UNDERSTANDING ARTIFICIAL INTELLIGENCE WORKSHOP
UNDERSTANDINGARTIFICIALINTELLIGENCE
WORKSHOP
INTRODUCTION: ARTIFICIAL INTELLIGENCE
WHAT IS AI AND WHY IT IS IMPORTANT TO LEARN ABOUT ITS APPLICATIONS
ARTIFICIAL INTELLIGENCEIS IT ANALYSIS?
ARTIFICIAL INTELLIGENCEIS IT ANALYSIS?
ARTIFICIAL INTELLIGENCEIS IT PREDICTION BASED ON ANALYSIS?
ARTIFICIAL INTELLIGENCEIS IT PREDICTION BASED ON ANALYSIS?
ARTIFICIAL INTELLIGENCELEARNINGREASONINGSELF-CORRECTION
ARTIFICIAL INTELLIGENCEINTELLIGENT SYSTEMS
WHAT IS DATA?DATA IS THE DIGITAL REPRESENTATION OF THE WORLD
WHAT IS DATA?THE DIGITAL REPRESENTATION OF THE WORLD
TEAM JEROME TEAM ANANDAN
WHAT IS AN ALGORITHM?AN ALGORITHM IS A PROCEDURE OR FORMULA FOR SOLVING A PROBLEM, BASED ON CONDUCTING A SEQUENCE OF SPECIFIED ACTIONS.
WHAT IS AN ALGORITHM?THINK OF IT AS FOLLOWING A RECIPE
WHAT IS MACHINE LEARNING?AN APPLICATION OF AI THAT PROVIDES SYSTEMS THE ABILITY TO AUTOMATICALLY LEARN AND IMPROVE FROM EXPERIENCE WITHOUT BEING EXPLICITLY PROGRAMMED.
MACHINE LEARNINGCAPABILITIES
MACHINE LEARNING IN USEEMAIL SPAM FILTERING
MACHINE LEARNING IN USEFRAUD DETECTION
MACHINE LEARNING IN USEFACIAL RECOGNITION
WHY USE MACHINE LEARNING?
Problems for which existing solutionsrequire a lot of hand-tuning or long lists of rules
Complex problems for which there is no good solution at all using a traditional approach
Fluctuating environments: a Machine Learning system can adapt to new data
Getting insights about complex problems and large amounts of data
TYPES OF MACHINE LEARNING SYSTEMSMACHINE LEARNING SYSTEMS CAN BE CLASSIFIED ACCORDING TO THE AMOUNT AND TYPE OF SUPERVISION THEY GET DURING TRAINING.
MACHINE LEARNING SYSTEMSFOUR MAJOR CATEGORIES
1. SUPERVISED LEARNING
MACHINE LEARNING SYSTEMS
In supervised learning, the training data you feed to the algorithm includes the desired solutions, called labels.
A typical supervised learning task is classification. The spam filter is a good example of this: it is trained with many example emails along with their class (spam or ham), and it must learn how to classify new emails.
SUPERVISED LEARNINGEXAMPLE: CLASSIFICATION
2. UNSUPERVISED LEARNING
MACHINE LEARNING SYSTEMS
In unsupervised learning, as you might guess, the training data is unlabeled. The system tries to learn without a teacher.
A typical supervised learning task is clustering. For example, say you have a lot of data about your blog’s visitors. You may want to run a clustering algorithm to try to detect groups of similar visitors. At no point do you tell the algorithm which group a visitor belongs to: it finds those connections without your help.
UNSUPERVISED LEARNINGEXAMPLE: CLUSTERING
3. SEMISUPERVISED LEARNING
MACHINE LEARNING SYSTEMS
Some algorithms can deal with partially labeled training data, usually a lot of unlabeled data and a little bit of labeled data. This is called semisupervised learning.
Some cloud hosting providers, such as Google Photos, are good examples of this. Once you upload all your family photos to the service, it automaticallyrecognizes that the same person A shows up in photos 1, 5, and 11, while another person B shows up in photos 2, 5, and 7.
SEMISUPERVISED LEARNINGEXAMPLE: CLUSTERING + CLASSIFICATION
4. REINFORCEMENT LEARNING
MACHINE LEARNING SYSTEMS
Reinforcement Learning is a little different. The learning system, called an agent, can observe the environment, select and perform actions, and get rewards in return (or penalties in the form of negative rewards). It must then learn by itself what is the best strategy, called a policy, to getthe most reward over time.
A policy defines what action the agent should choose when it is in a given situation.
REINFORCEMENT LEARNINGEXAMPLE: POSITIVE AND NEGATIVE REWARDS
MACHINE LEARNING: WHAT TO WATCH OUT FOR
In Machine Learning projects, we usually select a learning algorithm and train it on some data, so the things that can go wrong are related to bad algorithmsand bad data. However, data often matters more than algorithms for complex problems.
Let’s look at some examples of what we mean by bad data.
INSUFFICIENT DATANOT ENOUGH DATA
NON-REPRESENTATIVE DATADOES NOT REPRESENT ALL THE CASES
OVER-FITTING DATAFORCE-FITTING DATA TO MATCH THE USE CASE
UNDER-FITTING DATADATA IS TOO SIMPLE TO ACCOUNT FOR VARIANCE
POOR QUALITY DATADATA IS FULL OF ERRORS, OUTLIERS, AND NOISE
APPROACHINGPROBLEMS THROUGH AN AI LENS
7 QUESTIONSTO CONSIDER
ARTIFICIAL INTELLIGENCETECHNICAL LIMITATIONS
• Obtaining sufficiently large data sets
• The need to label training data
• Difficulty explaining results from large, complex neural-network-based systems
• Difficulties with domain adaptation and generalising
• Risk of discrimination and bias
• Privacy concerns
• Data quality, quantity, completeness
DISCRIMINATION AND BIAS IN AIEXAMPLE
DISCRIMINATION AND BIAS IN AIEXAMPLE
QUESTIONS?LET’S TALK ABOUT AI :)