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What is artificial intelligence (IA) ?

The document is an extensive overview of artificial intelligence (AI), machine learning (ML), and deep learning, covering their definitions, history, types, and applications across various sectors. It discusses key concepts like supervised and unsupervised learning, the importance of data, challenges in AI adoption, and provides insights into trends and use cases for these technologies. Moreover, it highlights the implications these advancements have on business and society, alongside the challenges faced in implementation.

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WHAT IS ARTIFICIAL INTELLIGENCE? A I V S M A C H I N E L E A R N I N G V S D E E P L E A R N I N G Name : Oussama BELAKHDAR framed by : Prof. Maria Moise, PhD
01 Table of Contents Presentation Outline Introduction to AI Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Deep Learning AI vs Machine Learning vs Deep Learning MACHINE LEARNING PRESENTATION
What is AI ? Introduction to AI Timeline : History of AI Types of Artificial Intelligence Artificiel Intelligence Value Chain Elemnts Where is AI used ? AI use cases AI trend in 2020 Challenges in adoption of Artificial Intelligence 02 Introduction to Artificial Intelligence
What is Artificial Intelligence ? Artifcial Intelligence is a popular branch of computer science that concerns with building "intelligent" smart machines capable of performing intelligent tasks. Artificial Intelligence With rapid advancements in deep learning and machine learning, the tech industry is transforming radically.
Sense Comprehend Act Artificial Intelligence Introduction AI Technologies Illustrative Solutions Natural Language Processing Knowledge Representation Computer Vision Audio Processing Machine Learning Expert Systems Virtual Agents Identity Analytics Cognitive Robotics Speech Analytics Reommendation Systems Data Visualization
Introduction to AI Levels ? Artificial Narraw Intelligence Artificial General Intelligence Artificial Super Intelligence Types of Artigicial Intelligence
Artificial Narrow Intelligence Artificial General Intelligence VS Beat Go World Champions Read Facial Expressions Write Music
Artificial Intelligence Artificial Intelligence (AI) is a populare branch of compter science that concerns with building "intelligent" smart machines capable of performing intelligent tasks. With rapid advancements in deep learning and machine learning, the teck industry is transforming radically. Artificial Intelligence Transforming the Nature of Work, Learning, and Learning to Work Machine Learning Deep Learning
Artificial Intelligence Approaches Logic & Rules-Based Approach Machine Learning (Pattern Based Approach) Artificial Intelligence
TIMELINE The History of Artificial Intelligence TURING TEST THE TERM AI ELIZA AI WINTER DEEP BLUE SIRI WATSON TAY ALEXA ALPHAGO ETHIC GUIDELINES 1950 1955 1966 1970s-1908s 1997 2011 2011 2014 2016 2017 2018 The Turning Test is a method for determining the intelligence of machine The Term"Artificial Intelligence" is used for the first time Eiza, one of the first chatbots, simulates conversations as a psychotherapist After the boom of early esearch there was a pause Chess comptuer Deep Blue wins againt chess world champion Garri kadparov Siri, the intelligent speech assitant, is integratd into the iPhone 4s for the first time Supercompter Watson wins against two human rivals in the quiz show Jeopardy Virtual assistant Alexa from Amazon moves into countless homes Chatbot, Tay had to be reissued after a short time because of racist and sexist remarks Google's AlphaGO beats the world champion in the game Go The European Union establishes guidelines for dealing with ethics in AI
Types of Artificial Intelligence Artificial Intelligence Deep Learning Machine Learning
Survey on Adoption of Emerging Technologies
Artificial Intelligence Key Statistics of senior decision-makers agree that AI is fundamental to the success of their organisations strategy , those currently or planning to use AI technology anticipate a 39% boost to their organisation's revenue, on average organizations that have replaced, or plan to replace, roles with technology will retain or redeploy those who are displaced Two-third By 2022 7 in 10
Artificiel Intelligence Value Chain Elements
Artificial Intelligence Development Phases Phase 1 Phase 2 Phase 3
Reasons of using Artificial Intelligence Monitoring and alerts about health and the business, 6.6% Automation that eliminates manual and repetitive tasks, 7.6% All of hte above automated data-driven reporting,18% Automated Communications that give consumers data they can use to effective decisions, 12.8% Automated data- driven reporting, 5% Automated communications that give firms data they can use to make effective business decisions,45% Other , 5%
Where is AI used ? Customer Experience Supply Chain Human Resources Fraud Detection Knowledge Creation Research & Development Predictive Analytics Real-time Operations Management Customer Services Risk Management & Analytics Customer Insight Pricing & Promotion
Artificial Intelligence & Investment by Sector
Core Areas of Artificial Intelligence Explainable AI Research on new algorithms High precision learning from small data sets Sensory AI such as Internet of things Physical AI like Industrial Automation Cognitive AI such as worker training General AI
Artificial Intelligence in various Sectors Transport Health Water Technology Environment Traffic
ENGAGEMENT HR Chatbot Engagement Surveys Dynamic Career Sites Smart Sourcing AI Use Cases in Human Resource LEARNING Curated Training Skill Development RECRUITING ONBOARDING Automated Messages Curate Videos Employee Life Cycle
Potential Use Cases of AI in Healthcare AI & ROBOTICS Training Research End of Life Care Treatment Decision Making Keeping Well Early Detection Diagnosis
Bring all of the above together for healthcare use cases Social Platforms Smartphones AI Chatbots in Healthcare Search Engine Users Learn to search for information Like Facebook connect users online Bring the internet online Healthbots Self learning machines becomes smarter the more they are used Messenger Apps Lets users chat anywhere, anytime App Eco-system Lets users dowload and use apps easily Artificial Intelligence
AI in Supply Chain Digital Ecosystem Data Lake Secure Access Via Identity Management for Transient Users
Payment System Nonmonetary System Authorization System AI in Banking for Fraud Detection Neural Network Engine Scoring Engine Cardholder Profiles Postings Case Creation Module Case Management Database Expert Rules Base Expert Authorization Response Module Workstation Configuration Workstation Analyst
Aerospace and Flight Oprations Controlled by AI 10 AI Trend in 2020 AI In Retail AI Mediated Media and Entertainent Advanced Cybersecurity Automated Business Process AI-powered Chatbots AI will Come for B2B Data Modling will move to the Edge AI will make Healthcare more Accurate Robotic Process Automation
Challenges in adoption of Artificial Intelligence Lack of enabling data ecosystem Low Intensity of AI research Inadequate availability of expertise, technology & research High resource cost & low awareness for adopting AI in business process Unclear privacy, security and ethical regulations Unattrative intellectual Property regime to incentivize reseach & adoption of AI
What is Machine Learning? 7 Steps of Machine Lrearning Machine learning vs Traditional Programming How does machine learning work? Machine learning use cases How to choose Machine Learning Algorithm Why to use decision tree algorithm Learning Challenges and Limitations of Machine Learning Application of Machine Learning Why is machine learning important? Types of Machine Learning Machine Learning
Machine Learning Machine Learning is a type of AI that enables machines to learn from data and deliver predictive models The machine learning is not dependent on any explicit programming but the data fed into it. It is a complicated process. Based on the data you feed into machine learning algorithm and the training given to it, an output is delivered. A predictive algorithm create a predictive model.
Machine Learning is the result of General AI that involves developing machines that can deliver results bettr than humans Machine Learning Traditional Programming Machine Learning Data (Input) Data (Input) Program Output Output Program Input Data: Feed Learner Varios Data Output Data: Present Rules "Learning" Machine Learning System
Is a type of that enables machines to learn from data and deliver predictive models. The machine learning is not depedent on any explicit programming but the data fed into it. It is a complicated process. Based on the data you feed into machine leaning algorithm and the training given to it, an output is delivered. A predictive algoritthm will create a predictive model. Improves What is Machine Learning? Machine Learning Machine learning Predicts Introduction to Machine Learning Learns Ordinary System With Ai
Machine Learning Main Points Pattern Detection Self-Programming Leanring Data
INPUT DATA information (+ Answers) OUTPUT DATA optimum Modl Algorithms + Techniques MACHINE LEARNING Relationships Patterns Hidden Structures Dependencies
Raw & Training Data Selecting Right Algorithms Modelling Machine Learning Process Visualisation Data Gathering Data Cleaning Building Model & Finalising Data Transformation into Predictions Candidate & Final Prediction & Strategy Data
Learning Error Analysis Phase 2: Prediction Why is Machine Learning Important? Pre-processing Phase 1 : Learning Model New Data Prediction Predicted Data Normalisation Dimension Reduction Image Processing,etc. Supervised Unsupervised Minimization, etc. Precision/ recall Over fitting Test/cross Validation data, etc. Training Data
Application of Machine Learning Image Recognition Speech Recognition Traffic Prediction Product Recommendations Self Driving Cars Email Spam and Malware Filtering Virtual Personal Assistant Online Fraud Detection Stock Market Trading Medical DIagnosis Automatic Language Translation
MACHINE LEARNING USE CASES Manufacturing Financial Services Energy, Feedstock & Utilities Seismic data processing Retail Travel & Hospitality Healthcare & Life Sciences Smart grid Management Power usage analytics Alerts & diagnostics from real- time patient data Healthcare provider sentiment analysis Demand forecasting Predictive maintenance Predictive invetory planning Reommenda tion engines Dynamic pricing Aircraft Scheduling Credit worthiness evaluation Customer segmentation Traffic patterns Risk analytics & regulation Process optimization Customer Rol Proactive health management
Machine Learning Use Cases Energy Feedstock & Utilities Financial Services Travel & Hospitality Healthcare & Life Sciences Power Usage Analytics Seismic Data Processing Smart Grid Management Energy Demand Risk Analytics & Regulation Customer Segmentation Credit Worthiness Evaluation Aircraft Scheduling Dynamic Pricing Trafic Patteerns & Congestion Management Predictive Maintenance or Condition Monitoring Demand Forecasting Process Optimization Telematics Predictive Inventory Planning Recommendatio Engines Customer ROI & Lifetime Value Alerts & Diagnostics from Real-time Patient Data Predictive Health Management Heathcare Provider Manufacturing Retail
7 STEPS OF MACHINE LEARNING HYPERPARAMETER TUNING GATHERING DATA PREPARING THAT DATA CHOSING A MODEL PREDICTION EVALUATION TRAINING
How does Machine Learning Work ? Define Objectives Prepare Data Train model Integrate model Collect Data Select Algorithm Test Model Identifty, the problem to be solved and create a clear objective. Preparing data is a crucial step and involves building workflows to clean, match and blend the data Data is fed as input and the algorithm configured with the required parameters. A percent of th data can be utilized to train the model. Publish the prepared expriment as web sevice, so appliations can use the model. Collect data from hospitals, health insurance companies, social service agencies, police and fire dept. Depend on the problem to be solved and the type of data an appropriate algorithm will be chosen. The remaining data is utilized to test the model, for accuracy, Depending on the results, improvements, can be performed in the "Train model" and /or "Select algorithm" phases, iteratively.
Machine Learning Process STEP 1 STEP 2 STEP 3 STEP 4 STEP 5 Gathering data from various sources Cleaning data to have homogeneity Modeml Building Selecting the right ML algorithm Gaining insights from the model's results Data visualisation- Transforming results into visuals graphs
Additional Requierements How to Choose Machine learning Algorithm How to Select Machine Learning Algorithms Algorithm Cheat Sheet What do you want to do with your Data? Accuracy Training Time Linearity Number of Parameters Number of Features
Machine Learning vs Traditional Programming Computer Computer Data Handcrafted Model Result Sample Data Expected Result Model New Data Model Result Traditional modelling Machine Learning Predition Learning Predition Computer
Use c4.5 Implementation To Classify Why use Decision Tree Machine Learning Algorithm Decision Trees To Predict Responsible Variable has only 2 Categories Response Variable has Multiple Categories Use Standard Classification here Use c4.5 Implementation Responsible variable is Continuous Linear Relationship between Predictors & Response Non-linear Relationship between Predictors & Response Use Standard Regression Tree
Disadvantages Challenges and Limitations of Machine Learning Advantages Easily Identifies Trends and Patterns No Human Intervention Needed Handling multi-dimensional & multi-variety Data Interpretation Results Continuos Improvement Wide Applications High Error- Susceptibility Time and Resources Data Acquisition
Supervised Learning Unsupervised Learning Reinforcement Learning Machine Understands the data (identifies Patterns/ Structures) Evaluation is Qualitative or indirect Does not Predict/Find anything Specific An approach to AI Reward Based Learning Learning from +ve & +ve Reinforcement Machine Learns how to act in a Certain Environment To Maximise Rewards Training Rewards Types of Machine Learning Data with Clearly define Output is given Resolves Classification and Regression Problems Makes Machine Learn Explicity Direct feedback is given Predict outome future Inputs Inputs Inputs Outputs Outputs Outputs
Hidden Markov Model Machine Learning Algorithms Machine Learning Supervised Unsupervised Reinforcement Regression Clustering Decision Tree Random Forest Classification Association Anlaysis Linear Polynomial SVD PCA K-means KNN Trees Logistic Regression Naïve-Bayes Apriori FP-Growth Continuous Categorical
Machine Learning Unsupervised Learning Supervised Learning Clustering Dimensionality Reduction Classification Regression Reinforcement Learning Recommender Systems Targetted Marketing Customer Segmentation Real-tme decisions Robot Navigation Game AI Skill Acquisition Learning Tasks Big Data Visualisation Meaningful Compression Structure Discovery Feature Eliitation Image Classification Idenity Fraud Detection Customer Retention Diagnostics Population Growth Prediction Advertising Popularity Prediction Weather Forecasting Market Forecasting Estimating Life Expectancy
Types of Machine Learning What is Supervised Machine Learning? How Supervised Machine Works? Types of Supervised Machine Learning Algorithms Supervised vs Unspervised Machine Learning Techniques Advantages of Supervised Learning Disadvantags of Supervised Learning Supervised Machine Learning
Output Training Data set Desired output What is Supervised Machine Learning? Supervised Learning Imput Raw Data Supervisor
Types of Supervised Machine Learning Algorithms Supervised Learning Classification Fraud Detection Email Spam Detection Diagnostics Image Classification Regression Risk Assessment Score Prediction
How Supervised Machine Learning works Classification Sorting Items into Categories Regression Identifing Real Values (Dollars, Weight, etc.) Types of Problems to which it's Suited Feed the machine new, unlabeled information to see if it Tags New data appropriately. If not, Continue Refining the algorithm Provide the Machine Learning Algorithm Categorized or "labeled" Input and Output Data from to Learn
Advantages of Supervised Learning Advantages It allows to be very specific about the definition of the labels. In other words, you'll train the algorithm to differntiate different classes where you'll set a perfect decision boudary. You are ready to determine the amount of classes you would like the possess. The input file is extremely documented and is labeled The results produced by the supervised method are more accurat and reliable as compared to the results produced by the unsupervised techniques of machine learning, This is often mainly because the imput file within the supervised algorithm is documented an labeled. This is often a key difference between supervised and unspervised learning. The answers within the analysis and therefore the output of your algorithm are likely to be know thanks to that each one classes used are known
Disadvantages of Supervised Learning Supervised learning are often a posh method as compared with the unsupervised method. The key reason is that you simply need to understand alright and label the imput in supervised Learning. It is needed tons of computation time for training. It doesn't happen in real time while the unsupervised learning is about the important time. This is often also a serious difference between supervised and usupervised learning. Supervised machine learning uses of-line analysis. If you're got a dynamic big and growing data, you"re unsure of the labels to predefine the principles. This will be true challenge.
What is Unsupervised Learning? How Unsupervised Machine Learning Works Types of Unsupervised Learning Disadvantages of Unsupervised Learning Unsupervised Machine Learning
Algorithm Output What is Unsupervised Learning? Unsupervised Learning Input Raw Data Unkown output No Training Data Set Interpretation Processing
How Unsupervised Machine Learning Works Provide the machine learning algorith uncategorized, unlabeled input data to see what patterns it finds Observe and learn from the patterns the machine identifies Types of Problems to Which it's Suited Clustering Identifying similarities in groups For Example: Are there patterns in the data to indicate certain patients will respond better to this treatment than others? Anomaly Detection Identifying abnormalities in data For Example: Is a hacker intruding in our network?
Types of Unsupervised Learning Unsupervised Learning Dimensionality Reduction Text Mining Face Recognition Big Data Visualization Image Recognition Clustering Biology City Planning Targeted Marketing
You cannot get very specific about the definition of the info sorting and therefore the output. This is often because the info utilized in unsupervised learning is labeled and not know. It's employement of the machine to label and group the data before determing the hidden patterns. The results of the analysis can't be ascertained. There's no prior knowledge within the unsupervised method of machine learning. Additionally, the numbers of classes also are no know. It results in the lack deterine the results generated by the analysis. Less accuracy of the resuts. This is often also because the imput file isn't known and not labeled by peopme beforehand. which suggests that the machine will got to do that alone. Disadvantages of Unsupervised Learning
Clustering Associatio n Patterns / Structure Discovery Supervised vs Unsupervised Machine Learning Techniques Supervised Learning Unsupervised Learning Input & Output Data Classification Regression Predictions & Predictive Models Imput Data
What is reinforcement learning? How reinforcement learning works Types of reinforcement learning Advantages reinforcement learning Disadvantage of reinforcement learning Reinforcement Learning
Reinforcement Learning uses rewards and punishement to train computing models to perform a sequence of selections. Here computing faces a game-like scenario where it employs trial and error to answer, Based on the action it performs, computing gets either rewards or penalties. Its goal is to maximise the rewards. Reinforcement Learning Action State, Reward
What is Reinforcement Learning? Input Response Feedback Learns Input Reinforced Response It's a mango Wrong! It's an apple Noted It's an apple
Unsupervised Types of Deep Learning Netwoks Artificial Neural Networks (ANN) Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Self Organinizing Maps (SOM) Bolzmann Machines (BM) AutoEncoders (AE) Artificial Neural Networks Convolutional Neural Networks Recurrent Neural Networks Used for Regresion & Classification Used for Coputer Vision Used for Time Series Analysis Used for Feature Detection Used for Recommendation Systems Used for Recommendation Systems Self-Oragnizin Maps Deep Boltzmann Machines AutoEncoders Supervised Supervised Unsupervised Deep Learning Models
Inventory Management Types of Reinforcement Learning Gaming Finance Sector Manufacturing Robot Navigation
Environnement Agent Output Best Action Selection of Algotithm How Reinforcement Learning Works? Reinforcement Learning Imput Raw Data Reward State
Disadvantage of Reinforcement Learning You cannoy get very specific about the definition of the info sorting and therefore the output. this is often because the info utilized in unspervised learning is labeled and not know. it's employement of the machine to label and group the data before determing the hidden patterns. Less accuracy of the results. this is often also because the inpute file isn't know and not labeled by peole beforehand, which suggests that the machine will got to do that alone. The results of the analysis can't be ascertained. There's no prior knowledge within the unsupervided method of machine learning. Additionally, the numbers of classes also are not know. It results in the lack to determine the results genereted by the analysis Reinforcement learning as a framework is wrong in many various ways, but it's precisly this quality that creates it useful. Too much reinforcement learning can cause an overload of states which may diminish the results. Reinforcement learning isn't prefereable to use for solving simple problems. Reinforcement learning needs tons of knowledge and tons of compulation. it's data- hungry. that's why it works rather well in video games because one can play the sport again and again and again. so getting many data seems feasible.
What is Deep Learning? Deep Learning Process Classification of Neural Networks Types of Deep Learning Networks Reinfocement leanring Examples of deep leaning applications Why is Deep Learning Important ? Limitations of Deep Learning Deep Learning
Deep Learning Deep Learning is a sublifield of machine learning that is concerned with agorithms inspired by the brain's Structure functions known as artificial neural networks A computer model can be taught using Deep Learning to run classification actions using pictures, texts or sounds as input &
Feature Extraction + Classfication Output Deep Learning is a subfield of machine learning that is concerned with algorithms inspired by the brain's structure and functions know as artificial neural networks A computer model can be taught using Deep Learning to run classification actions using pictures, texts or sounds as inoput? What is Deep Learning ? What is Deep Learning? Car Not Car Input
Artificial Intelligence (AI) is a popular branch of computer science that concerns with building "intelligent" smart machines capable of performing intelligent tasks. With rapid advancement in deep Learning and machine learning, the tech industry is transforming radically. What is Deep Learning ?
Understand the Problem Deep Learning Process Identify Data Select Deep Learning Algorithm Training the Model Test the Model
Why is Deep Learning Important ? Deep Learning Other Learning Algorithms
Deep Learning Process Identify Data Select Deep Learning ALgorithms Training the model Test the Model Understand the Problem
Examples of Deep Learning Applications Applications of Deep Learning Drug Discovery & Better Diagnostics of Diseases in Healthcare Image Recognition Natural Language Processing Robots and Sell Driving Cars Speech Recognition Portfolio Management & Prediction of stock Price Movements
Limitations of Deep Learning Amount of Data Interoretability Statistical Reasoning Limitations of Deep Learning
Difference between Machine Learning and Deep Learning Which is better to start Artificiel Intelligence , Machine Learning or Deep Learning ? Difference between Artificial Intelligence vs Machine Learning vs Deep Learning
Artificial Intelligence originated around 1950s Machine Learning is the practice of getting machines to make decisions without being programmed AI represents simulate intelligence in machines AI is a subset of data science Deep Learning is a subset of Machine Learning Ai & Data Science AI VS Machine Learning VS Deep Learning Aim is to build machines which are capable of thinking like humans Machine Learning originated around 1960s Machine Learning is a subset of AI & Data Science Aim is to make machines learn through data so that they can solve problems Deep learning originated around 1970s Deep Learning is the process of using artificial neural networks to solve complex problems Aim is to build neural network that automatically discover patterns for feature detection Artificial Intelligence Machine Learning Deep Learning
Difference between Machine learning and deep learning Feature Extraction Feature Extraction+Classification Classification Imput Output Imput Output Machine Learning Deep Learning Car Car Not Car Not Car
Difference between Artificial Intelligence vs Machine Learning vs Deep Learning Machine Learning Deep Learning Artificial Intelligence Engineering of making intelligent machines and programs Ability to learn without being explicity programmed Learning based on deep neural network
Which is better to start Artificiel Intelligence, Machine Learning or Deep Learning? Subset of AI Techniques which use statistical Methods to Enable Machines to improve with Experiences. Subset of Machine Learning which make the Computation of Multi-layer Neural Networks Feasible. Any technique which enables computers to mimic human behavior Artificial Intellignce Machine Learing Deep Learning
Machine learning algorithms learn from data to find hidden relations, tomake predictions, to interact with the world, … A machine learning algorithm is as good as its input data • Good model + Bad data = Bad Results Deep learning is making significant breakthroughs in: speech recognition,language processing, computer vision, control systems, … If you are not using or considering using Deep Learning to understand or solve vision problems, you almost certainly should be Conclusion
Bibliography Books 1. Introduction to Machine LearningThe Wikipedia Guide Machine Learning for dummies A Willey Brand MACHINE LEARNING Tom M Mitchell Understanding Machine Learning: From Theory to Algorithmsc 2014 by Shai Shalev-Shwartz and Shai Ben-David 2.Journals Journal of Machine Learning Research www.jmlr.org Machine Learning IEEE Transactions on Neural Networks IEEE Transactions on Pattern Analysis and Machine Intelligence Annals of Statistics Journal of the American Statistical Association International Conference on Machine Learning (ICML) European Conference on Machine Learning (ECML) Neural Information Processing Systems (NIPS) Computational Learning International Joint Conference on Artificial Intelligence (IJCAI) ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) IEEE Int. Conf. on Data Mining (ICDM) 3.Conferences
thank you for your time

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What is artificial intelligence (IA) ?

  • 1.
    WHAT IS ARTIFICIAL INTELLIGENCE? AI V S M A C H I N E L E A R N I N G V S D E E P L E A R N I N G Name : Oussama BELAKHDAR framed by : Prof. Maria Moise, PhD
  • 2.
    01 Table of Contents Presentation Outline Introductionto AI Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Deep Learning AI vs Machine Learning vs Deep Learning MACHINE LEARNING PRESENTATION
  • 3.
    What is AI? Introduction to AI Timeline : History of AI Types of Artificial Intelligence Artificiel Intelligence Value Chain Elemnts Where is AI used ? AI use cases AI trend in 2020 Challenges in adoption of Artificial Intelligence 02 Introduction to Artificial Intelligence
  • 4.
    What is ArtificialIntelligence ? Artifcial Intelligence is a popular branch of computer science that concerns with building "intelligent" smart machines capable of performing intelligent tasks. Artificial Intelligence With rapid advancements in deep learning and machine learning, the tech industry is transforming radically.
  • 5.
    Sense Comprehend Act ArtificialIntelligence Introduction AI Technologies Illustrative Solutions Natural Language Processing Knowledge Representation Computer Vision Audio Processing Machine Learning Expert Systems Virtual Agents Identity Analytics Cognitive Robotics Speech Analytics Reommendation Systems Data Visualization
  • 6.
    Introduction to AILevels ? Artificial Narraw Intelligence Artificial General Intelligence Artificial Super Intelligence Types of Artigicial Intelligence
  • 7.
    Artificial Narrow Intelligence Artificial General Intelligence VS BeatGo World Champions Read Facial Expressions Write Music
  • 8.
    Artificial Intelligence Artificial Intelligence(AI) is a populare branch of compter science that concerns with building "intelligent" smart machines capable of performing intelligent tasks. With rapid advancements in deep learning and machine learning, the teck industry is transforming radically. Artificial Intelligence Transforming the Nature of Work, Learning, and Learning to Work Machine Learning Deep Learning
  • 9.
    Artificial Intelligence Approaches Logic& Rules-Based Approach Machine Learning (Pattern Based Approach) Artificial Intelligence
  • 10.
    TIMELINE The History ofArtificial Intelligence TURING TEST THE TERM AI ELIZA AI WINTER DEEP BLUE SIRI WATSON TAY ALEXA ALPHAGO ETHIC GUIDELINES 1950 1955 1966 1970s-1908s 1997 2011 2011 2014 2016 2017 2018 The Turning Test is a method for determining the intelligence of machine The Term"Artificial Intelligence" is used for the first time Eiza, one of the first chatbots, simulates conversations as a psychotherapist After the boom of early esearch there was a pause Chess comptuer Deep Blue wins againt chess world champion Garri kadparov Siri, the intelligent speech assitant, is integratd into the iPhone 4s for the first time Supercompter Watson wins against two human rivals in the quiz show Jeopardy Virtual assistant Alexa from Amazon moves into countless homes Chatbot, Tay had to be reissued after a short time because of racist and sexist remarks Google's AlphaGO beats the world champion in the game Go The European Union establishes guidelines for dealing with ethics in AI
  • 11.
    Types of ArtificialIntelligence Artificial Intelligence Deep Learning Machine Learning
  • 13.
    Survey on Adoptionof Emerging Technologies
  • 14.
    Artificial Intelligence KeyStatistics of senior decision-makers agree that AI is fundamental to the success of their organisations strategy , those currently or planning to use AI technology anticipate a 39% boost to their organisation's revenue, on average organizations that have replaced, or plan to replace, roles with technology will retain or redeploy those who are displaced Two-third By 2022 7 in 10
  • 15.
  • 16.
    Artificial Intelligence DevelopmentPhases Phase 1 Phase 2 Phase 3
  • 17.
    Reasons of usingArtificial Intelligence Monitoring and alerts about health and the business, 6.6% Automation that eliminates manual and repetitive tasks, 7.6% All of hte above automated data-driven reporting,18% Automated Communications that give consumers data they can use to effective decisions, 12.8% Automated data- driven reporting, 5% Automated communications that give firms data they can use to make effective business decisions,45% Other , 5%
  • 18.
    Where is AIused ? Customer Experience Supply Chain Human Resources Fraud Detection Knowledge Creation Research & Development Predictive Analytics Real-time Operations Management Customer Services Risk Management & Analytics Customer Insight Pricing & Promotion
  • 19.
    Artificial Intelligence &Investment by Sector
  • 20.
    Core Areas ofArtificial Intelligence Explainable AI Research on new algorithms High precision learning from small data sets Sensory AI such as Internet of things Physical AI like Industrial Automation Cognitive AI such as worker training General AI
  • 21.
    Artificial Intelligence invarious Sectors Transport Health Water Technology Environment Traffic
  • 22.
    ENGAGEMENT HR Chatbot Engagement Surveys Dynamic CareerSites Smart Sourcing AI Use Cases in Human Resource LEARNING Curated Training Skill Development RECRUITING ONBOARDING Automated Messages Curate Videos Employee Life Cycle
  • 23.
    Potential Use Casesof AI in Healthcare AI & ROBOTICS Training Research End of Life Care Treatment Decision Making Keeping Well Early Detection Diagnosis
  • 24.
    Bring all ofthe above together for healthcare use cases Social Platforms Smartphones AI Chatbots in Healthcare Search Engine Users Learn to search for information Like Facebook connect users online Bring the internet online Healthbots Self learning machines becomes smarter the more they are used Messenger Apps Lets users chat anywhere, anytime App Eco-system Lets users dowload and use apps easily Artificial Intelligence
  • 25.
    AI in SupplyChain Digital Ecosystem Data Lake Secure Access Via Identity Management for Transient Users
  • 26.
    Payment System Nonmonetary System AuthorizationSystem AI in Banking for Fraud Detection Neural Network Engine Scoring Engine Cardholder Profiles Postings Case Creation Module Case Management Database Expert Rules Base Expert Authorization Response Module Workstation Configuration Workstation Analyst
  • 27.
    Aerospace and FlightOprations Controlled by AI 10 AI Trend in 2020 AI In Retail AI Mediated Media and Entertainent Advanced Cybersecurity Automated Business Process AI-powered Chatbots AI will Come for B2B Data Modling will move to the Edge AI will make Healthcare more Accurate Robotic Process Automation
  • 28.
    Challenges in adoptionof Artificial Intelligence Lack of enabling data ecosystem Low Intensity of AI research Inadequate availability of expertise, technology & research High resource cost & low awareness for adopting AI in business process Unclear privacy, security and ethical regulations Unattrative intellectual Property regime to incentivize reseach & adoption of AI
  • 29.
    What is MachineLearning? 7 Steps of Machine Lrearning Machine learning vs Traditional Programming How does machine learning work? Machine learning use cases How to choose Machine Learning Algorithm Why to use decision tree algorithm Learning Challenges and Limitations of Machine Learning Application of Machine Learning Why is machine learning important? Types of Machine Learning Machine Learning
  • 30.
    Machine Learning Machine Learningis a type of AI that enables machines to learn from data and deliver predictive models The machine learning is not dependent on any explicit programming but the data fed into it. It is a complicated process. Based on the data you feed into machine learning algorithm and the training given to it, an output is delivered. A predictive algorithm create a predictive model.
  • 31.
    Machine Learning isthe result of General AI that involves developing machines that can deliver results bettr than humans Machine Learning Traditional Programming Machine Learning Data (Input) Data (Input) Program Output Output Program Input Data: Feed Learner Varios Data Output Data: Present Rules "Learning" Machine Learning System
  • 32.
    Is a typeof that enables machines to learn from data and deliver predictive models. The machine learning is not depedent on any explicit programming but the data fed into it. It is a complicated process. Based on the data you feed into machine leaning algorithm and the training given to it, an output is delivered. A predictive algoritthm will create a predictive model. Improves What is Machine Learning? Machine Learning Machine learning Predicts Introduction to Machine Learning Learns Ordinary System With Ai
  • 33.
    Machine Learning MainPoints Pattern Detection Self-Programming Leanring Data
  • 34.
    INPUT DATA information (+Answers) OUTPUT DATA optimum Modl Algorithms + Techniques MACHINE LEARNING Relationships Patterns Hidden Structures Dependencies
  • 35.
    Raw & TrainingData Selecting Right Algorithms Modelling Machine Learning Process Visualisation Data Gathering Data Cleaning Building Model & Finalising Data Transformation into Predictions Candidate & Final Prediction & Strategy Data
  • 36.
    Learning Error Analysis Phase2: Prediction Why is Machine Learning Important? Pre-processing Phase 1 : Learning Model New Data Prediction Predicted Data Normalisation Dimension Reduction Image Processing,etc. Supervised Unsupervised Minimization, etc. Precision/ recall Over fitting Test/cross Validation data, etc. Training Data
  • 37.
    Application of MachineLearning Image Recognition Speech Recognition Traffic Prediction Product Recommendations Self Driving Cars Email Spam and Malware Filtering Virtual Personal Assistant Online Fraud Detection Stock Market Trading Medical DIagnosis Automatic Language Translation
  • 38.
    MACHINE LEARNING USE CASES Manufacturing Financial Services Energy, Feedstock & Utilities Seismicdata processing Retail Travel & Hospitality Healthcare & Life Sciences Smart grid Management Power usage analytics Alerts & diagnostics from real- time patient data Healthcare provider sentiment analysis Demand forecasting Predictive maintenance Predictive invetory planning Reommenda tion engines Dynamic pricing Aircraft Scheduling Credit worthiness evaluation Customer segmentation Traffic patterns Risk analytics & regulation Process optimization Customer Rol Proactive health management
  • 39.
    Machine Learning UseCases Energy Feedstock & Utilities Financial Services Travel & Hospitality Healthcare & Life Sciences Power Usage Analytics Seismic Data Processing Smart Grid Management Energy Demand Risk Analytics & Regulation Customer Segmentation Credit Worthiness Evaluation Aircraft Scheduling Dynamic Pricing Trafic Patteerns & Congestion Management Predictive Maintenance or Condition Monitoring Demand Forecasting Process Optimization Telematics Predictive Inventory Planning Recommendatio Engines Customer ROI & Lifetime Value Alerts & Diagnostics from Real-time Patient Data Predictive Health Management Heathcare Provider Manufacturing Retail
  • 40.
  • 41.
    How does MachineLearning Work ? Define Objectives Prepare Data Train model Integrate model Collect Data Select Algorithm Test Model Identifty, the problem to be solved and create a clear objective. Preparing data is a crucial step and involves building workflows to clean, match and blend the data Data is fed as input and the algorithm configured with the required parameters. A percent of th data can be utilized to train the model. Publish the prepared expriment as web sevice, so appliations can use the model. Collect data from hospitals, health insurance companies, social service agencies, police and fire dept. Depend on the problem to be solved and the type of data an appropriate algorithm will be chosen. The remaining data is utilized to test the model, for accuracy, Depending on the results, improvements, can be performed in the "Train model" and /or "Select algorithm" phases, iteratively.
  • 42.
    Machine Learning Process STEP1 STEP 2 STEP 3 STEP 4 STEP 5 Gathering data from various sources Cleaning data to have homogeneity Modeml Building Selecting the right ML algorithm Gaining insights from the model's results Data visualisation- Transforming results into visuals graphs
  • 43.
    Additional Requierements How to ChooseMachine learning Algorithm How to Select Machine Learning Algorithms Algorithm Cheat Sheet What do you want to do with your Data? Accuracy Training Time Linearity Number of Parameters Number of Features
  • 44.
    Machine Learning vsTraditional Programming Computer Computer Data Handcrafted Model Result Sample Data Expected Result Model New Data Model Result Traditional modelling Machine Learning Predition Learning Predition Computer
  • 45.
    Use c4.5 Implementation To Classify Whyuse Decision Tree Machine Learning Algorithm Decision Trees To Predict Responsible Variable has only 2 Categories Response Variable has Multiple Categories Use Standard Classification here Use c4.5 Implementation Responsible variable is Continuous Linear Relationship between Predictors & Response Non-linear Relationship between Predictors & Response Use Standard Regression Tree
  • 46.
    Disadvantages Challenges and Limitationsof Machine Learning Advantages Easily Identifies Trends and Patterns No Human Intervention Needed Handling multi-dimensional & multi-variety Data Interpretation Results Continuos Improvement Wide Applications High Error- Susceptibility Time and Resources Data Acquisition
  • 47.
    Supervised Learning Unsupervised Learning Reinforcement Learning Machine Understands thedata (identifies Patterns/ Structures) Evaluation is Qualitative or indirect Does not Predict/Find anything Specific An approach to AI Reward Based Learning Learning from +ve & +ve Reinforcement Machine Learns how to act in a Certain Environment To Maximise Rewards Training Rewards Types of Machine Learning Data with Clearly define Output is given Resolves Classification and Regression Problems Makes Machine Learn Explicity Direct feedback is given Predict outome future Inputs Inputs Inputs Outputs Outputs Outputs
  • 48.
    Hidden Markov Model Machine LearningAlgorithms Machine Learning Supervised Unsupervised Reinforcement Regression Clustering Decision Tree Random Forest Classification Association Anlaysis Linear Polynomial SVD PCA K-means KNN Trees Logistic Regression Naïve-Bayes Apriori FP-Growth Continuous Categorical
  • 49.
    Machine Learning Unsupervised Learning Supervised Learning Clustering Dimensionality Reduction Classification Regression Reinforcement Learning Recommender Systems Targetted Marketing Customer Segmentation Real-tme decisions Robot Navigation GameAI Skill Acquisition Learning Tasks Big Data Visualisation Meaningful Compression Structure Discovery Feature Eliitation Image Classification Idenity Fraud Detection Customer Retention Diagnostics Population Growth Prediction Advertising Popularity Prediction Weather Forecasting Market Forecasting Estimating Life Expectancy
  • 50.
    Types of MachineLearning What is Supervised Machine Learning? How Supervised Machine Works? Types of Supervised Machine Learning Algorithms Supervised vs Unspervised Machine Learning Techniques Advantages of Supervised Learning Disadvantags of Supervised Learning Supervised Machine Learning
  • 51.
    Output Training Data set Desired output What isSupervised Machine Learning? Supervised Learning Imput Raw Data Supervisor
  • 52.
    Types of SupervisedMachine Learning Algorithms Supervised Learning Classification Fraud Detection Email Spam Detection Diagnostics Image Classification Regression Risk Assessment Score Prediction
  • 53.
    How Supervised MachineLearning works Classification Sorting Items into Categories Regression Identifing Real Values (Dollars, Weight, etc.) Types of Problems to which it's Suited Feed the machine new, unlabeled information to see if it Tags New data appropriately. If not, Continue Refining the algorithm Provide the Machine Learning Algorithm Categorized or "labeled" Input and Output Data from to Learn
  • 54.
    Advantages of SupervisedLearning Advantages It allows to be very specific about the definition of the labels. In other words, you'll train the algorithm to differntiate different classes where you'll set a perfect decision boudary. You are ready to determine the amount of classes you would like the possess. The input file is extremely documented and is labeled The results produced by the supervised method are more accurat and reliable as compared to the results produced by the unsupervised techniques of machine learning, This is often mainly because the imput file within the supervised algorithm is documented an labeled. This is often a key difference between supervised and unspervised learning. The answers within the analysis and therefore the output of your algorithm are likely to be know thanks to that each one classes used are known
  • 55.
    Disadvantages of SupervisedLearning Supervised learning are often a posh method as compared with the unsupervised method. The key reason is that you simply need to understand alright and label the imput in supervised Learning. It is needed tons of computation time for training. It doesn't happen in real time while the unsupervised learning is about the important time. This is often also a serious difference between supervised and usupervised learning. Supervised machine learning uses of-line analysis. If you're got a dynamic big and growing data, you"re unsure of the labels to predefine the principles. This will be true challenge.
  • 56.
    What is UnsupervisedLearning? How Unsupervised Machine Learning Works Types of Unsupervised Learning Disadvantages of Unsupervised Learning Unsupervised Machine Learning
  • 57.
    Algorithm Output What isUnsupervised Learning? Unsupervised Learning Input Raw Data Unkown output No Training Data Set Interpretation Processing
  • 58.
    How Unsupervised MachineLearning Works Provide the machine learning algorith uncategorized, unlabeled input data to see what patterns it finds Observe and learn from the patterns the machine identifies Types of Problems to Which it's Suited Clustering Identifying similarities in groups For Example: Are there patterns in the data to indicate certain patients will respond better to this treatment than others? Anomaly Detection Identifying abnormalities in data For Example: Is a hacker intruding in our network?
  • 59.
    Types of UnsupervisedLearning Unsupervised Learning Dimensionality Reduction Text Mining Face Recognition Big Data Visualization Image Recognition Clustering Biology City Planning Targeted Marketing
  • 60.
    You cannot getvery specific about the definition of the info sorting and therefore the output. This is often because the info utilized in unsupervised learning is labeled and not know. It's employement of the machine to label and group the data before determing the hidden patterns. The results of the analysis can't be ascertained. There's no prior knowledge within the unsupervised method of machine learning. Additionally, the numbers of classes also are no know. It results in the lack deterine the results generated by the analysis. Less accuracy of the resuts. This is often also because the imput file isn't known and not labeled by peopme beforehand. which suggests that the machine will got to do that alone. Disadvantages of Unsupervised Learning
  • 61.
    Clustering Associatio n Patterns / Structure Discovery Supervisedvs Unsupervised Machine Learning Techniques Supervised Learning Unsupervised Learning Input & Output Data Classification Regression Predictions & Predictive Models Imput Data
  • 62.
    What is reinforcementlearning? How reinforcement learning works Types of reinforcement learning Advantages reinforcement learning Disadvantage of reinforcement learning Reinforcement Learning
  • 63.
    Reinforcement Learning uses rewardsand punishement to train computing models to perform a sequence of selections. Here computing faces a game-like scenario where it employs trial and error to answer, Based on the action it performs, computing gets either rewards or penalties. Its goal is to maximise the rewards. Reinforcement Learning Action State, Reward
  • 64.
    What is ReinforcementLearning? Input Response Feedback Learns Input Reinforced Response It's a mango Wrong! It's an apple Noted It's an apple
  • 65.
    Unsupervised Types of DeepLearning Netwoks Artificial Neural Networks (ANN) Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Self Organinizing Maps (SOM) Bolzmann Machines (BM) AutoEncoders (AE) Artificial Neural Networks Convolutional Neural Networks Recurrent Neural Networks Used for Regresion & Classification Used for Coputer Vision Used for Time Series Analysis Used for Feature Detection Used for Recommendation Systems Used for Recommendation Systems Self-Oragnizin Maps Deep Boltzmann Machines AutoEncoders Supervised Supervised Unsupervised Deep Learning Models
  • 66.
    Inventory Management Types ofReinforcement Learning Gaming Finance Sector Manufacturing Robot Navigation
  • 67.
    Environnement Agent Output Best Action Selection of Algotithm HowReinforcement Learning Works? Reinforcement Learning Imput Raw Data Reward State
  • 68.
    Disadvantage of ReinforcementLearning You cannoy get very specific about the definition of the info sorting and therefore the output. this is often because the info utilized in unspervised learning is labeled and not know. it's employement of the machine to label and group the data before determing the hidden patterns. Less accuracy of the results. this is often also because the inpute file isn't know and not labeled by peole beforehand, which suggests that the machine will got to do that alone. The results of the analysis can't be ascertained. There's no prior knowledge within the unsupervided method of machine learning. Additionally, the numbers of classes also are not know. It results in the lack to determine the results genereted by the analysis Reinforcement learning as a framework is wrong in many various ways, but it's precisly this quality that creates it useful. Too much reinforcement learning can cause an overload of states which may diminish the results. Reinforcement learning isn't prefereable to use for solving simple problems. Reinforcement learning needs tons of knowledge and tons of compulation. it's data- hungry. that's why it works rather well in video games because one can play the sport again and again and again. so getting many data seems feasible.
  • 69.
    What is DeepLearning? Deep Learning Process Classification of Neural Networks Types of Deep Learning Networks Reinfocement leanring Examples of deep leaning applications Why is Deep Learning Important ? Limitations of Deep Learning Deep Learning
  • 70.
    Deep Learning Deep Learningis a sublifield of machine learning that is concerned with agorithms inspired by the brain's Structure functions known as artificial neural networks A computer model can be taught using Deep Learning to run classification actions using pictures, texts or sounds as input &
  • 71.
    Feature Extraction +Classfication Output Deep Learning is a subfield of machine learning that is concerned with algorithms inspired by the brain's structure and functions know as artificial neural networks A computer model can be taught using Deep Learning to run classification actions using pictures, texts or sounds as inoput? What is Deep Learning ? What is Deep Learning? Car Not Car Input
  • 72.
    Artificial Intelligence (AI)is a popular branch of computer science that concerns with building "intelligent" smart machines capable of performing intelligent tasks. With rapid advancement in deep Learning and machine learning, the tech industry is transforming radically. What is Deep Learning ?
  • 73.
    Understand the Problem Deep LearningProcess Identify Data Select Deep Learning Algorithm Training the Model Test the Model
  • 74.
    Why is DeepLearning Important ? Deep Learning Other Learning Algorithms
  • 75.
    Deep Learning Process Identify DataSelect Deep Learning ALgorithms Training the model Test the Model Understand the Problem
  • 76.
    Examples of DeepLearning Applications Applications of Deep Learning Drug Discovery & Better Diagnostics of Diseases in Healthcare Image Recognition Natural Language Processing Robots and Sell Driving Cars Speech Recognition Portfolio Management & Prediction of stock Price Movements
  • 77.
    Limitations of DeepLearning Amount of Data Interoretability Statistical Reasoning Limitations of Deep Learning
  • 78.
    Difference between MachineLearning and Deep Learning Which is better to start Artificiel Intelligence , Machine Learning or Deep Learning ? Difference between Artificial Intelligence vs Machine Learning vs Deep Learning
  • 79.
    Artificial Intelligence originated around1950s Machine Learning is the practice of getting machines to make decisions without being programmed AI represents simulate intelligence in machines AI is a subset of data science Deep Learning is a subset of Machine Learning Ai & Data Science AI VS Machine Learning VS Deep Learning Aim is to build machines which are capable of thinking like humans Machine Learning originated around 1960s Machine Learning is a subset of AI & Data Science Aim is to make machines learn through data so that they can solve problems Deep learning originated around 1970s Deep Learning is the process of using artificial neural networks to solve complex problems Aim is to build neural network that automatically discover patterns for feature detection Artificial Intelligence Machine Learning Deep Learning
  • 80.
    Difference between Machinelearning and deep learning Feature Extraction Feature Extraction+Classification Classification Imput Output Imput Output Machine Learning Deep Learning Car Car Not Car Not Car
  • 81.
    Difference between ArtificialIntelligence vs Machine Learning vs Deep Learning Machine Learning Deep Learning Artificial Intelligence Engineering of making intelligent machines and programs Ability to learn without being explicity programmed Learning based on deep neural network
  • 82.
    Which is betterto start Artificiel Intelligence, Machine Learning or Deep Learning? Subset of AI Techniques which use statistical Methods to Enable Machines to improve with Experiences. Subset of Machine Learning which make the Computation of Multi-layer Neural Networks Feasible. Any technique which enables computers to mimic human behavior Artificial Intellignce Machine Learing Deep Learning
  • 83.
    Machine learning algorithmslearn from data to find hidden relations, tomake predictions, to interact with the world, … A machine learning algorithm is as good as its input data • Good model + Bad data = Bad Results Deep learning is making significant breakthroughs in: speech recognition,language processing, computer vision, control systems, … If you are not using or considering using Deep Learning to understand or solve vision problems, you almost certainly should be Conclusion
  • 84.
    Bibliography Books 1. Introduction to MachineLearningThe Wikipedia Guide Machine Learning for dummies A Willey Brand MACHINE LEARNING Tom M Mitchell Understanding Machine Learning: From Theory to Algorithmsc 2014 by Shai Shalev-Shwartz and Shai Ben-David 2.Journals Journal of Machine Learning Research www.jmlr.org Machine Learning IEEE Transactions on Neural Networks IEEE Transactions on Pattern Analysis and Machine Intelligence Annals of Statistics Journal of the American Statistical Association International Conference on Machine Learning (ICML) European Conference on Machine Learning (ECML) Neural Information Processing Systems (NIPS) Computational Learning International Joint Conference on Artificial Intelligence (IJCAI) ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) IEEE Int. Conf. on Data Mining (ICDM) 3.Conferences
  • 85.