A Ar i l In e l ce Introduction to Arti cial Intelligence Arti cial Intelligence AI is a rapidly growing technology that businesses and organizations are utilizing to improve their operations AI is becoming an essential tool for automating tasks making predictions and improving decision making This article provides a comprehensive understanding of AI from theory to practical applications Arti cial Intelligence Basics Different Types of AI AI Using Python Deep Learning Tensor ow Convolutional Neural Networks Arti cial Neural Networks Recurrent Neural Networks Chat GPU Best Applications of Arti cial Intelligence How to Start a Career in Arti cial Intelligence Best Practices in Arti cial Intelligence Arti cial Intelligence Interview Questions with Answers Agenda 1 What is Arti cial Intelligence Arti cial Intelligence is the science and engineering of making intelligent machines that can work and behave like humans AI has been able to accomplish this by creating machines and robots that have been used in a wide range of elds including healthcare robotics marketing business analytics and many more AI Machine Learning and Deep Learning AI Machine Learning and Deep Learning are interconnected elds Machine Learning is a subset of AI that focuses on getting machines to make decisions by feeding them data Deep Learning is a subset of Machine Learning that uses neural networks to solve complex problems AI however covers a vast domain of elds including natural language processing object detection computer vision robotics expert systems and so on AI can be structured along three evolutionary stages Arti cial Narrow Intelligence Arti cial General Intelligence and Arti cial Super Intelligence Arti cial Narrow Intelligence Weak AI involves applying AI only to speci c tasks such as face veri cation autopilot features in a car and Google Maps Arti cial General Intelligence Strong AI involves machines that have the ability to perform any intellectual tasks that a human being can Arti cial Super Intelligence is presently seen as a hypothetical situation where machines will take over the world by surpassing human capabilities Types of AI 2 Applications of AI AI has found its way into our daily lives and we use it all the time such as Google giving accurate search results Facebook feeds giving content based on our interests and Twitter s AI identifying hate speech and terroristic languages in tweets AI has covered all possible domains in the market including nance healthcare social media and more Introduction to Arti cial Intelligence Arti cial Intelligence AI is a eld of study that focuses on creating machines that can perform tasks that typically require human intelligence such as visual perception speech recognition decision making and language translation AI has become an integral part of our lives and has many applications including virtual assistants self driving cars and image recognition Virtual assistants such as Siri Alexa and Cortana are examples of AI Google s virtual assistant Google Duplex can respond to calls and book appointments for you adding a human touch It has astonished millions of people with its capabilities AI has implemented computer vision image detection and deep learning to build self driving cars that can detect objects and drive around without human intervention Virtual Assistants Self Driving Cars 3 Elon Musk talks extensively about how AI is implemented in Tesla s self driving cars and autopilot features AI Applications Since the emergence of AI in the 1950s we have seen exponential growth in its potential AI covers domains such as machine learning deep learning neural networks natural language processing knowledge base and expert systems It has also made its way into computer vision and image processing It is estimated that AI will take over the world within the next 30 years AI vs Machine Learning vs Deep Learning AI is a broader umbrella under which machine learning and deep learning come Machine learning is a subset of AI that enables the computer to act and make data driven decisions to carry out a certain task Deep learning is a particular kind of machine learning that is inspired by the functionality of our brain cells called neurons Understanding Machine Learning and Deep Learning To understand the difference between machine learning and deep learning it is essential to understand how deep learning works at a conceptual level For instance recognizing a square from other shapes involves checking if there are four connected and closed lines associated with a gure that is perpendicular and all its sides are equal This task is a nested hierarchy of concepts and deep learning works similarly but at a larger scale 4 Deep learning automatically nds the features that are most important for classi cation unlike machine learning where we have to manually give out features Machine learning is the next evolution of machine learning and its performance depends on data dependencies hardware dependencies feature engineering problem solving approach execution time and interpretability Data Dependencies Deep learning algorithms need a large amount of data to understand it perfectly and they don t perform well with small data sets On the other hand machine learning algorithms can easily work with smaller data sets Hardware Dependencies Deep learning algorithms are heavily dependent on high end machines while machine learning algorithms can work on low end machines GPUs are an integral part of deep learning s working as they do a large amount of matrix multiplication operations which can only be e ciently optimized using a GPU Feature Engineering In machine learning most features are identi ed by an expert and then hand coded as per the domain and data type Deep learning algorithms try to learn high level features from the data reducing the task of developing new feature extractors for every problem Problem Solving Approach 5 Machine learning algorithms handle problems by breaking them down into different sub parts solving them individually and then combining them to get the desired result Deep learning algorithms solve a problem from end to end Execution Time Interpretability Deep learning algorithms take a long time to train while machine learning algorithms take much less time to train During testing deep learning algorithms take much less time to run than machine learning algorithms The main reason deep learning is not widely used in the industry is because of its low