CS 330 - Artificial Intelligence - Introduction II Instructor: - - PowerPoint PPT Presentation

CS 330 - Artificial Intelligence

• Introduction II

Instructor: Renzhi Cao Computer Science Department Pacific Lutheran University Fall 2017 1

Special appreciation to Ian Goodfellow, Joshua Bengio, Aaron Courville, Michael Nielsen, Andrew Ng, Katie Malone, Sebastian Thrun, Ethem Alpaydin, Christopher Bishop, Geoffrey Hinton.

Announcement

• 2. Think about teams
• 1. Gitlab
• 3. Read the book
• 4. Labs, and your computer availability

Introduction

3

When machine learning starts?

Inventors have long dreamed of creating machines that can learn. Desires date back to at least the time of ancient Greece. Inventors Pygmalion and statue he carved - Galatea Talos - a giant automaton made of bronze to protect Europa in Crete from pirates and invaders, by inventor Daedalus Inventor Hephaestus and the first human woman created by him - Pandora

Introduction

When machine learning starts?

People wonder whether such machines may become intelligent. Today, Artificial intelligence (AI) is a thriving field with many practical applications and active research topics.

Machine learning in early days

Used to solve problems that are intellectually difficult for human beings but relatively straightforward for computers, based on a list of formal and mathematical rules. The true challenge for machine learning is to solve tasks that are easy for people but difficult for machine to do. Recognizing spoken words, faces in images.

Machine learning in now days

Introduction

5

Why machine learning?

• My experience at Silicon Valley. (13 w)
• It’s everywhere. You may not want to know how to make a

car, but it’s always good if you know something about it.

• A dream that one day the machine is as intelligent as

human.

Introduction

6

Why machine learning?

More importantly, the traditional program can not solve some problems, such as recognizing a three-dimensional object from a novel view in new lighting conditions in a cluttered scene.

• What to write?
• Even you know what to

write, it’s too complicate.

Introduction

7

Why machine learning?

What rule to decide your credit card transaction is fraudulent?

• There may be simple rules, but we need to

combine a lot of weak rules

• Rules will be changed, so your method needs to

be updated all the time.

Introduction

8

Machine learning approach

Instead of writing program with all different rules, we collect examples that specify the correct output for a given input.

• The machine created program may look very different from typical
• program. It may contains millions of numbers
• If we do it right, the machine created program works well for new

cases, and also the ones we trained on it

• If the data changes, the program can also be changed by training on

new data

A machine learning algorithm takes the examples and produces program that does the job Massive amounts of computation are now cheaper than paying someone to write-specific program.

Spam Filtering Web Search Postal Mail Routing Fraud Detection

Movie Recommendations

Vehicle Driver Assistance Web Advertisements Social Networks Speech Recognition

Machine Learning in Our Daily Lives

Introduction

Google search demo

Introduction

Google translation demo

Introduction

12

Introduction

13

Introduction

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Self-driving car

Introduction

15

Amazon recommendation

Art

Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." arXiv preprint arXiv:1603.08155 (2016).

Art

Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." arXiv preprint arXiv:1603.08155 (2016).

Art

Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." arXiv preprint arXiv:1603.08155 (2016).

The Muse, Pablo Picasso, 1935

Computational biology

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Introduction

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Machine VS Human

• IBM deep blue chess-playing system

defeated world champion Garry Kasparov in 1997.

March 2016

AlphaGo 4 – Lee Sedol 1

https://www.britgo.org/intro/intro2.html

The rules

• Starts with an empty board.
• Players take turns to place one stone on vacant point
• Capture your opponent’s stones by completely surrounding them
• Goal: Use your stones to form territories by surrounding vacant

areas of the board

Baidu’s AI boss, Andrew Ng, pictured left with the host of ‘Super Brain’ and Baidu’s robot, Xiaodu

• Jan. 2017

https://www.theguardian.com/technology/2017/jan/30/libratus-poker-artificial-intelligence-professional-human-players-competition

• Jan. 2017

SWARM AI CORRECTLY PREDICTED THE OUTCOME OF SUPER BOWL LI, RIGHT DOWN TO THE FINAL SCORE

http://www.digitaltrends.com/cool-tech/swarm-artificial-intelligence-super-bowl-patriots/

The New England Patriots’ win over the Atlanta Falcons was nothing short of

• amazing. The Pats rallied back from a 25-point deficit to tie the game in the final

minutes of regulation and secured the win with a decisive touchdown drive in

• vertime.

Swarm AI (Combines swarming algorithms with human input) accurately predicted the outcome of the game, right down to the 34-28 win by the Patriots. February 6, 2017

Introduction

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Machine learning VS AI

• Interesting talk with students
• Artificial Intelligence is the broader concept of machines being

able to carry out tasks in a way that we would consider “smart”.

• Machine Learning is a current application of AI based around the idea

that we should really just be able to give machines access to data and let them learn for themselves.

http://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#2d597284687c

AI Machine learning Representation learning Deep learning Example: Knowledge bases Example: Logistic regression Example: Shallow autoencoders Example: MLPs

The MNIST Dataset

Goodfellow, 2016

Introduction

Historical Trends: Growing Datasets

1900 1950 1985 2000 2015 100 101 102 103 104 105 106 107 108 109 Dataset size (number examples) Iris MNIST Public SVHN ImageNet CIFAR-10 ImageNet10k ILSVRC 2014 Sports-1M Rotated T vs. C T vs. G vs. F Criminals Canadian Hansard WMT

Figure 1.8

Goodfellow, 2016

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Biological neural network size from Wikipedia (2015).

Introduction

Number of Neurons

Figure 1.11

Goodfellow, 2016

1950 1985 2000 2015 2056 10−2 10−1 100 101 102 103 104 105 106 107 108 109 1010 1011 Number of neurons (logarithmic scale) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Sponge Roundworm Leech Ant Bee Frog Octopus Human

Introduction

Solving Object Recognition

Figure 1.12

Goodfellow, 2016

2010 2011 2012 2013 2014 2015 0.00 0.05 0.10 0.15 0.20 0.25 0.30 ILSVRC classification error rate

Introduction

What is machine learning?

Introduction

Machine learning definition

Arthur Samuel (1959). Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.

Introduction

Machine learning definition

Tom Mitchell provides a more modern definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” Example: playing checkers. E = the experience of playing many games of checkers T = the task of playing checkers. P = the probability that the program will win the next game.

Introduction

Machine learning types

• Supervised learning
• Unsupervised learning
• Reinforcement Learning

Introduction

Supervised learning

• Learn to predict an output when given an input vector

UnSupervised learning

• Learn to select an action to minimize payoff

Reinforcement Learning

• Discover a good internal representation of the input

Introduction

Supervised learning

• Each training case consists of an input

vector x and a target output t.

• Regression: the target output is a real

number or a whole vector of real numbers.

• Classification: the output is a class label.

Introduction

Supervised learning

We start by choosing a model-class: y = f(x,W)

• A model-class f is a way of using some numerical parameters

W to map each input vector x into a predicted output y.

Learning usually means adjusting the parameters to reduce the discrepancy between the target output t on each training case and the actual output y, which produced by the model.

• For regression, we usually use the following as sensible measure of

discrepancy: (y-t)2/2.

• For classification, there are other measures that are generally more

sensible.

850

Supervised Learning

Given “Right answers”

Regression:

• utput is a continuous value

Classification

Output is discrete value (0

• r 1, or 2, and etc.)

2.5

Breast

Introduction

Unsupervised learning

For about 40 years, unsupervised learning was largely ignored by the machine learning community

• Some widely used definition of machine learning

actually excluded it

• Many researchers thought that clustering is the only

form of unsupervised learning. It’s hard to define what’s the goal of unsupervised learning.

x1 x2

Unsupervised Learning

No “Right answers”

https://news.google.com/

http://www.zlti.com/subdomains/analytics/Technology.html

https://bmcstructbiol.biomedcentral.com/articles/10.1186/1472-6807-14-13

Introduction

Reinforcement learning

In reinforcement learning, the output is an action or sequence of actions and the only supervisory signal is an

• ccasional scalar reward.
• The goal in selecting each section is to maximize the

expected sum of the future rewards.

• We usually use a discount factor for delayed rewards so

that we didn’t have to look too far into the future

Reinforcement learning is difficult:

• The rewards are typically delayed so it’s hard to know where we

went wrong (or right).

• A scalar reward does not supply much information.

Introduction

• Exercises
• Check course website

Introduction

• Proposing interesting machine learning topics
• Check practices in the website.