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OA Introduction to AI from Object Automation

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OA Introduction to AI from Object Automation

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Object Automation Software Solutions Pvt Ltd in collaboration with SRM Ramapuram delivered Workshop for Skill Development on Artificial Intelligence.
Introduction to AI by Mr.Vaibhav Raja, Research Scholar from Object Automation.

Object Automation Software Solutions Pvt Ltd in collaboration with SRM Ramapuram delivered Workshop for Skill Development on Artificial Intelligence.
Introduction to AI by Mr.Vaibhav Raja, Research Scholar from Object Automation.

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OA Introduction to AI from Object Automation

  1. 1. Introduction to Artificial Intelligence By Vaibhav Raja Research Scholar Object Automation Software Solutions
  2. 2. What is AI?  According to Britannica, Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Humans have been obsessed with automation since the beginning of technology adoption. AI enables machines to think without any human intervention. It is a broad area of computer science. AI systems fall into three types: ANI: Artificial Narrow Intelligence, which is goal-oriented and programmed to perform a single task. AGI (Artificial General Intelligence) which allows machines to learn, understand, and act in a way that is indistinguishable from humans in a given situation. ASI (Artificial Super Intelligence) is a hypothetical AI where machines are capable of exhibiting intelligence that surpasses brightest humans.
  3. 3. Impact of AI in day to day life  Social media  Digital Assistants  Self-Driving And Parking Vehicles  Email communications  Web searching  Stores and services  Offline experiences
  4. 4. How Artificial Intelligence Improves Social Media •Twitter • From tweet recommendations to fighting inappropriate or racist content and enhancing the user experience, Twitter has begun to use artificial intelligence behind the scenes to enhance their product. • Facebook • Deep learning is helping Facebook draw value from a larger portion of its unstructured datasets created by almost 2 billion people updating their statuses 293,000 times per minute. •Instagram • Instagram also uses big data and artificial intelligence to target advertising and fight cyberbullying and delete offensive comments. •Chatbots • Chatbots recognize words and phrases in order to (hopefully) deliver helpful content to customers who have common questions
  5. 5. How Artificial Intelligence Helps You Every Day Through Parking Your Car And Driving It  Self-driving and parking cars use deep learning, a subset of AI, to recognize the space around a vehicle.  Technology company Nvidia uses AI to give cars “the power to see, think, and learn, so they can navigate a nearly infinite range of possible driving scenarios,” .  The company’s AI-powered technology is already in use in cars made by Toyota, Mercedes-Benz, Audi, Volvo, and Tesla , and is sure to revolutionize how people drive and enable vehicles to drive themselves.
  6. 6. How Artificial Intelligence Improves Email Communications  Smart Replies in Gmail  Smart replies offer users a way to respond to emails with simple phrases like “Yes, I’m working on it.” or “No I have not. ” with the click of a button.  Email Filters in Gmail  Google uses AI to ensure that nearly all of the email landing in your inbox is authentic. Their filters attempt to sort emails into the following categories: Primary, Social, Promotions, Updates, Forums, Spam.
  7. 7. How Artificial Intelligence Helps With Web Searches  Google Predictive Searches:  When you begin typing a search term and Google makes recommendations for you to choose from, that’s AI in action.
  8. 8. Google’s Algorithm  Google search engines evolved over time by studying the linguistics used in searches. Its AI learns from results and adapts over time to better meet the needs of users.  For example, a search of “what are neural networks and how are they related to synapses” offers Google’s choice of “best answer” highlighted at the top, followed by a list of sources that answer the question.
  9. 9. How Artificial Intelligence Improves Your Experience at Online Stores and Services?  Product Recommendations  Amazon and other online retailers use AI to gather information about your preferences and buying habits. Then, they personalize your shopping experience by suggesting new products tailored to your habits.  Below is an example of AI-powered recommendations on Amazon.com.
  10. 10. What are the different types of recommendations? There are basically three important types of recommendation engines: • Collaborative filtering • Content-Based Filtering • Hybrid Recommendation Systems
  11. 11. How AI can have an impact on Employee Lifecycle Management • AI automation in Talent Acquisition and HR Practices • AI in Recruitment Process • AI in Interviewing • AI in onboarding • AI in giving Feedback • AI in Employee Attrition • AI in Employee Joining
  12. 12. Machine Learning  Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.  ML is one of the most exciting technologies that one would have ever come across.  As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn.  Machine learning is actively being used today, perhaps in many more places than one would expect. ML is a subset of AI that uses statistical learning algorithms to build smart systems. The ML systems can automatically learn and improve without explicitly being programmed. The recommendation systems on music and video streaming services are examples of ML. The machine learning algorithms are classified into three categories: supervised, unsupervised and reinforcement learning.
  13. 13. Applications of Machine Learning  1. Image Recognition:  Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is, Automatic friend tagging suggestion:  Facebook provides us a feature of auto friend tagging suggestion. Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with name, and the technology behind this is machine learning's face detection and recognition algorithm.  2. Speech Recognition  While using Google, we get an option of "Search by voice," it comes under speech recognition, and it's a popular application of machine learning.  3. Traffic prediction:  If we want to visit a new place, we take help of Google Maps, which shows us the correct path with the shortest route and predicts the traffic conditions.
  14. 14.  4. Product recommendations:  Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendation to the user.  5. Self-driving cars:  One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on self-driving car. It is using unsupervised learning method to train the car models to detect people and objects while driving.  6. Stock Market trading:  Machine learning is widely used in stock market trading. In the stock market, there is always a risk of up and downs in shares, so for this machine learning's long short term memory neural network is used for the prediction of stock market trends.  7. Medical Diagnosis:  In medical science, machine learning is used for diseases diagnoses. With this, medical technology is growing very fast and able to build 3D models that can predict the exact position of lesions in the brain.  It helps in finding brain tumors and other brain-related diseases easily.
  15. 15. Deep Learning  Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions.  Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled.  Deep learning, a form of machine learning, can be used to help detect fraud or money laundering, among other functions. This subset of AI is a technique that is inspired by the way a human brain filters information. It is associated with learning from examples. DL systems help a computer model to filter the input data through layers to predict and classify information. Deep Learning processes information in the same manner as the human brain. It is used in technologies such as driver-less cars. DL network architectures are classified into Convolutional Neural Networks, Recurrent Neural Networks, and Recursive Neural Networks. How Deep Learning Works?  Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world.  This data, known simply as big data, is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing.
  16. 16. Deep Learning vs. Machine Learning  One of the most common AI techniques used for processing big data is machine learning, a self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with newly added data.  If a digital payments company wanted to detect the occurrence or potential for fraud in its system, it could employ machine learning tools for this purpose.  Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web.  While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a nonlinear approach.
  17. 17. OpenPOWER AI  OpenPOWER Foundation is an open development alliance based on IBM's POWER microprocessor architecture. The Consortium intends to build advanced server, networking, storage and GPU-acceleration technology aimed at delivering more choice, control and flexibility to developers of next- generation, hyper scale and cloud data centers.  OpenPOWER AI , Cloud Virtual University YouTube Channel: https://www.youtube.com/c/OpenPOWERAICloudVirtualUniversity/videos
  18. 18. Some fascinating videos on AI and other recent technologies:  OpenPOWER AI and HPC Webinar from Virginia Tech - https://youtu.be/IEXcJ2KM33g  OpenPOWER Webinar Series : AI at Scale in Enterprise and Containerizing Applications: https://youtu.be/TCYC1eXUAb4  OpenPOWER Workshop for AI and HPC at CINECA - https://youtu.be/- xo8k_J_M54  Open POWER ISA Cores- https://youtu.be/nejEA15xTnU  OpenPOWER Webinar: OpenShift and Kubernetes on POWER/OpenPOWER systems by Industry experts - https://youtu.be/7xIQYVeIk40  OpenPOWER Webinar from Berkeley Lab and IBM Weather.com - https://youtu.be/WGp-VM5TYek  Machine Learning at Scale using OpenPOWER/POWER9 Systems - https://youtu.be/TzHf5hLbusU
  19. 19.  Deep learning Fundamentals and Case Studies using IBM POWER systems - https://youtu.be/JZu5lGCgbHM  Deep learning fundamental and Research project on IBM POWER9 system from NUS - https://youtu.be/MVlqyzVfDdE  OpenPOWER Summit NA 2020 - https://www.youtube.com/playlist?list=PLEqfbaomKgQpc - jB5bVjluRdzh9Pdr3kY
  20. 20. Conclusion  Artificial Intelligence makes our lives more efficient every day AI powers many programs and services that help us do everyday things such as connecting with friends, using an email program, or using a ride-share service.  If you have reservations about the use of artificial intelligence, it may be comforting to know that most of us have been using AI on a daily basis for many years.
  21. 21. Thank You! We look forward to working together! www.object-automation.com Object Automation Software Solutions Pvt. Ltd No.1, Nehru Street, Second Floor, Kooturavu Nagar, Adambakkam, Chennai – 600 088 Contact: hr@object-automation.com +91 7397784815

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