Ce diaporama a bien été signalé.
Le téléchargement de votre SlideShare est en cours. ×

PPT1: Introduction to Artificial Intelligence, AI Applications and Advantages of Artificial Intelligence

Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Chargement dans…3
×

Consultez-les par la suite

1 sur 18 Publicité
Publicité

Plus De Contenu Connexe

Diaporamas pour vous (20)

Similaire à PPT1: Introduction to Artificial Intelligence, AI Applications and Advantages of Artificial Intelligence (20)

Publicité

Plus récents (20)

PPT1: Introduction to Artificial Intelligence, AI Applications and Advantages of Artificial Intelligence

  1. 1. AI Matters? Can you emulate brain? How this self created system of intelligency works? How to represent AI?
  2. 2. Table of Contents 1. Introduction to Intelligence (AI) 2. Beginning of AI (Evolution) 3. AI Approach 4. Types of AI a. Type1 b. Type2 5. Applications of AI 6. Technologies in Use 7. AI Advancements in different sectors 8. AI and IOT 9. Advantages & Disadvantages 10 .Data Science for AI 11. Data Science Life Cycle 12. Why ML for AI? 13. ML Types and Algorithms 14. Into Deep Learning 15. DS vs ML vs DL 16. Takeaways
  3. 3. Intelligence The ability to learn, understand and think in a logical way -Oxford Learning Reasoning Problem Solving Perception Language Human Intelligence Artificial Intelligence Human Being Human Machines
  4. 4. Beginning of AI Decoded Enigma Machine – Alan M. Turing Turing Test (1950) – On whether a computer can “think” When Human Machines tactically absorbs or imitates the super human qualities at times far beyond is what we call Artificial Intelligence. 1956 – DartMouth Conference (AI term coined) AI and Computers evolution hugely correlated AI winters ( Computers sorrow -> No for AI) Applied Epistemology Machine Intelligence Computational Intelligence Artificial Intelligence
  5. 5. AI Approach Top-Down Approach Bottom-Up Approach Optical Scanner Code comapares each letter with geometric description
  6. 6. Types of AI
  7. 7. Applications of AI Alexa Ask me Anything
  8. 8. Technologies in Use Text Analytics & NLP Includes the process of text mining, text identification, text parsing, text extraction etc. Text Analytics & NLP Includes the process of text mining, text identification, text parsing, text extraction etc. Decision Management Makes structured business decisions with the help of data. Decision Management Makes structured business decisions with the help of data. Speech Recognition When the system recognizes speech and converts it into text. Speech Recognition When the system recognizes speech and converts it into text. AI-Optimized Hardware Alexa by Amazon AI-Optimized Hardware Alexa by Amazon Biometrics Identification and access control through human characteristics. Biometrics Identification and access control through human characteristics. Robotic Process Automation These are software bots that emulate human interaction within GUI, and automated Business workflows. Robotic Process Automation These are software bots that emulate human interaction within GUI, and automated Business workflows. Computer Vision To see and extract meaning such as Face Recognition, Autonomous Vehicles etc. Computer Vision To see and extract meaning such as Face Recognition, Autonomous Vehicles etc. Virtual Agents Chat bot serving as a customer service representative. Virtual Agents Chat bot serving as a customer service representative.
  9. 9. AI Advancements in different sectors Cyber Security Secure Systems from digital attacks Cyber Security Secure Systems from digital attacks Business Intelligence Best practices of Analysis of systems to better improve and optimize business decisions. Business Intelligence Best practices of Analysis of systems to better improve and optimize business decisions. Education Universal access such as presentation translator, Individualized learning, Automate admin tasks etc. Education Universal access such as presentation translator, Individualized learning, Automate admin tasks etc. Management AI automates more routine tasks thus privides insights into workers productivity. Management AI automates more routine tasks thus privides insights into workers productivity. Supply Chain Management Machine Learning for Warehouse management, Autonomous vehicles for Logistics & Shipping. Supply Chain Management Machine Learning for Warehouse management, Autonomous vehicles for Logistics & Shipping. Manufacturing Generative Design, Computer Vision Manufacturing Generative Design, Computer Vision City Planning Systems easily identify million of elements such as people, cars, Public workers, trash accidents allowing autonomous monitoring. City Planning Systems easily identify million of elements such as people, cars, Public workers, trash accidents allowing autonomous monitoring. Devops and Cloud Hosting Automating software delivery process Devops and Cloud Hosting Automating software delivery process Retail Smart Analytics,Natural Language Processing to streamline shopping experience Retail Smart Analytics,Natural Language Processing to streamline shopping experience Healthcare Virtual Nursing Assistants, Robotic Surgery, administrative tasks, Image Analysis etc. Healthcare Virtual Nursing Assistants, Robotic Surgery, administrative tasks, Image Analysis etc.
  10. 10. AI & IOT Data Discovery Data Preparation Visualization of ● Streaming Data Predictive and ● Advance Analytics Time Series ● Accuracy of Data Real-Time Geospatial ● and Location
  11. 11. Advantages & Disadvantages of AI Advantages ● Reduce time taken for a task ● Overcome Human limitations ● Multi-Tasking ● Ease workload ● Deployed across Industries ● Has no downtime, 24*7 working Disadvantages ● Machines require high cost to create, run, maintain & repair ● Cannot replicate human on moral and emotional level ● Daily basis tasks difficult to acheive through AI ● Resonse altering is difficult for machines as compared to humans ● Affects Industry 4.0
  12. 12. Data Science for AI Is there a Science that experiments with data? Yes And AI helps Process Maintain Analyze Data Science Artificial Intelligence
  13. 13. Data Science Life Cycle
  14. 14. Why Machine Learning For AI ML is the method behind how machines learn from data . AI to grow and get sharpen results it needs to learn from huge data for eg.. Machine Learning Grinder is Algorithms Kiwi ? Machine Learning Algorithms
  15. 15. Machine Learning Types & Algorithms
  16. 16. Into Deep Learning Deep Learning subset of Machine Learning require Artificial Neural network & Algorithms to learn from large amount of Data Applications of DL in AI ● Drones ● Autonomous Cars ● Virtual Assistants ● Facial Recognition ● Chatbots ● Personalized Shopping ● Medicine & Pharmaceuticals
  17. 17. Data Science vs Machine Learning vs Deep Learning Data Science Machine Learning Deep Learning A field encompassing several subfield including AI,ML & DL. A Multidisciplinary field Talks about – AI, ML, DL, Data Visualization, Statistics, EDA, Data Mining etc. Tools – Apache Spark, Matlab, Tableau, Apache Haddop, Scala, Apache Hive etc. A field encompassing several subfield including AI,ML & DL. A Multidisciplinary field Talks about – AI, ML, DL, Data Visualization, Statistics, EDA, Data Mining etc. Tools – Apache Spark, Matlab, Tableau, Apache Haddop, Scala, Apache Hive etc. A specialization or a subset for AI totally into its core. A subfield of AI Talks about – A lot of Algorithms, data dependencies, Features etc. Tools – TenserFlow, Pytorch, Scikit-learn, NLTK, Tenserboard etc. A specialization or a subset for AI totally into its core. A subfield of AI Talks about – A lot of Algorithms, data dependencies, Features etc. Tools – TenserFlow, Pytorch, Scikit-learn, NLTK, Tenserboard etc. A specialization or a subset for ML totally into its core. A subfield of ML Talks About – Few Algorithms, large training datasets, high data dependencies. Tools – CNTK, Caffe, MXNet, Chainer, Keras, Deeplearning4j A specialization or a subset for ML totally into its core. A subfield of ML Talks About – Few Algorithms, large training datasets, high data dependencies. Tools – CNTK, Caffe, MXNet, Chainer, Keras, Deeplearning4j
  18. 18. Takeaways AI Matters? Yes Can you emulate brain? No, not fully How this self created system of intelligency works? Hope you understand it by now How to represent AI? Alexa, Siri

×