ch-1introductiontoartificialintelligence-220915143706-2bad755a.pptx

artificial intelligence class 9 cbse

1. Artificial Intelligence Class-VIII

2. ability to acquire and apply knowledge and skills Man-made

3. https://www.youtube.com/watch?v=UFDOY1wOOz0&app=desktop

4. Alan Turing In 1950, English mathematician and scientist published paper speculating about developing machines that can think. He introduced the Turing test to determine whether a computer can think like human .

5. John mccarthy The term artificial intelligence was first coined by john McCarthy in 1956 when he held the first academic conference on the subject. He defined AI as “ The science and Engineering of making intelligent machines ”

6. Turing Test – Can a computer respond like a human? 1950 Alan Turing’s paper, Computing Machinery and Intelligence, described what is now called “The Turing Test”. If a computer system can successfully convince an interrogator that he is a human being, then it can be called Artificially intelligent

7. Artificial Intelligence Artificial Intelligence (AI) is a field of computer science aimed at developing machines which are intelligent enough to do certain tasks that would normally be performed only by humans. It uses machine learning to continuously learn and adapt its algorithms to become smarter each time and autonomously undertake actions without human intervention. Machine learning essentially means machines learning on their own to improve their functioning without human intervention.

8. Artificial Intelligence Artificial Intelligence (AI) refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making; it is inspired by the ways people use their brains to perceive, learn, reason out and decide the action

9. Difference between Human brain and Computers Brain is more analog while computers are digital Brain has content addressable memory Brain is a parallel machine while computers are stackable modular serial Processing speed and system clock Short term memory vs RAM Processing and memory functions separation

10. Activity 1 What is easy and what is difficult for Computers? Hard for Computers but easy for Humans Hard for Humans but easy for Computers Voice Recognition (the context and semantics of language) Sorting items based on a particular attribute (numbers, letters, words etc.) Voice Recognition (sarcasm, emotions) Location and Directions Face Recognition (finding someone in a photo) Math Problems such as adding / multiplying/ dividing Image Recognition (finding objects in images) Searching for an item from a list

11. Types of ai Broad AI: systems are capable of executing multiple tasks across various fields. Imagine a robot which can do your laundery , understand your voice commands for reading emails, managing calls and schedule appointments- all at once. Broad AI would truly replicate human intelligence and help us leverage true power of AI. Narrow AI: Narrow AI systems are very good at one specific task that they are designed to do. They can’t execute any task outside their scope. Imagine an image recognition system that is designed to distinguish between humans and animals but cannot tell the difference between cat and dog unless it is designed so. For Eg. A spam filtering tool in your mailbox

12. Types of ai Generic AI (AGI): Artificial General intelligence or “Strong” AI refers to machines that exhibit human intelligence. In other words, AGI can successfully perform  any  intellectual task that a human being can. This is the sort of AI that we see sci-fi movies in which humans interact with machines and operating systems that are conscious, sentient, and driven by emotion and self-awareness. AGI is expected to be able to reason, solve problems, make judgements under uncertainty, plan, learn, integrate prior knowledge in decision-making, and be innovative, imaginative and creative.

13. A big leap in pursuing general ai- ibm watson One of the mainstream applications in the world moving towards generic AI was IBM Watson. Watson had humble beginning as the Computer system developed to answer questions on the famous quiz show Jeopardy! (a US based reality show) and in 2011, the Watson computer system completed on Jeopardy! Against legendary champions Brad Rutter and Ken Jennings, winning the first-place prize of $1 million! Watson was 16 terabytes of RAM through which it could process 500 gigabytes(1 million books) per second. Content was stored in Watson’s RAM rather than in memory for easy access and it cost about $3 million. https://www.youtube.com/watch?v=P18EdAKuC1U

14. Types of ai Narrow AI (ANI): Artificial Narrow Intelligence (ANI) also known as “Weak” AI is the AI that exists in our world today. Narrow AI is AI that is programmed to perform a single task — whether it’s checking the weather, being able to play chess, or analyzing raw data to write journalistic reports. ANI systems can attend to a task in real-time, but they pull information from  a specific data-set . Narrow AI operates within a pre-determined, pre-defined range, even if it appears to be much more sophisticated than that. Every sort of machine intelligence that surrounds us today is Narrow AI. Google Assistant, Google Translate, Siri and other natural language processing tools are examples of Narrow AI. 

15. Activity 2 Identify examples of Narrow and Broad AI from your daily-life application. Narrow Intelligence General Intelligence Beat Go World Champions Understand abstract concepts Read facial expressions Explain why? Write music Be creative like children Diagnose Mental Disorders Tell right from wrong Comfort Earthquake Survivors Have emotions

16. WEAK AI can handle specific tasks scenarios very good at one kind of problem Examples: Siri/Alexa/Google Home- can listen to talk and understand what you are saying, but can’t make any inferences, just reads off Google search results. Postal service zip code reader for India will always look for 6 handwritten digits, the logic would have to be completely redesigned to work in Canada which includes 3 letters alternating with 3 digits.

17. Strong AI Strong AI is generalized – it can generalize its knowledge to solve more than one kind of problem. Intellectual strength, not physical strength Displays human-like behavior Examples: Can guess answers to questions, give you ideas based on past understanding. Postal service zip code reader can adapt from 5 digits to 3 digits and letters almost immediately.

18. Automated and autonomous processing Automated System Autonomous System It is deterministic in nature. IF this THEN that. It is probabilistic in nature which means that it is based on inputs, situation and context. Will always produce same answer /response for a specific query. Can give different answers/output to the same set of inputs based on the context Most Computers and algorithms work on this approach. The human brain works in a similar way as an autonomous system and most strong AI systems would be like a fully autonomous decision-making system to augment human intelligence.

19. Activity 3 How does a Driverless Car Work ? List down various technologies and sensors being used in a self-driving car. How do you think AI is being used here? Is this narrow or general AI? Strong or Weak AI? https://www.youtube.com/watch?v=taMP_n3wL7M

20. Fun time Did you know Google Assistant came close to passing the Turing Test. See how Google passes the Turing Test: https://www.youtube.com/watch?v=JvbHu_bVa_g

21. Activity 4 Rock, Paper and Scissors https://www.afiniti.com/corporate/rock-paper-scissors Analyze the game with larger perspective.

22. Experiencing ai in real life In 1996, ELIZA was the first natural language programming (NLP)-based conversation program described by Joseph Weizenbaum . It featured a conversation between a human user and a computer program representing a mock psychotherapist. What does ELIZA do? It used algorithms such as pattern matching to give canned responses that made user feel they were talking to someone who understood their input and was like human counsellor. There were essentially scripts and IF THEN logics built into it. Many people call such systems as “Expert Systems”

23. DID you know? Sophia was the first bot that was recently offered citizenship by the kingdom of Saudi Arabia! https://www.youtube.com/watch?v=IsFv_gKS3YE

24. benefits of AI AI would have a low error rate compared to humans, if coded properly. They would have incredible precision, accuracy, and speed. They won't be affected by hostile environments, thus able to complete dangerous tasks, explore in space, and endure problems that would injure or kill us. -This can even mean mining and digging fuels that would otherwise be hostile for humans. Replace humans in repetitive, tedious tasks and in many laborious places of work. Predict what a user will type, ask, search, and do. They can easily act as assistants and can recommend or direct various actions. -An example of this can be found in the smartphone.

25. benefits of AI Can detect fraud in card-based systems, and possibly other systems in the future. Interact with humans for entertainment or a task as avatars or robots. - An example of this is AI for playing many videogames. They can think logically without emotions, making rational decisions with less or no mistakes. They don't need to sleep, rest, take breaks, or get entertained, as they don't get bored or tired.

26. Limitations of AI Data Availability : Any AI system needs training data to start and then test data for ensuring that it has learnt properly. Data can be in the form of images, audio or video which poses a bog challenge in interpreting and using it in AI systems. It also heavy in size and comes in multiple formats and quality.  Bias: Human being often have bias about certain things. It could be reasonable or unreasonable. When algorithms are developed by humans, their bias also sometimes creeps into the AI system and becomes its bias. Emotional Intelligence : AI uses Natural Language Processing to understand the intent of conversation but can’t understand the tone and subtle non-verbal cues well.

27. Limitations of AI HIGH COST OF IMPLEMENTATION: Setting up AI-based machines, computers, etc. entails huge costs given the complexity of engineering that goes into building one. Further, the astronomical expense doesn’t stop there as repair and maintenance also run into thousands of dollars.  LACKS CREATIVITY : AI’s creativity is limited to the creative ability of the person who programs and commands them. Although they can help you in designing and creating something special, they still can’t compete with the human brain.

28. AI Components

29. Data identification and collection Data is the starting point for all AI applications. These data sets can be numeric (sales, insurance premium, weather data etc.) Categorical (color, gender etc.), even unstructured free text (comments, audio, images, videos, notes, feedback). Data collection is the process of identifying various sources of data(structured and unstructured), collecting data and preparing to label it. We need to make sure that data collected is in the correct format and aligned with project requirements. It starts with having basic hardware, sensors and devices in places to capture the data required for our AI model. Next stage is having right storage system having IT infrastructure (servers, cloud storage etc.) and systems like ERP, MIS can keep this data.

30. We then need to do data cleaning to ensure that right data in correct format is available to run any analytics or data science models on it. The next step is to run data visualization models, classification of data, data labelling and defining some analytics metrics for identified set of data. The final stage is to apply Machine Learning algorithms to identify patterns and forecast future trends on data. We need to do A/B testing to iterate on the model. Companies like Google, Amazon and Facebook are dominating their industries because they were the first begin building data sets. Their data set have become so large and complicated and their data collection and analysis is so sophisticated that they are able to grow it to their competitive advantage.

31. Computer vision  Computer Vision is a subset of AI that lets machine see and extract meaning from pixels in an image. CV aims to mirror how human vision works and interpret things we see. Deep learning can work hand in hand with CV creating powerful systems such as searching images in Google, tagging of friends in social media, apps which can create a future aged version of your face, speech-to-text translation, intrusion detection system. Computer Vision (CV) has been around for over 50 years. Its development began in 1950’s around the same time when artificial intelligence gained prominence. Some CV applications include self-driving cars, facial recognition-based tracking systems with vision cameras and Amazon Go. https://www.youtube.com/watch?v=NrmMk1Myrxc

32. Amazon go Amazon Go is a new kind of store with no checkout required. Amazon claims it to be world’s most advanced shopping technology as you never have wait in line. With the Just walk out shopping experience, simply use the Amazon Go app to enter the store, take the products you want, and go! No lines, no checkouts. The key underlying technology in these amazing use cases is computer vision.

33. Natural language processing Natural Language Processing (NLP) is the technology used to aid computers to understand the human’s natural language such as English. Processing of the natural language is required when an intelligent machine needs to perform some actions based on instructions given by you. It is a subfield of AI which help design systems on how to process and analyze large amounts of natural language data. For example, If you are talking to Alexa, it needs to understand your language, words, context and emotion as well. NLP is the technology which enables Alexa to accomplish this task.

34. Natural language processing Applications of NLP Translation tools such as Google Translate, Microsoft Translator Document processors such as Microsoft Word and Grammarly that employ NLP to check grammatical, semantic errors and plagiarism to check accuracy of texts. Standard interactive voice response (IVR) applications used in call centers to handle support queries. Personal assistant applications such as Google Assistant, Siri and Alexa.

35. ARTIFICAL INTELLIGENCE MACHINE LEARNING DEEP LEARNING https://experiments.withgoogle.com/ai/giorgio-cam/view/ Emerging AI Technologies

36. https://www.youtube.com/watch?v=VwVg9jCtqaU&feature=emb_rel_end Purpose: Classification of Models into Rule-based approach and Learning approach.  Say: “In general, there are two approaches taken by researchers when building AI models. They either take a rule-based approach or learning approach.  A Rule based approach is generally based on the data and rules fed to the machine, where the machine reacts accordingly to deliver the desired output.  Under learning approach , the machine is fed with data and the desired output to which the machine designs its own algorithm (or set of rules) to match the data to the desired output fed into the machine”

37. As you can see in the Venn Diagram, Artificial Intelligence is the umbrella terminology which covers machine and deep learning under it and Deep Learning comes under Machine Learning. It is a funnel type approach where there are a lot of applications of AI out of which few are those which come under ML out of which very few go into DL.

38. Machine learning and deep learning Machine Learning enables a machine to “recognize” and “learn” the patterns in the training set of data. The machine learns by looking for patterns from the training data set and then builds a model. ML, enables machines to improve at tasks with experience. The machine learns from its mistakes and takes them into consideration in the next execution. It improvises itself using its own experiences. DL, enables software to train itself to perform tasks with vast amounts of data. In deep learning, the machine is trained with huge amounts of data which helps it into training itself around the data. Such machines are intelligent enough to develop algorithms for themselves.