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AI, Machine Learning and Deep Learning - The Overview

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AI, Machine Learning and Deep Learning - The Overview

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The deck takes you into a fascinating journey of Artificial Intelligence, Machine Learning and Deep Learning, dissect how they are connected and in what way they differ. Supported by illustrative case studies, the deck is your ready reckoner on the fundamental concepts of AI, ML and DL.

Explore more videos, masterclasses with global experts, projects and quizzes on https://spotle.ai/learn

The deck takes you into a fascinating journey of Artificial Intelligence, Machine Learning and Deep Learning, dissect how they are connected and in what way they differ. Supported by illustrative case studies, the deck is your ready reckoner on the fundamental concepts of AI, ML and DL.

Explore more videos, masterclasses with global experts, projects and quizzes on https://spotle.ai/learn

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AI, Machine Learning and Deep Learning - The Overview

  1. 1. Artificial Intelligence, Machine Learning and Deep Learning Spotle.ai Study Material Spotle.ai/Learn 1
  2. 2. Instructors Mousum Dutta Chief Data Scientist, Spotle.ai IIT Kharagpur Dr Sourish Das Assistant Professor, Chennai Mathematical Institute Common Wealth Rutherford Fellow, University of Southampton Spotle.ai Study Material Spotle.ai/Learn 2
  3. 3. Contents: What is Artificial Intelligence? What is Machine Learning? How do Machines Learn? Types of Learning AI, Deep Learning and Machine Learning Machine Learning – Applications A B C D F E Spotle.ai Study Material Spotle.ai/Learn 3
  4. 4. A What Is Artificial Intelligence? Artificial Intelligence is the technique of building intelligent machines or computer programs – systems that can ‘think’. It is the set of principles applied to make computers capable of doing tasks that need human intelligence for e.g. playing chess, driving a car, carrying out a conversation. Spotle.ai Study Material Spotle.ai/Learn 4
  5. 5. Confidential Turing Test John Mccarthy coined the term Artificial Intelligence in 1955. In 1950, Alan Turing devised the Turing Test to assess machine intelligence. The test requires that a human being should be unable to distinguish the machine from another human being by using the replies to questions put to both. Gaming AI Arthur Samuel’s checkers program, developed in the middle 50s and early 60s, challenged a respectable amateur. In 1997 IBM’s Deep Blue beat Gary Kasparov at chess. Autonomous Cars In 2005, a Stanford robot drove autonomously for 131 miles along an unrehearsed desert trail. Autonomous cars denote a significant break-through in the history of AI as it requires an agent to process multi-sensory inputs and process those in real-time. A The AI Milestones Spotle.ai Study Material Spotle.ai/Learn 5
  6. 6. A Types of AI Spotle.ai Study Material Spotle.ai/Learn 6 Narrow AI or Weak AI – Defines most of the AI we have around us now – AI trained to perform a single task within a pre-defined range. E.g. Siri can tell you the weather but will be stumped if you ask it existential questions. General AI or Strong AI (Also known as Full AI) - Strong artificial intelligence (strong AI) is an artificial intelligence construct that can ‘think’ for itself with full cognitive abilities like a human brain. Super AI - Super-intelligence denotes hypothetical agent that possesses intelligence surpassing human intelligence. Think Matrix.
  7. 7. “Machine learning and AI is a horizontal enabling layer. It will empower and improve every business, every government organization, every philanthropy — basically there’s no institution in the world that cannot be improved with machine learning.” – Jeff Bezos A AI World-view – The Optimist Spotle.ai Study Material Spotle.ai/Learn 7
  8. 8. “With Artificial Intelligence, we are summoning the demon.” – Elon Musk A AI World-view – The Pessimist Spotle.ai Study Material Spotle.ai/Learn 8
  9. 9. What we see around us, is still much of Applied Or Narrow AI, AI with ability to perform one task like playing chess or driving a car well. With more computing power, machines can beat us at these tasks too. But the construct of super-intelligent, omnipotent AI is at best a myth. Of course AI is subject to human bias and intentions – at best or worst and that is what we have to guard against. Sorry, Elon but there are no demons coming! A AI World-view – The Pragmatist Spotle.ai Study Material Spotle.ai/Learn 9
  10. 10. Machine Learning is a discipline of AI, specifying the ability of systems to automatically learn from experience without the need of being explicitly programmed. A key, building block of Artificial Intelligence, Machine Learning mimics human learning to allow systems automatically process data, infer results and adapt its behaviour. Computer System Computer System Data Algorithm Output Data Output Algorithm Traditional Programming Machine Learning B What Is Machine Learning? Spotle.ai Study Material Spotle.ai/Learn 10
  11. 11. Machine Learning works through a cyclic process of training, validation, testing, feedback and optimisation A subset of real data is provided to the data scientist. The data includes a sufficient number of positive and negative examples to allow any potential algorithm to learn Training Testing with a subset of real data called the validation set and measuring the error to choose best fit algorithm. Validation The final algorithm is tested with another subset of data called test set. It is then deployed and observed in live scenario. Test and Deploy Feedback and Optimization C How does Machine Learning work? Spotle.ai Study Material Spotle.ai/Learn 11
  12. 12. Machine Learning algorithms are trained on labelled data. For e.g., a chatbot is trained to recognise that words like ‘Hey’, ‘Hello,’ ‘Hi’, ‘Hola’ are greetings and have to be accordingly responded to. Labelled/ Classified Data Machine Learning Algorithm Real-world Data Learned Model Prediction TrainingPrediction C How do you ‘Train Machines’? Spotle.ai Study Material Spotle.ai/Learn 12
  13. 13. Machine Learning algorithms can be broadly divided into 3 types: Supervised Learning • Is a branch of machine learning that learns on the basis of labelled training data. • E.g. a chatbot recognises greetings such as ‘Hello’, ‘Hi’, ‘Hey’ based on classified text. Unsupervised Learning • Unsupervised learning learns from test data that has not been labelled, classified or categorized. It works on the basis of clustering. E.g. grouping similar resumes based on text patterns. Reinforcement Learning • It is about taking suitable action to maximize reward in a particular situation. For e.g. learn how to play Chess or Jeopardy by winning/ losing or answering right/ wrong. D Types Of Learning Spotle.ai Study Material Spotle.ai/Learn 13
  14. 14. Deep Learning is a specific type of Machine Learning that works on a hierarchy of concepts built on top of each other, eliminating the need for humans to explicitly specify all knowledge (tags, classifiers) for a machine to make a decision. Deep Learning gives machines the power to solve more intuitive problems by automatically extracting features and classifiers from data sets. Input Data Feature/ Tag Extraction Input Data Deep Learning Algorithm Traditional Machine Learning Algorithm E What Is Deep Learning?TraditionalMachine Learning Deep Learning Spotle.ai Study Material Spotle.ai/Learn 14
  15. 15. Machine Learning is a discipline of Artificial Intelligence. Deep Learning is a specialised form of Deep Learning Artificial Intelligence Machine Learning Deep Learning •Ability of machines to think and act rationally Artificial Intelligence •Ability of machines to take decisions without being explicitly programmed. •Traditional Machine Learning trains machines based on labelled and classified data. Machines take decisions basis this data Machine Learning •Machines that mimic human brain through artificial neural networks capable of extracting features and classifiers from data •Does not require pre-processing of data or feature engineering Deep Learning E AI, ML and DL – How are they related? Spotle.ai Study Material Spotle.ai/Learn 15
  16. 16. Machine Learning Deep Learning Feature Engineering In traditional Machine Learning, features or discriminators have to be extracted and hand-coded by experts. Deep Learning algorithms extract high-level features from data Human Intervention If an ML algorithm returns an inaccurate prediction, then an engineer needs to step in and make adjustments. E.g. a chatbot can be trained to start a conversation with Hello, Hi. With a deep learning model, the algorithms can determine on their own if a prediction is accurate or not. A deep learning model analyses human conversation to know it can also start with Hey Data Dependency Traditional ML with handcrafted rules can be trained with less data. Deep Learning algo need more extensive data sets for initial training. Hardware Dependency Traditional ML needs lower computing power as compared to Deep Learning Needs higher computing power as it crunches large amounts of data. E Difference Between ML and DL Spotle.ai Study Material Spotle.ai/Learn 16
  17. 17. Feed-forward Network Convolutional Neural Network Self-organising Neural Network Recurrent Neural Networks or LSTM (Long Short Term Memory) Modular Neural Network Radial Basis Function Neural Network Artificial Neural Network (ANN), forming the basis of Deep Learning, is an information processing construct modeled on the brain. An ANN is made up of layers of inter-connected processing elements (neurons). Each neuron takes an input, performs a task and passes the output to the following neuron. There are 6 broad types of ANNs listed below: E Artificial Neural Networks – Definition and Types Spotle.ai Study Material Spotle.ai/Learn17
  18. 18. Some Machine Learning ApplicationsSpotle.ai Study Material Spotle.ai/Learn 18
  19. 19. F Recommender Systems – Netflix Knows Your Favourite Movies Spotle.ai Study Material Spotle.ai/Learn 19 Netflix’s recommendation system works on a 3 pronged model: ✓ Viewing data from over 100M user accounts ✓ Content tagging by Netflix’s staff and freelancers to understand for e.g. if a content is cerebral or funny ✓ Machine Learning Algorithms that crunch the labelled or tagged data and real-time user data to generate viewing predictions
  20. 20. Can a computer recognise a cat? Yes ML algorithms can be trained on tagged cat images, to recognise a cat. Confidential This is a cat! This is a cat! This is also a cat! F Image Recognition - Voila! It Is A Cat! Spotle.ai Study Material Spotle.ai/Learn 20
  21. 21. Confidential Digital assistants such as Alexa or Siri or the supposedly mind-blowing Google Duplex use a variety of technology including 1. Speech Processor System consisting of a DSP or Digital Signal Processing Module 2. Pattern Recognition consisting of NLU (Natural Language Understanding) and NLP (Natural Language Processing) to understand the speech and 3. A Speech synthesis module based on NLG or Natural Language Generation techniques. F Speech Recognition – Google Duplex, Siri, Alexa Spotle.ai Study Material Spotle.ai/Learn 21

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