Python AI Tutorial | Artificial Intelligence Programming Python

1. Artificial Intelligence Programming Python

2. Contents 1. What is Artificial Intelligence?....................................................................................................1 2. Problems in AI.............................................................................................................................1 a. Reasoning and Problem Solving.................................................................................................2 b. Knowledge Representation.........................................................................................................2 c. Planning.......................................................................................................................................2 d. Learning......................................................................................................................................3 e. Natural Language Processing......................................................................................................3 f. Perception....................................................................................................................................3 g. Motion and Manipulation............................................................................................................3 h. Social Intelligence.......................................................................................................................3 i. General Intelligence.....................................................................................................................3 3. Approaches to Artificial Intelligence...........................................................................................3 a. Cybernetics and Brain Simulation...............................................................................................4 b. Symbolic.....................................................................................................................................4 c. Sub-Symbolic .............................................................................................................................. 4 d. Statistical Learning ..................................................................................................................... 4 4. Python AI Tutorial - Artificial Intelligence Tools.........................................................................4 a. Search and Optimization.............................................................................................................5 b. Logic...........................................................................................................................................5 c. Probabilistic Methods for Uncertain Reasoning.........................................................................5 d. Classifiers and Statistical Learning Methods..............................................................................5 e. Artificial Neural Networks..........................................................................................................6 f. Evaluating Progress.....................................................................................................................6 5. Python AI Tutorial - Applications of Artificial Intelligence.........................................................6

3. Python AI Tutorial Artificial Intelligence With <, Python 1.What is Artificial Intelligence? Artificial Intelligence, often dubbed AI, is the intelligence a machine demonstrates. With machine intelligence, it is possible to give a device the ability to discern its environment and act to maximize its chances of success in achieving its goals. In other words, AI is when a machine can mimic cognitive functions like learning and problem-solving. “AI is whatever hasn’t been done yet.” As we said, an AI takes in its environment and acts to maximize its chances of success in achieving its goals. A goal can be simple or complex, explicit or induced. It is also true that many algorithms in AI can learn from data, learn new heuristics to improve and write other algorithms. One difference to humans is that AI does not possess the features of human commonsense reasoning and folk psychology. This makes it end up making different mistakes than a human would. 2.Problems in AI When simulating or creating AI, we may run into problems around the following traits- 1 https://data-flair.training/big-data-hadoop/

4. Python AI Tutorial ^|Data EMFUir General Intelligence Problems in AI Social Intelligence Reasoning and Problem Solving Learning Perception Natural Language . Processing Motion and Manipulation Knowledge Representation a.Reasoning and Problem Solving Earlier, algorithms mimicked step-by-step reasoning that humans display. AI research later introduced methods to work with incomplete and uncertain information. However, as the problems grew larger, these algorithms became exponentially slower. Humans often use fast, intuitive judgments instead of a step-by-step deduction. b.Knowledge Representation Some expert systems accumulate esoteric knowledge from experts. A comprehensive commonsense knowledge base holds many things including- objects, properties, categories, relations between objects, situations, events, states, time, causes, effects, knowledge about knowledge, and other domains. When we talk about ontology, we talk about what exists. Under knowledge representation, we observe the following domains- • Default reasoning; Qualification problem • Breadth of commonsense knowledge • Subsymbolic form of some commonsense knowledge c.Planning An intelligent agent should be capable of setting goals, achieving them, and visualizing the future. Assuming it is the only system in the world, an agent can be certain of their actions’ consequences. If there are more actors, the agent should be able to reason under uncertainty. For this, it should be able to assess its environment, make predictions, evaluate predictions, and adapt according to its assessment. With multi-agent planning, we observe multiple agents cooperate and compete to achieve a goal. 2 https://data-flair.training/big-data-hadoop/

5. Python AI Tutorial d.Learning AI is related to Machine Learning in some way. We have often talked about unsupervised learning- the ability to take a stream of input and find patterns in it. This includes classification and numerical regression. We classify things into categories and produce a function that describes how inputs and outputs relate and change each other. These function approximators. e.Natural Language Processing NLP is an area of Computer Science that gives machines the ability to read the human language and understand it. With it, we can retrieve information, mine text, answer questions, and translating using machines. We use strategies like keyword spotting and lexical affinity. f.Perception With machine perception, we can take input from sensors like cameras, microphones, and lidar to recognize objects. We can use it for applications like speech recognition, facial recognition, and object recognition. We can also analyze visual input with Computer Vision. g.Motion and Manipulation With AI, we can develop advanced robotic arms and more for modern factories. These can use the experience to learn to deal with friction and gear slippage. The term Motion Planning means dividing a task into primitives like individual joint movements. h.Social Intelligence “Should I go to bed, Siri?”, I ask as I reach home from a busy day. “I think you should sleep on it”, Siri quips back. Affective Computing, an umbrella term, encompasses systems that can recognize, interpret, process, or simulate human affects/ emotions. In this domain, we have observed textual sentiment analysis and multimodal affect analysis. The aim is to allow AI to understand others’ motives and emotional states to predict their actions. It can mimic human emotion and expressions to appear sensitive and interact with humans. A robot with rudimentary social skills is Kismet, developed at MIT by Dr. Cynthia Breazeal. i.General Intelligence Lately, many AI researchers have begun working on tractable narrow AI applications like a medical diagnosis. The future could hold machines with Artificial General Intelligence(AGI) that combines such narrow skills. Google’s DeepMind will be an example of this. 3.Approaches to Artificial Intelligence We observe four different approaches to AI- 3 https://data-flair.training/big-data-hadoop/

6. Python AI Tutorial Python AI Tutorial - AI Approaches a.Cybernetics and Brain Simulation Some machines exist that use electronic networks to display rudimentary intelligence. b.Symbolic This approach considers reducing human intelligence to symbolic manipulation. This includes cognitive simulation, logic-based, anti-logic or scruffy, and knowledge-based approaches. c.Sub-Symbolic For processes of human cognition like perception, robotics, learning, and pattern recognition, sub-symbolic approaches came into picture. This includes approaches like embodied intelligence and computational intelligence and soft computing. d.Statistical Learning Statistical learning techniques like HMM and neural networks deliver better accuracy in practical domains like data mining. Limitations of HMM include that it cannot model the infinite possible combinations of natural language. 4.Python AI Tutorial - Artificial Intelligence Tools For AI, we have the following tools- 4 https://data-flair.training/big-data-hadoop/

7. Python AI Tutorial >«i^Data ( jf Flair Artificial Intelligence Tools Python AI Tutorial - Artificial Intelligence Tools a.Search and Optimization To intelligently search through possible solutions and use reasoning to do so is a tool for AI. For real-world problems, simple exhaustive searches rarely suffice. This is because these have really large search spaces. This leads to a slow search or one that never ends. To get around this, we can use heuristics. b.Logic AI research uses different forms of logic. Propositional logics use truth functions like ‘or’ and ‘not’. The fuzzy set theory holds a degree of truth (values between 0 and 1) to vague statements. First-order logic adds quantifiers and predicates.Fuzzy logichelps with control systems to contribute vague rules. c.Probabilistic Methods for Uncertain Reasoning We often use tools like Bayesian networks for reasoning, learning, planning, and perception. We can also use probabilistic algorithms to filter, predict, smoothen, and explain streams of data. d.Classifiers and Statistical Learning Methods Classifiers and controllers work together. Consider an object. If it is shiny, the classifier knows it is a diamond. If it is shiny, the controller picks it up. But before inferring an action, a controller classifies conditions. As a function, a classifier matches patterns to find the closest match. Supervised learning puts each pattern into a predefined class. 5 https://data-flair.training/big-data-hadoop/

8. Python AI Tutorial e.Artificial Neural Networks ANNsare collections of nodes that are interconnected- inspired by the huge network of neurons in the human brain. Python AI Tutorial - Artificial Neural Networks Under these, we have categories like feedforward neural networks and recurrent neural networks. We will take up ANNs as a separate topic in another tutorial. f.Evaluating Progress Since AI is general purpose, there is no way to find out which domains it excels in. Games are a good benchmark to assess progress. Some of these include AlphaGo and StarCraft. 5.Python AI Tutorial - Applications of Artificial Intelligence Like we said, AI is pretty general-purpose. Here are a few domains it finds use in- • Automotive • Healthcare • Video games 6 https://data-flair.training/big-data-hadoop/

9. Python AI Tutorial • Military • Finance and Economics • Art • Auditing • Advertising So, this was all in Python AI Tutorial. Hope you like our explanation. Next step to follow •Copy in Python - Shallow Copy and Deep Copy •NLTK Python Tutorial (Natural Language Toolkit) •Python Assert Statements | Assertion Error in Python •Python Speech Recognition - Artificial Intelligence 7 https://data-flair.training/big-data-hadoop/