AI ML Unit-1 in machine learning techniques.pptx.
- 1. Unit-1 Fundamentals of Artificial Intelligence
- 2. AI The simulation of human intelligence in machines that are programmed to think and act like humans. It involves the development of algorithms and computer programs for visual perception, speech recognition, decision-making, and language translation.
- 5. Maturation of Artificial Intelligence (1943-1952) Year 1943: artificial neurons. Year 1949: Donald Hebb demonstrated an updating rule for modifying the connection strength between neurons. His rule is now called Hebbian learning. Year 1950: The Alan Turing publishes "Computing Machinery and Intelligence" in which he proposed a test. The test can check the machine's ability to exhibit intelligent behavior equivalent to human intelligence, called a Turing test.
- 6. The birth of Artificial Intelligence (1952-1956) Year 1955: first artificial intelligence program Which was named as "Logic Theorist". This program had proved 38 of 52 Mathematics theorems, and find new and more elegant proofs for some theorems. Year 1956: For the first time, AI coined as an academic field.
- 7. The golden years-Early enthusiasm (1956-1974) Year 1966: The researchers emphasized developing algorithms which can solve mathematical problems. Joseph Weizenbaum created the first chatbot in 1966, which was named as ELIZA. Year 1972: The first intelligent humanoid robot was built in Japan which was named as WABOT-1.
- 8. The first AI winter (1974-1980) A boom of AI (1980-1987) - Year 1980: After AI winter duration, AI came back with "Expert System".
- 9. The emergence of intelligent agents (1993-2011) Year 1997: In the year 1997, IBM Deep Blue beats world chess champion, Gary Kasparov, and became the first computer to beat a world chess champion. Year 2002: for the first time, AI entered the home in the form of Roomba, a vacuum cleaner. Year 2006: AI came in the Business world till the year 2006. Companies like Facebook, Twitter, and Netflix also started using AI.
- 10. Deep learning, big data and artificial general intelligence (2011-present) Year 2011: Watson had proved that it could understand natural language and can solve tricky questions quickly. Year 2012: Google has launched an Android app feature "Google now", which was able to provide information to the user as a prediction. Year 2014: In the year 2014, Chatbot "Eugene Goostman" won a competition in the infamous "Turing test.“ Year 2018: The "Project Debater" from IBM debated on complex topics with two master debaters and also performed extremely well.
- 11. Types of AI
- 12. Weak AI or Narrow AI Able to perform a dedicated task with intelligence. cannot perform beyond its field or limitations, as it is only trained for one specific task. Some Examples of Narrow AI are playing chess, purchasing suggestions on e-commerce site, self-driving cars, speech recognition, and image recognition.
- 14. General AI Can perform any intellectual task with efficiency like a human. The idea behind the general AI to make such a system which could be smarter and think like a human by its own.
- 15. General AI
- 16. Super AI Super AI is a level of Intelligence of Systems at which machines could surpass human intelligence, and can perform any task better than human with cognitive properties. Some key characteristics of strong AI include capability include the ability to think, to reason,solve the puzzle, make judgments, plan, learn, and communicate by its own.
- 18. Reactive Machines Purely reactive machines are the most basic types of Artificial Intelligence. Such AI systems do not store memories or past experiences for future actions. These machines only focus on current scenarios and react on it as per possible best action. IBM's Deep Blue system, Google's AlphaGo is an example of reactive machines.
- 20. Limited Memory Limited memory machines can store past experiences or some data for a short period of time. These machines can use stored data for a limited time period only. Self-driving cars are one of the best examples of Limited Memory systems. These cars can store recent speed of nearby cars, the distance of other cars, speed limit, and other information to navigate the road.
- 24. Theory of Mind Theory of Mind AI should understand the human emotions, people, beliefs, and be able to interact socially like humans. This type of AI machines are still not developed, but researchers are making lots of efforts and improvement for developing such AI machines.
- 26. Self-Awareness Self-awareness AI is the future of Artificial Intelligence. These machines will be super intelligent, and will have their own consciousness, sentiments, and self-awareness.
- 28. AI ethics and limitation 1. Unemployment. What happens after the end of jobs? 2. Inequality. How do we distribute the wealth created by machines? 3. Humanity. How do machines affect our behaviour and interaction? 4. Artificial stupidity. How can we guard against mistakes?
- 29. 5. Racist robots. How do we eliminate AI bias? 6. Security. How do we keep AI safe from adversaries? 7. Evil genies. How do we protect against unintended consequences? 8. Singularity. How do we stay in control of a complex intelligent system?
- 30. Artificial Intelligence Areas Expert systems Neural Networks Natural Language Processing Fuzzy Logic Systems Robotics Machine Learning
- 32. Expert Systems Model of human experts like doctors, lawyers, engineers, scientists, carpenters, musicians, It mimics the decision-making intelligence of a human expert. It conducts this by deriving knowledge from its knowledge base by implementing reasoning and insights rules in terms with the user queries.
- 33. Expert Systems More the information collected in it, the more the system enhances its efficiency.
- 34. Expert Systems
- 36. Applications of Expert System In the knowledge domain In the finance domain In the diagnosis Planning and Scheduling
- 37. Neural Network Makes use of neurology Inspired by how the brain is composed with large number of Neurons and Connections. Neurons work as processors and Connections work as memories.
- 38. Neural Network A neuron in a neural network is a mathematical function (such as activation functions) whose work is to gather and classify information according to a particular structure, the network strongly implements various statistical techniques, such as regression analysis, to accomplish tasks.
- 42. Natural Language Processing Focuses on the interaction between computers and human language. Enable machines to understand, interpret, and generate text or speech in a way that is both meaningful and useful.
- 44. Natural Language Processing Common example of NLP is spam detection, computer algorithms can check whether an email is a junk or not by looking at the subject of a line, or text of an email. Twitter uses NLP technique to filter terroristic language from various tweets, Amazon implements NLP for interpreting customer reviews and enhancing their experience.
- 45. Applications of NLP (1) Machine Translation
- 46. Applications of NLP (2) Sentiment Analysis
- 47. Applications of NLP (3) Chatbots
- 48. Applications of NLP (4) Text Summarization
- 49. Fuzzy Logic Systems Technique that represents and modifies uncertain information by measuring the degree to which the hypothesis is correct. If the concept is completely true, standard logic is 1.0 and 0.0 for the completely false concept. But in fuzzy logic, there is also an intermediate value too which is partially true and
- 52. Application of Fuzzy Logic Systems Defence - underwater target recognition and the automatic target recognition of thermal infrared images. Pattern Recognition and Classification - used in the searching of fuzzy images. Microwave oven - setting the lunes power and cooking strategy. Finance - predicting the stock market, and for managing the funds. Automobiles - It is also used for controlling the brakes. Chemical Industries - for controlling the ph, and chemical distillation process. Household Items -It is also used in the vacuum cleaners, and the timings of washing machines, heaters, air conditioners, and humidifiers.
- 53. Robotics Bridges the gap between the digital and physical worlds and integrates AI with mechanical design and engineering. Robots are deployed often for conducting tasks that might be laborious for humans to perform steadily.
- 54. Difference in Robot System and Other AI Program
- 55. Applications of Robotics Industries handling material, cutting, welding, color coating, − drilling, polishing, etc. Military Can reach inaccessible and hazardous zones during − war. Medicine Carrying out hundreds of clinical tests − simultaneously, rehabilitating permanently disabled people, and performing complex surgeries such as brain tumors. Exploration The robot rock climbers used for space − exploration, underwater drones used for ocean exploration Entertainment Disney,s engineers have created hundreds of − robots for movie making.
- 57. AI application in various indsutries
- 58. AI in healthcare
- 59. AI in social media
- 61. AI in Automobile
- 62. AI in Gaming
- 63. AI in finance
- 64. References https://www.analyticssteps.com/blogs/6- major-branches-artificial-intelligence-ai https://databasetown.com/7-main-areas- of-artificial-intelligence-ai/ https://medium.datadriveninvestor.com/ major-areas-of-artificial-intelligence-74f41 917453 Neural Network : https://www.youtube.com/watch?v=aircAr uvnKk https://www.javatpoint.com/fuzzy-logic