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An overview of Artificial intelligence

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An overview of Artificial intelligence

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This presentation gives you a broad overview of Artificial Intelligence. It explains briefly the technologies and concepts that fall under the domain of AI.

This presentation gives you a broad overview of Artificial Intelligence. It explains briefly the technologies and concepts that fall under the domain of AI.

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An overview of Artificial intelligence

  1. 1. AN OVERVIEW OF ARTIFICIAL INTELLIGENCE Getting started with AI
  2. 2. What is Artificial Intelligence? Artificial Intelligence is the field of study that is: An intelligent entity created by humans. Capable of performing tasks intelligently without being explicitly instructed. Capable of thinking and acting rationally and humanely. In this presentation, we will learn about the types of Artificial Intelligence, various sub-domains of Artificial Intelligence, their applications, and career prospects in the field of AI. Let’s get started! 01
  3. 3. Artificial Narrow Intelligence These AI systems solve a single problem at a time and excel at it Ability to match or surpass human functioning but in very specific context and controlled environments with limited parameters Examples - product recommendations, weather predictions Artificial General Intelligence Artificial General Intelligence is still a theoretical concept. It is the AI which has a human-level of cognitive function An AGI system would need to comprise of thousands of Artificial Narrow Intelligence systems working in tandem and communicating with each other to mimic human reasoning. Types of Artificial Intelligence 02
  4. 4. Sub-domains of Artificial Intelligence Artificial Super Intelligence An Artificial Super Intelligence (ASI) system is still a concept It would be able to surpass all human capabilities including decision making, things like making better art, and building emotional relationships. Artificial Intelligence: Machine Learning Deep Learning Neural Networks Natural Language Processing Computer Vision Cognitive Computing 03
  5. 5. 04 Ques. What is Machine Learning? Ans. Arthur Samuel coined the term Machine Learning in the year 1959. He defined Machine Learning as “Field of study that gives computers the capability to learn without being explicitly programmed”. Machine Learning Importance of Machine Learning Businesses can automate their routine tasks Creating models to process and analyse large amount of complex data to deliver accurate results Helps building precise and scalable models that function with less turnaround time Enables intelligent decision making and forecasting for businesses can leverage profitable opportunities and avoid unknown risks
  6. 6. 05 Unsupervised Learning Clustering Association Types of Machine Learning Supervised Learning Regression Classification Reinforcement Learning Machine Learning Supervised Learning The supervised learning model has a set of input variables (x), and an output variable (y). An algorithm identifies the mapping function between the input and output variables. The relationship is y = f(x).
  7. 7. 06 Unsupervised Learning We already know the output and the algorithm is trained over the data set and amended until it achieves an acceptable level of performance. Regression is used to predict future values and the model is trained with the historical data. E.g., predicting the future price of a product. In classification, various labels train the algorithm to identify items within a specific category. E.g., Disease or no disease, Apple or an orange, Beer or wine. This approach is the one where the output is unknown, and we have only the input variable at hand. The algorithm learns by itself and discovers an impressive structure in the data. Clustering means bundling the input variables with the same characteristics together. E.g., grouping users based on search history. In association, we discover the rules that govern meaningful associations among the data set. E.g., People who watch ‘X’ will also watch ‘Y.
  8. 8. 07 Reinforcement Learning In this approach, machine learning models are trained to make a series of decisions based on the rewards and feedback they receive for their actions. The machine learns from its own experiences when there is no training data set present. Machine Learning Reinforcement Learning Supervised Learning Unsupervised Learning ClassificationDimensionality Reduction RegressionClustering Real-time decisions Robot Navigation Skill Acquisition Learning Tasks Game AI Advertising Popularity Prediction Identity Fraud Detection Image Classification Customer Retention Diagnostics Big Data Visualization Meaningful Compression Structure Discovery Feature Elicitation Weather Forecasting Market Forecasting Estimating Life Expectancy Population Growth Prediction Recommendation Systems Customer Segmentation Targetted Marketing Industry applications of different Types of Machine Learning Here are some of the applications of Machine Learning.
  9. 9. 08 Machine Learning Tools General Machine Learning Frameworks ML frameworks for neural network modelling Big Data Tools Tensorflow & Tensorboard Pytorch Keras Caffe2 Apache Spark MemSQL Numpy Scikit-learn NLTKMachine Learning Languages Data Analysis and Visualisation tools Pandas Matplotlib Jupyter Notebook Tableau Weka Python R C++
  10. 10. 09 What is Deep Learning? It is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural net- works. Deep Learning uses brain simulations to make learning algorithms efficient and simpler to use. The repeated analysis of massive datasets through it- erative learning models eradicates errors and discrep- ancies in findings and lead to a reliable conclusion. Importance of Deep Learning Deep learning is fuelling the automation in to- day’s world. The world is generating exponential amounts of data today, which needs structuring on a large scale. Deep learning uses the growing volume and availability of data most appropri- ately. Deep Learning Applications of Deep Learning
  11. 11. 10 Relationship between AI, ML, and DL Deep Learning Machine Learning Artificial Intelligence
  12. 12. What is a Neural Network? Neural Network is a series of algorithms that mimic the functioning of the human brain to de- termine the underlying relationships and pat- terns in a set of data. Most Common types of Neural Networks are: Feedforward Neural Network - Data inputs travel in one direction, going in from the input node and exiting on the output node. Convolutional Neural Network (CNN) - Similar to the feed-forward neural networks with the neu- rons having learn-able biases and weights. It is applied in signal and image processing. Recurrent Neural Network (RNN) - Saves the output of a layer and feeds it back to the input for helping in predicting the output of the layer. 11 Neural Networks
  13. 13. 12 Natural Language Processing What is Natural Language Processing? Natural Language Processing (NLP) is the ability of a machine to understand the human language as it is spoken, and provide with a result. Importance of NLP NLP enables computers to read data and convey the same in languages humans under- stand. It helps computers to record unstructured data in proper formats. It helps in analysing texts and speech to extract their meaning. Not only is the process auto- mated, but also near-accurate all the time.
  14. 14. 13 Applications of NLP Grammarly, Microsoft Word, Google Docs Search engines like DuckDuckGo, Google Voice assistants - Alexa, Siri News Feeds - Facebook, Google News Translation sytstems - Google translate
  15. 15. 14 Computer Vision Retail stores- for tracking inventory and customers. Facial Recognition for surveillance and security systems. For diagnosing diseases. Financial institutions use computer vision to prevent fraud, allow mobile deposits, and display information visually. What is Computer Vision? A field of study where techniques are developed that enable computers to ‘see’ and understand the digital images and videos. The goal is to draw inferences from visual sources and apply it towards solv- ing real-world problems. What is Computer Vision used for?
  16. 16. 15 What is Deep Learning in Computer Vision? The Deep Learning Approach for Computer Vision Cognitive Computing What is cognitive computing? Object Classification and Localisation Semantic Segmentation Colourisation Reconstructing Images Cognitive computing algorithms try to mimic a human brain by analysing text/speech/images/objects in a manner that a human does and gives the desired output.
  17. 17. 16 AI can be built on a diverse set of components and functions as an amalgamation of- Importance of Artificial Intelligence Philosophy Mathematics Neuroscience Psychology Computer Engineering Control Theory Linguistics The purpose of Artificial Intelligence is to aid human capabilities and help us make advanced decisions with far-reaching consequences. AI is used in different domains to give insights into user behaviour and give recommendations based on the data. Let us take a look at advantages of AI- Reduction in human error 24x7 Availability Automation Digital assistance Rational Decision Maker Medical applications Improves Security Efficient Communication
  18. 18. 17 Healthcare: Industry Applications of AI AI truly has the potential to transform many industries, with a wide range of possible use cases. Let us take a look at some of the indus- tries where AI is currently shining- Administration- AI systems are helping with the routine, day-to-day administrative tasks to minimise human errors and maximise efficiency. Assisted Diagnosis- Through computer vision and convolutional neural networks, AI is now capable of reading MRI scans to check for tumours and other malignant growths. Robot-assisted surgery- Robotic surgeries have a very minuscule margin-of-error and can consistently perform surgerie round-the-clock without getting exhausted. Vital Stats Monitoring- With wearable devices achieving mass-mar- ket popularity now, this data is notavailable on tap. 01 02 03 04
  19. 19. 18 E-commerce: 01 02 03 01 02 Better recommendations- Most large e-commerce players have incorporated Artificial Intelligence to make product recommendations that users might be interested in. Chatbots- These chatbots are now serving customers in odd-hours and peak hours as well, removing the bottleneck of limited human resources. Filtering spam and fake reviews- Through the power of NLP, Artificial Intelligence can scan these re- views for suspicious activities and filter them out, making for a better buyer experience. Entertainment: Personalised user experience is given a lot of importance with streaming channels that recommend content based on specific user activity and behaviour. Artificial Intelligence softwares are improving the speed and efficiency of the media production process and the ability to organise visual assets.
  20. 20. 19 Banking and Finance: 01 02 AI is being used to detect anti-money laundering patterns, which is much more efficient than the traditional rule-based software systems. Apart from the regulatory and legal aspects, banks and financial institutions are using chatbots and virtual assistants to provide better customer service than ever. Marketing: 01 02 AI is being applied for quality checks, maintenance, and creating more reliable designs and layouts for a manufacturing plant and its processes. It can be helpful in reducing environmental impact by applying methods of cutting down waste and using resources optimally. Manufacturing: 01 02 AI is being applied for quality checks, maintenance, and creating more reliable designs and layouts for a manufacturing plant and its processes. It can be helpful in reducing environmental impact by applying methods of cutting down waste and using resources optimally.
  21. 21. 20 Career Opportunities in AI It would not be wrong to state that AI has picked up the pace to reach its prime, and is going to see an upward graph in the coming years. The career opportunities are likewise growing. The challenge is that the supply of skilled resources in Artificial Intelligence lags behind the demand substantially. On av- erage, there has been a 60-70% hike in salaries of aspirants who have successfully transitioned into AI roles. of the companies in india hiring for AI roles. AI job were vacant in the year 2018 57% 4000
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  23. 23. 22-By Kevin Warwick Machine Learning for Absolute Beginners:A Plain English Introduction Superintelligence: Paths, Dangers, Strategies Life 3.0 The Singularity Is Near The Sentiment Machine The Society of Mind The Emotion Machine Human Compatible - Artificial Intelligence and the Problem of Control -By Oliver Theobald -By Nick Bostrom -By Max Tegmark -By Ray Kurzweil -By Amir Husain -By Marvin Minsky -By Marvin Minsky -By Stuart Russell Here are some of the AI books that would help you to start learning about the subject professionally. Best Artificial Intelligence Books for Beginners Artificial Intelligence - A Modern Approach (3 Edition) Machine Learning for Dummies Make Your Own Neural Network Machine Learning: The New AI Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies The Hundred-Page Machine Learning Book Artificial Intelligence for Humans Machine Learning for Beginners Artificial Intelligence: The Basics -By Stuart Russell & Peter Norvig -By John Paul Mueller and Luca Massaron -By Tariq Rashid -By Ethem Alpaydin -By John D. Kelleher, Brian Mac Namee, Aoife D’Arcy -By Andriy Burkov -By Jeff Heaton -By Chris Sebastian
  24. 24. THANK YOU

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