Introduction to Artificial Intelligence

Slide 1

Slide 1 text

Ali Akbar Septiandri Introduction to Artiﬁcial Intelligence

Slide 2

Slide 2 text

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?

Slide 3

Slide 3 text

WHAT IS AI? 01 Why do people talk about it?

Slide 4

Slide 4 text

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.”

Slide 5

Slide 5 text

● Can machines be more intelligent than humans? ● If so, what will happen to humanity? ● How can we measure human intelligence? SOME PHILOSOPHICAL QUESTIONS

Slide 6

Slide 6 text

Source: Digital Wellbeing

Slide 7

Slide 7 text

No content

Slide 8

Slide 8 text

Full Self-Driving Tesla

Slide 9

Slide 9 text

AI Adoption by McKinsey Global Institute (2017)

Slide 10

Slide 10 text

AI DESIGNS ● Rational actions ● Mathematical and empirical evaluation ● Psychology, neuroscience ● Optimization theory, statistics, game theory, etc.

Slide 11

Slide 11 text

Source: NVIDIA

Slide 12

Slide 12 text

Jake VanderPlas “Fundamentally, machine learning involves building mathematical models to help understand data.”

Slide 13

Slide 13 text

No content

Slide 14

Slide 14 text

WHY IS IT HARD?

Slide 15

Slide 15 text

WHY IS IT HARD?

Slide 16

Slide 16 text

WHAT IS IN THIS PICTURE?

Slide 17

Slide 17 text

WHAT IS IN THIS PICTURE?

Slide 18

Slide 18 text

ENTER: DEEP LEARNING This is the one responsible for the AI hype nowadays

Slide 19

Slide 19 text

A DEEP NEURAL NETWORK

Slide 20

Slide 20 text

No content

Slide 21

Slide 21 text

IN DEPTH 02 What is deep learning? Why is it different from machine learning?

Slide 22

Slide 22 text

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

Slide 23

Slide 23 text

MACHINE LEARNING TASKS UNSUPERVISED LEARNING SUPERVISED LEARNING REINFORCEMENT LEARNING

Slide 24

Slide 24 text

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?

Slide 25

Slide 25 text

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

Slide 26

Slide 26 text

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

Slide 27

Slide 27 text

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

Slide 28

Slide 28 text

...but we need millions of images!

Slide 29

Slide 29 text

images from 20k categories AlexNet achieved a top-5 error of 15.3% 14 mio

Slide 30

Slide 30 text

Convolutional Neural Networks

Slide 31

Slide 31 text

Generative Adversarial Networks (GANs)

Slide 32

Slide 32 text

Recurrent Neural Networks

Slide 33

Slide 33 text

UNSUPERVISED LEARNING Customer segmentation (clustering) Topic modelling Anomaly or outlier detection

Slide 34

Slide 34 text

UNSUPERVISED LEARNING Word Representations

Slide 35

Slide 35 text

REINFORCEMENT LEARNING Monte Carlo Tree Search for AlphaGo

Slide 36

Slide 36 text

Source: NVIDIA

Slide 37

Slide 37 text

AI in INDONESIA 03 Where are we now?

Slide 38

Slide 38 text

No content

Slide 39

Slide 39 text

No content

Slide 40

Slide 40 text

No content

Slide 41

Slide 41 text

No content

Slide 42

Slide 42 text

No content

Slide 43

Slide 43 text

ETHICAL AI 04 Is AI innately unbiased?

Slide 44

Slide 44 text

No content

Slide 45

Slide 45 text

No content

Slide 46

Slide 46 text

No content

Slide 47

Slide 47 text

No content

Slide 48

Slide 48 text

No content

Slide 49

Slide 49 text

No content

Slide 50

Slide 50 text

BIAS and FAIRNESS in AI

Slide 51

Slide 51 text

“...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

Slide 52

Slide 52 text

UNDERSTANDING CAUSALITY Confounders Graphical presentation of confounding in directed acyclic graphs (Suttorp et al., 2014) Age Chronic kidney disease Mortality

Slide 53

Slide 53 text

Dermatoscopic images See (Finlayson et al., 2019) ADVERSARIAL ATTACKS

Slide 54

Slide 54 text

FUTURE of AI 05 What’s next for AI?

Slide 55

Slide 55 text

Is it even possible? ARTIFICIAL GENERAL INTELLIGENCE

Slide 56

Slide 56 text

AlphaStar by DeepMind

Slide 57

Slide 57 text

Generating Images from Brain Signals

Slide 58

Slide 58 text

No content

Slide 59

Slide 59 text

AI-Aided Prediction Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening (Wu et al., 2019) Human+AI AUC, Malignant Prediction

Slide 60

Slide 60 text

AI for EVERYONE 06 How to prepare individuals and companies for AI era?

Slide 61

Slide 61 text

No content

Slide 62

Slide 62 text

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

Slide 63

Slide 63 text

WHAT ML CAN DO Anything you can do with 1 second of thought, we can probably now or soon automate

Slide 64

Slide 64 text

WHAT ML CANNOT DO e.g. Market research and run an extended market report, give empathetic responses

Slide 65

Slide 65 text

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

Slide 66

Slide 66 text

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

Slide 67

Slide 67 text

by Ainun Najib AI/ML for KIDS

Slide 68

Slide 68 text

No content

Slide 69

Slide 69 text

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? [email protected] @aliakbars uai.aliakbars.id THANKS