Artificial intelligence (AI) involves creating intelligent machines that can perform tasks requiring human intelligence. This document discusses the history and development of AI including conventional AI methods like expert systems and computational intelligence methods like neural networks. It provides examples of applications of AI like game playing, speech recognition, natural language processing, computer vision, and expert systems. Overall, the document provides a comprehensive overview of the field of AI, its methods and applications.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence
Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence
Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
Research about artificial intelligence (A.I)Alị Ŕỉźvị
These slides contains no extra ordinary textual information but they are very good as being creative. relevant to the topic animations were added which guarantee not to make the audience bore.
Artificial intelligence (AI),the intelligence exhibited by machines or software, is an oxymoron in itself.Reason being that we homosapiens, struggle to comprehend the reasons behind our own consciousness, more often than not turning to the metaphysical for answers. So we can’t really expect this to be created at the hands of humanity anytime soon.However the recent developments in AI have been quite promising: Microsoft launched Kinect for Xbox 360,the first gaming device to track human body movement and Narrative Science successfully created a computer program ‘Quill’ that analyses numerical data such as sports scores or financial earnings to write a news story Hence in this paper, AI is seen with a new perspective and it’s present day scenario and future implications in terms of consciousness ,are discussed.
Artificial intelligence - (A seminar on Emerging Trends of Technology) ileomax
This presentation got the first prize for the competition at a seminar Emerging Trends of Technology @ MGM Indore.
Please leave your comments if you like/unlike it..
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
An outline of the Pattie Maes, MIT Media-Laboratory Article, Behavior-Based Artificial Intelligence. Presented for CS 493 - Advanced Topics in Robotics.
Abstract Generally, computer system is handled by the English language only. But the person who is unaware of the English language and structure of query language cannot handle the system. This paper proposed a new approach for accessing the database easily without knowing English. So, the database is accessed with the help of natural languages such as Hindi, Marathi etc. Natural language processing (NLP) is the study of mathematical and computational modeling of various aspects of language and the development of a wide range of systems. Natural Language Processing holds great promise for making computer interfaces that are easier to use for people, since people will be able to talk to the computer in their own language, rather than learn a specialized language of computer commands. Keywords: NLP, Mathematical modeling, Computational modeling
Tracxn Research — Artificial Intelligence Startup Landscape, September 2016Tracxn
Notable investments in 2016 include antivirus and endpoint protection vendor Cylance ($100M Series D), Digital Reasoning ($40M, Series D), and Globality, ($27M, Series B).
Research about artificial intelligence (A.I)Alị Ŕỉźvị
These slides contains no extra ordinary textual information but they are very good as being creative. relevant to the topic animations were added which guarantee not to make the audience bore.
Artificial intelligence (AI),the intelligence exhibited by machines or software, is an oxymoron in itself.Reason being that we homosapiens, struggle to comprehend the reasons behind our own consciousness, more often than not turning to the metaphysical for answers. So we can’t really expect this to be created at the hands of humanity anytime soon.However the recent developments in AI have been quite promising: Microsoft launched Kinect for Xbox 360,the first gaming device to track human body movement and Narrative Science successfully created a computer program ‘Quill’ that analyses numerical data such as sports scores or financial earnings to write a news story Hence in this paper, AI is seen with a new perspective and it’s present day scenario and future implications in terms of consciousness ,are discussed.
Artificial intelligence - (A seminar on Emerging Trends of Technology) ileomax
This presentation got the first prize for the competition at a seminar Emerging Trends of Technology @ MGM Indore.
Please leave your comments if you like/unlike it..
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
An outline of the Pattie Maes, MIT Media-Laboratory Article, Behavior-Based Artificial Intelligence. Presented for CS 493 - Advanced Topics in Robotics.
Abstract Generally, computer system is handled by the English language only. But the person who is unaware of the English language and structure of query language cannot handle the system. This paper proposed a new approach for accessing the database easily without knowing English. So, the database is accessed with the help of natural languages such as Hindi, Marathi etc. Natural language processing (NLP) is the study of mathematical and computational modeling of various aspects of language and the development of a wide range of systems. Natural Language Processing holds great promise for making computer interfaces that are easier to use for people, since people will be able to talk to the computer in their own language, rather than learn a specialized language of computer commands. Keywords: NLP, Mathematical modeling, Computational modeling
Tracxn Research — Artificial Intelligence Startup Landscape, September 2016Tracxn
Notable investments in 2016 include antivirus and endpoint protection vendor Cylance ($100M Series D), Digital Reasoning ($40M, Series D), and Globality, ($27M, Series B).
The ppt Sujoy and I made for the Psi Phi ( An Inter School Competition held by our School). Our Topic was Artificial Intelligence.
Credits:
Theme Images from ESET NOD32 (My Antivirus of Choice)
Backgrounds from SwimChick.net (Amazing designs here)
Credits Image from Full Metal Alchemist (One of my favorite Anime).
AI is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for the use of information), reasoning (using the rules to reach approximate or final conclusions) and self-correction. Particular applications of the AI include expert system speech recognition and artificial vision.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Artificial intelligence can be defined as the branch of computer science that is concerned with automation of computer system in an intelligent manner as like as humans.
Artificial Intelligence focuses on developing computer programs to solve complex problems and process.
What humans can do, now can be performed by machines too just, because of artificial intelligence.
AI is used because, it saves a whole lot of time and manpower.
The Revolution of Digital Marketing in the Artificial Intelligence eraMohamed Hanafy
In this course, we will explore the impact of artificial intelligence on the field of digital marketing. We will discuss how AI technologies such as machine learning, natural language processing, and predictive analytics are revolutionizing the way businesses approach marketing. We will also examine real-world examples of how companies are using AI to improve their marketing strategies and achieve better results.
Introduction to Artificial intelligence and MLbansalpra7
**Title: Understanding the Landscape of Artificial Intelligence: A Comprehensive Exploration**
**I. Introduction**
In recent decades, Artificial Intelligence (AI) has emerged as a transformative force, reshaping industries, influencing daily life, and pushing the boundaries of human capabilities. This comprehensive exploration delves into the multifaceted landscape of AI, encompassing its origins, key concepts, applications, ethical considerations, and future prospects.
**II. Historical Perspective**
AI's roots can be traced back to ancient history, where philosophers contemplated the nature of intelligence. However, it wasn't until the mid-20th century that AI as a field of study gained momentum. The influential Dartmouth Conference in 1956 marked the official birth of AI, with early pioneers like Alan Turing laying the theoretical groundwork.
**III. Foundations of AI**
Understanding AI requires grasping its foundational principles. Machine Learning (ML), a subset of AI, empowers machines to learn patterns and make decisions without explicit programming. Within ML, various approaches, such as supervised learning, unsupervised learning, and reinforcement learning, play crucial roles in shaping AI applications.
**IV. Types of Artificial Intelligence**
AI is not a monolithic entity; it spans a spectrum of capabilities. Narrow AI, also known as Weak AI, excels in specific tasks, like image recognition or language translation. In contrast, General AI, or Strong AI, would possess human-like intelligence across a wide range of tasks, a goal that remains a long-term aspiration.
**V. Applications of AI**
AI's impact is felt across diverse sectors. In healthcare, AI aids in diagnostics and personalized treatment plans. In finance, it enhances fraud detection and risk assessment. Self-driving cars exemplify AI in transportation, while virtual assistants like Siri and Alexa showcase its role in daily life. The convergence of AI with other technologies, such as the Internet of Things (IoT) and robotics, amplifies its transformative potential.
**VI. Machine Learning Algorithms**
The backbone of AI lies in its algorithms. Linear regression, decision trees, neural networks, and deep learning models are among the many tools in the ML toolkit. Exploring the mechanics of these algorithms reveals the intricacies of how AI processes information, learns from data, and makes predictions.
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...AI Publications
Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as a neural network. Use neural network in forecasts time series can be a good solution, but the problem is network architecture and the training method in the right direction. One of the choices that might be using a genetic algorithm. A genetic algorithm is a search algorithm stochastic resonance based on how it works by the mechanisms of natural selection and genetic variation that aims to find a solution to a problem. This algorithm can be used as teaching methods in train models are sent back propagation neural network. The application genetic algorithm and neural network for divination time series aim to get the weight optimum. From the training and testing on the data index share price euro 50 obtained by the RMSE testing 27.8744 and 39.2852 RMSE training. The weight or parameters that produced by has reached an optimum level in second-generation 1000 with the best fitness and the average 0.027771 the fitness of 0.0027847.Model is good to be used to give a prediction that is quite accurate information that is shown by the close target with the output.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
Artificial intelligence
1. ARTIFICIAL INTELLIGENCE
ABSTRACT:
Artificial intelligence is exhibited by artificial entity, a system is generally assumed to be
a computer. AI systems are now in routine use in economics, medicine, engineering and the
military, as well as being built into many common home computer software applications,
traditional strategy games like computer chess and other video games.
It cleared the concept of computational and conventional categories. It includes various
advanced systems such as Neural Network, Fuzzy Systems and Evolutionary computation. AI is
used in typical problems such as Pattern recognition, Natural language processing and more.
This system is working throughout the world as an artificial brain.
Intelligence involves mechanisms, and AI research has discovered how to make
computers carry out some of them and not others.
We can learn something about how to make machines solve problems by observing other
people or just by observing our own methods. On the other hand, most work in AI involves
studying the problems the world presents to intelligence rather than studying people or animals.
AI researchers are free to use methods that are not observed in people or that involve much more
computing than people can do.
Although AI has a strong science fiction connotation, it forms a vital branch of
computer science, dealing with intelligent behavior, learning and adaptation in machines.
Research in AI is concerned with producing machines to automate tasks requiring intelligent
behavior. Examples include control, planning and scheduling, the ability to answer diagnostic
and consumer questions, handwriting, speech, and facial recognition. As such, it has become a
scientific discipline, focused on providing solutions to real life problems. AI systems are now in
routine use in economics, medicine, engineering and the military, as well as being built into
many common home computer software applications, traditional strategy games like computer
chess and other video games.
CONTENT:
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2. History:
The intellectual roots of AI, and the concept of intelligent machines, may be
found in Greek mythology. Intelligent artifacts appear in literature since then, with real
mechanical devices actually demonstrating behaviour with some degree of intelligence. After
modern computers became available following World War-II, it has become possible to create
programs that perform difficult intellectual tasks. It was started from 1950 onwards.
Categories of AI:-
AI divides roughly into two schools of thought:
• Conventional AI.
• Computational Intelligence (CI).
• Conventional AI:-
It involves methods now classified as machine learning, characterized by
formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI
and Good Old Fashioned Artificial Intelligence (GOFAI).
Methods include:
• Expert systems: apply reasoning capabilities to reach a conclusion. An expert
system can process large amounts of known information and provide
conclusions based on them.
• Case based reasoning.
• Bayesian networks.
• Behavior based AI: a modular method of building AI systems by hand.
• Computational Intelligence (CI):-
It involves iterative development or learning (e.g. parameter tuning e.g. in
connectionist systems). Learning is based on empirical data and is associated with non-
symbolic AI, scruffy AI and soft computing.
Methods include:
• Neural networks: systems with very strong pattern recognition capabilities.
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3. • Fuzzy systems: techniques for reasoning under uncertainty, has been widely
used in modern industrial and consumer product control systems.
• Evolutionary computation: applies biologically inspired concepts such as
populations, mutation and survival of the fittest to generate increasingly better
solutions to the problem. These methods most notably divide into
evolutionary algorithms (e.g. genetic algorithms) and swarm intelligence (e.g.
ant algorithms).
Genetic Algorithms GA’s – What are they?
Genetic Algorithms are based on the principles of survival of the fittest; sometimes called
natural selection. GA’s, informally, work as follows.
Many potential solutions to the problem are created. Each solution is evaluated to see
how good it is. The best solutions are allowed to breed with each other.
This cycle continues in the hope that, as we are breeding with the good solutions, we will
gradually breed better and better solutions. And the evidence supports that this is the case for a
wide variety of problems. As GA’s have their origins in genetics the terms have carried through
into the computer counterparts.
Each solution is normally called a chromosome (or an individual). Each chromosome is
made up of genes, which are the individual elements that represent the problem. The collection
of chromosomes is called a population.
In a problem such as the travelling salesman problem (TSP) the genes would be
individual cites. A chromosome would be a complete tour and the population would be a number
of complete tours.
As stated above, GA’s work by breeding the best individuals from the current population.
Let’s consider this is a bit more detail.
It is common to have a population of one hundred. We’ll work with that figure for now.
The initial population of one hundred chromosomes is normally created randomly. Many
problems are represented as bit strings, but this need not be the case and other representations
have been used.
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4. The individuals that make up the population are then evaluated. That is, some function considers
each chromosome and assigns it a value depending on how good an answer it is to the problem.
The chromosomes are then allowed to breed. But what does this mean?
Firstly we need to choose suitable “parents.” Overall we want to choose the fittest
members of the population to breed but, as in life, everybody must be given the chance to breed.
Just because an individual has a low evaluation does not mean he/she does not have something to
contribute to society.
Therefore, in choosing parents, it is normally done in such a way that they are chosen in
proportion to their evaluation rating. In doing this the fitter individuals are more likely to breed
but the weaker members of the population also have the opportunity.
Having chosen our parents we now allow them to breed. This means (normally) two
offspring are produced from the two parents. The children consist of genetic material taken from
both parents. How the genetic material is distributed can be done in a number of ways, which we
discuss below.
However, we do not breed all the time. There is a probability associated with each
breeding pair as to whether they produce children or not. The probability of breeding is usually
set to something like sixty percent but other figures are often experimented with.
Occasionally, as in life, a mutation occurs and GA’s also mimic this.
Mutation happens with low probability and how the mutation occurs depends on the coding that
is being used. If the problem is being represented by bit strings then mutation is fairly easy to
implement. It can simply look at each bit in the chromosome and decide (with some low
probability) if the bit should be replaced with a randomly produced bit.
The last point that should be made about GA’s is that they have no knowledge about the
problem it is trying to solve. The only part of the GA that has some domain knowledge is the
evaluation function. This function is given a chromosome and passes back an evaluation for the
chromosome. It is this evaluation rating that the breeding mechanism uses in deciding which
chromosomes should breed.
The breeding mechanism has no knowledge about the problem. In its simplest form a GA
is just manipulating bit strings.
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5. GA Algorithm:
A Genetic Algorithm can be implemented using the following outline algorithm
1. Initialize a population of chromosomes
2. Evaluate each chromosome (individual) in the population
2.1. Create new chromosomes by mating chromosomes in the current population (using
crossover and mutation)
2.2. Delete members of the existing population to make way for the new members
2.3. Evaluate the new members and insert them into the population
3. Repeat stage 2 until some termination condition is reached (normally based on time or
number of populations produced)
4. Return the best chromosome as the solution.
This is the basic GA algorithm. As we shall see below there are many parameters that we can
use to affect this basic algorithm.
Typical problems to which AI methods are applied:-
• Pattern recognition
o Optical character recognition
o Handwriting recognition
o Speech recognition
o Face recognition
• Natural language processing, Translation and Chatter bots
• Non-linear control and Robotics
• Computer vision, Virtual reality and Image processing
• Game theory and Strategic planning.
Other fields in which AI methods are implemented:-
• Automation.
• Cybernetics.
• Hybrid intelligent system.
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6. • Intelligent agent.
• Intelligent control.
• Automated reasoning.
• Data mining.
• Behavior-based robotics.
• Cognitive robotics.
• Developmental robotics.
• Evolutionary robotics.
• Chatbot.
• Knowledge Representation.
Applications of AI:-
1. Game Playing:-
You can buy machines that can play master level chess for a few hundred dollars.
There is some AI in them, but they play well against people mainly through brute force
computation--looking at hundreds of thousands of positions.
2. Speech Recognition:-
In the 1990s, computer speech recognition reached a practical level for limited
purposes. Thus United Airlines has replaced its keyboard tree for flight information by a
system using speech recognition of flight numbers and city names. It is quite convenient.
On the other hand, while it is possible to instruct some computers using speech, most
users have gone back to the keyboard and the mouse as still more convenient.
3. Understanding Natural Language:-
Just getting a sequence of words into a computer is not enough. Parsing sentences
is not enough either. The computer has to be provided with an understanding of the
domain the text is about, and this is presently possible only for very limited domains.
4. Computer Vision:-
The world is composed of three-dimensional objects, but the inputs to the human
eye and computer’s TV cameras are two dimensional. Some useful programs can work
solely in two dimensions, but full computer vision requires partial three-dimensional
information that is not just a set of two-dimensional views. At present there are only
6
7. limited ways of representing three-dimensional information directly, and they are not as
good as what humans evidently use.
5. Expert Systems:-
A knowledge engineer'' interviews experts in a certain domain and tries to
embody their knowledge in a computer program for carrying out some task. How well
this works depends on whether the intellectual mechanisms required for the task are
within the present state of AI. One of the first expert systems was MYCIN in 1974, which
diagnosed bacterial infections of the blood and suggested treatments. It did better than
medical students or practicing doctors, provided its limitations were observed.
REFERENCES:
• www.whatis.com
• www.wikipedia.com
• www.extremetech.com
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