVELS OF INTELLIGENCE Simplest form of thought process Search problems,

    Markov decision processes, adversarial games Constraint satisfaction problems, Bayesian networks First-order logic, knowledge base
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    SUPERVISED LEARNING Even better, can we tell that there is

    a cat and a dog in the image? How to identify cats or dogs in an image?
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    BEFORE (CLASSICAL ML) IMAGE PROCESSING Edge detection, texture analyser, color

    histogram FEATURE EXTRACTION Eye position, eye colour, nose colour, fur type, leg counts MODEL Logit model, SVM, k-NN
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    y = σ(β 0 + β 1 x 1 +

    β 2 x 2 + β 3 x 3 ) Trying to find the optimum weights for each feature
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    AFTER (DEEP LEARNING) INPUT You only need raw images! FEATURE

    EXTRACTION + MODEL TRAINING Your model will do the feature extraction step for you OUTPUT Just define how many classes
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    “...a project to look for skin cancer in photographs. It

    turns out that dermatologists often put rulers in photos of skin cancer, for scale, but that the example photos of healthy skin do not contain rulers. To the system, the rulers (or rather, the pixels that we see as a ruler) were just differences between the example sets, and sometimes more prominent than the small blotches on the skin. So, the system that was built to detect skin cancer was, sometimes, detecting rulers instead.” (Evans, 2019) BIAS in AI
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    UNDERSTANDING CAUSALITY Confounders Graphical presentation of confounding in directed acyclic

    graphs (Suttorp et al., 2014) Age Chronic kidney disease Mortality
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    AI-Aided Prediction Deep Neural Networks Improve Radiologists’ Performance in Breast

    Cancer Screening (Wu et al., 2019) Human+AI AUC, Malignant Prediction
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    AI Transformation Execute pilot projects to gain momentum Customer segmentation,

    review categorization, photo classification 1st Build an in-house AI team Find people with specific skills related to ML 2nd Provide broad AI training Everyone should learn what AI is! Be it managers, team leaders, etc. 3rd Develop an AI strategy What are some vital things to enhance? 4th Develop internal & external communications Talk with board of directors, investors, your precious stakeholders 5th
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    WHAT ML CAN DO Anything you can do with 1

    second of thought, we can probably now or soon automate
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    WHAT ML CANNOT DO e.g. Market research and run an

    extended market report, give empathetic responses
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    WORKING with an AI TEAM 1 Define acceptance criteria Sometimes,

    you don’t really need 100% accuracy 3 Ensure clean data Big data without correct labels can mislead you! 2 Find sufficient data The bigger, the better
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    AI PITFALLS to AVOID Don’t think you need superstar AI

    engineers Don’t expect traditional planning w/o changes Don’t expect it to work the first time Don’t just hire 2/3 super ML engineers Don’t expect AI to solve everything
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    CREDITS: This presentation template was created by Slidesgo, including icons

    by Flaticon, and infographics & images by Freepik. Please keep this slide for attribution. Does anyone have any questions? pm@aliakbars.id @aliakbars uai.aliakbars.id THANKS