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Artificial Intelligence

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Artificial Intelligence

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The slide helps to get an insight on the concepts of Artificial Intelligence.
The topics covered are as follows,
* Concept of AI
* Meaning of AI
* History of AI
* Levels of AI
* Types of AI
* Applications of AI - Agriculture, Health, Business (Emerging market), Education
* AI Tools and Platforms

The slide helps to get an insight on the concepts of Artificial Intelligence.
The topics covered are as follows,
* Concept of AI
* Meaning of AI
* History of AI
* Levels of AI
* Types of AI
* Applications of AI - Agriculture, Health, Business (Emerging market), Education
* AI Tools and Platforms

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Artificial Intelligence

  1. 1. Artificial Intelligence Module - 3
  2. 2. Table of Contents Concept of AI Meaning of AI History of AI Levels of AI Types of AI Applications of AI - Agriculture, Health, Business (Emerging market), Education AI Tools and Platforms
  3. 3. Artificial Intelligence - Introduction • Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind • Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks • Computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data. • Example:  Self Driving Cars  Chess Playing Computer – Deep Blue  Chatbot
  4. 4. Artificial Intelligence - Introduction
  5. 5. Goals of Artificial Intelligence  Logical Reasoning - IBM Deep Blue  Knowledge Representation – Smalltalk Programming Language  Planning and Navigation – Self Driving Vehicles  Natural Language Processing – Alexa, Siri  Perception – Human Machine Interaction {Touch, Sense, Sight, Hear}  Emergent Intelligence - Emotional Intelligence and Moral Reasoning
  6. 6. History of Artificial Intelligence
  7. 7. 1950: Alan Turing publishes Computing Machinery and Intelligence 1956: John McCarthy coins the term 'artificial intelligence' at the first-ever AI conference at Dartmouth College 1960s: Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning 1967: Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that 'learned' though trial and error 1970s: Defense Advanced Research Projects Agency (DARPA) completed street mapping projects 1980s: Neural networks which use a backpropagation algorithm to train itself become widely used in AI applications 1997: IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch) 2002 - The first commercially successful robotic vacuum cleaner was created 2011: IBM Watson beats champions Ken Jennings and Brad Rutter at Jeopardy! 2015: Baidu's Minwa supercomputer uses a special kind of deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human 2016: DeepMind's AlphaGo program 2006 – Present: Driverless Vehicles, Chatbots, Automated Robots History of Artificial Intelligence
  8. 8. Types of Artificial Intelligence
  9. 9. Types of Artificial Intelligence – Capability Based Weak AI  Weak AI outperforms humans in narrowly defined tasks  Chatbot that answers customer service questions  Facial recognition on Facebook  Alexa, Google Assistant, and Siri Augmented AI  Helping humans make better decisions, also boosts their expertise and improves their productivity  IBM Watson for Oncology  Humans become faster and smarter at the tasks they’re performing Generalized AI  Form of “Whole Brain Emulation”, where a machine can think and make decisions on many different subjects  Computers we see on science- fiction video  Talking to humans about many subjects
  10. 10. Weak AI
  11. 11. Augmented AI
  12. 12. Artificial Narrow Intelligence  Artificial Narrow intelligence or “Weak” AI refers to machines are specialized in one area and solves one problem  ANI model can only execute the task for which it was trained - Unable to perform beyond its area of expertise.  Apple Siri, which operates on a set of pre-defined functions, is one of the finest examples of ANI  The IBM Watson supercomputer, that integrates machine learning and natural language processing with an expert systems approach  Playing chess, product recommendations on an e-commerce site, self-driving vehicles, speech recognition, and image recognition are all examples of narrow AI
  13. 13. Artificial General Intelligence  Artificial General intelligence or “Strong” AI refers to machines that exhibit human intelligence  AGI can successfully perform any intellectual task that a human being can  Movies like “Her” or other sci-fi movies in which humans interact with machines and operating systems that are conscious, sentient, and driven by emotion and self-awareness  AGI able to - reason, solve problems, make judgements under uncertainty, plan, learn, integrate prior knowledge in decision- making, and be innovative, imaginative and creative
  14. 14. Artificial Super Intelligence  “Any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest” - Nick Bostrom {Oxford philosopher}  Artificial Super Intelligence (ASI) will surpass human intelligence in all aspects — from creativity, to general wisdom, to problem-solving
  15. 15. Types of Artificial Intelligence - Functionality Based
  16. 16. Types of Artificial Intelligence - Functionality Based
  17. 17. Importance of Artificial Intelligence  AI automates repetitive learning and discovery through data - AI performs frequent, high-volume, computerized tasks  AI adds intelligence to existing products - Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies  AI adapts through progressive learning algorithms to let the data do the programming - models adapt when given new data  AI analyzes more and deeper data using neural networks that have many hidden layers  AI achieves incredible accuracy through deep neural networks - AI techniques from deep learning and object recognition can now be used to pinpoint cancer on medical images with improved accuracy  AI gets the most out of data – When algorithms are self-learning, the data itself is an asset
  18. 18. Artificial Intelligence Frameworks
  19. 19. Machine Learning  Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude  Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.  While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn
  20. 20. Deep Learning Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing
  21. 21. Neural Network A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
  22. 22. NLP – Natural Language Processing  Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.  Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language.  NLP helps computers communicate with humans in their own language, making it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important.
  23. 23. Computer Vision  Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”  From recognizing faces to processing the live action of a football game, computer vision rivals and surpasses human visual abilities in many areas.
  24. 24. Commercial Business uses of AI  Banking Fraud Detection From extensive data consisting of fraudulent and non-fraudulent transactions, the AI learns to predict if a new transaction is fraudulent or not. • Online Customer Support AI is now automating most of the online customer support and voice messaging systems. • Cyber Security Using machine learning algorithms and sample data, AI can be used to detect anomalies and adapt and respond to threats. • Virtual Assistants Siri, Cortana, Alexa, and Google now use voice recognition to follow the user's commands. They collect information, interpret what is being asked, and supply the answer via fetched data. These virtual assistants gradually improve and personalize solutions based on user preferences.
  25. 25.  Finance sector Analyzing stock markets to give future trends and keep finances in check  Manufacturing Sector Assembling are already done by robotic hands in building complex systems such as electronic goods and automobiles  Robotics Automating manual repetitive tasks  Spam and Malware Filtering  Automatic Language Translation  Product Recommendations  Traffic Prediction  Driverless Cars Commercial Business uses of AI
  26. 26. AI in Detecting Floods – Natural Calamities In the flood-prone region of Patna in northern India, the waters were rising. But thanks in part to an artificial intelligence system, residents of the region received early warnings on their phones. A flood forecasting system that Google developed for India’s Central Water Commission is making a difference! But it can do more than forecast high waters. It’s also smart enough to avoid false alarms. Sella Nevo, the head of the flood forecasting unit and a software engineering manager at Google, notes that “For our high-risk alerts, we had less than 10 percent false positives [down to regions measuring 64 by 64 meters] ... That’s highly accurate.” The trick is training the system’s accuracy so that unnecessary evacuations are avoided, and trust can be built for the alert system.
  27. 27. AI in Health Care  AI applications can provide personalized medicine and X-ray readings. Personal health care assistants can act as life coaches, reminding you to take your pills, exercise or eat healthier  Cardiologists often work in fast-paced healthcare environments where inefficiency or delays can affect their ability to deliver high-quality care  cardiologists and their teams can streamline workflows to make their cardiovascular service line more efficient, cost- effective and patient-centered
  28. 28. AI in Education Education at any time Education adapts to student needs Virtual mentors Personalization Curriculum automatic formulation Ability to detect weakness Better engagement Example: Little Dragon, Brainly, ThinkerMath, CTI etc..
  29. 29. AI in Agriculture Analyzing Market Demand AI can simplify crop selection and help farmers identify what produce will be most profitable. Managing Risk Farmers can use forecasting and predictive analytics to reduce errors in business processes and minimize the risk of crop failures. Breeding Seeds By collecting data on plant growth, AI can help produce crops that are less prone to disease and better adapted to weather conditions. Monitoring Soil Health AI systems can conduct chemical soil analyses and provide accurate estimates of missing nutrients. Protecting Crops AI can monitor the state of plants to spot and even predict diseases, identify and remove weeds, and recommend effective treatment of pests. Feeding Crops AI is useful for identifying optimal irrigation patterns and nutrient application times and predicting the optimal mix of agronomic products. Harvesting With the help of AI, it’s possible to automate harvesting and even predict the best time for it.
  30. 30. AI in Agriculture  Using AI and machine learning-based surveillance systems to monitor every crop field's real-time video feeds identifies animal or human breaches, sending an alert immediately  AI and machine learning improve crop yield prediction through real-time sensor data and visual analytics data from drones  The UN, international agencies and large-scale agricultural operations are pioneering drone data combined with in-ground sensors to improve pest management  Shortage of agricultural workers, making AI and machine learning-based smart tractors, agribots and robotics a viable option for many remote agricultural operations that struggle to find workers  Improving the track-and-traceability of agricultural supply chains by removing roadblocks to getting fresher, safer crops to market is a must-have today  Optimize the right mix of biodegradable pesticides and limiting their application to only the field areas that need treatment to reduce costs while increasing yields is one of the most common uses of AI and machine learning in agriculture today  Monitoring livestock’s health, including vital signs, daily activity levels and food intake, ensures their health, is one of the fastest-growing aspects of AI and machine learning in agriculture
  31. 31. AI in Business Retail AI provides virtual shopping capabilities that offer personalized recommendations and discuss purchase options with the consumer. Stock management and site layout technologies will also be improved with AI. Manufacturing AI can analyze factory IoT data as it streams from connected equipment to forecast expected load and demand using recurrent networks, a specific type of deep learning network used with sequence data.
  32. 32. AI/ML across Retail Value Chain
  33. 33. AI in Banking Artificial Intelligence enhances the speed, precision and effectiveness of human efforts. In financial institutions, AI techniques can be used to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, as well as automate manually intense data management tasks
  34. 34. AI Platforms  Google Cloud AI  Amazon AI services  Microsoft Azure AI  H2O.ai  IBM Watson Studio  TensorFlow  DataRobot  Wipro Holmes AI and automation platform  Salesforce Einstein  Infosys Nia
  35. 35. AI Tools Scikit Learn Tensorflow Auto ML Theano Caffe MxNet Keras PyTorch CNTK Google ML Kit
  36. 36. Artificial Intelligence for Enterprise Choice and Flexibility Deploy your AI applications on the cloud environment that best supports your business needs Security and Trust Take advantage of built-in security capabilities and AI model monitoring Deep Industry Capabilities Choose from a wide range of AI products, built for the specific needs of your industry
  37. 37. Thank You
  38. 38. Appendix

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