The document discusses artificial intelligence (AI) and pattern recognition. It defines AI as the intelligence demonstrated by machines, and the branch of computer science which creates it. Pattern recognition is described as assigning labels or classifications to input values based on identifying patterns. The history of AI from its origins in the 1950s is briefly outlined, along with major branches like logical AI, planning, and applications like game playing, speech recognition, robotics, and computer vision.
This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
Artificial intelligence (AI) is the human-like intelligence exhibited by machines or software. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology. Major AI researchers and textbooks define the field as "the study and design of intelligent agents",[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.[2] John McCarthy, who coined the term in 1955,[3] defines it as "the science and engineering of making intelligent machines".[4]
AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence (or "strong AI") is still among the field's long term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.
Deep Learning: Towards General Artificial IntelligenceRukshan Batuwita
For the past several years Deep Learning methods have revolutionized the areas in Pattern Recognition, namely, Computer Vision, Speech Recognition, Natural Language Processing etc. These techniques have been mainly developed by academics, closely working with tech giants such as Google, Microsoft and Facebook where the research outcomes have been successfully integrated into commercial products such as Google image and voice search, Google Translate, Microsoft Cortana, Facebook M and many more interesting applications that are yet to come. More recently, Google DeepMind Technologies has been working on Artificial General Intelligence using Deep Reinforcement Learning methods, where their AlphaGo system beat the world champion of the complex Chinese game 'Go' in March 2016. This talk will present a thorough introduction to major Deep Learning techniques, recent breakthroughs and some exciting applications.
This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
Artificial intelligence (AI) is the human-like intelligence exhibited by machines or software. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology. Major AI researchers and textbooks define the field as "the study and design of intelligent agents",[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.[2] John McCarthy, who coined the term in 1955,[3] defines it as "the science and engineering of making intelligent machines".[4]
AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence (or "strong AI") is still among the field's long term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.
Deep Learning: Towards General Artificial IntelligenceRukshan Batuwita
For the past several years Deep Learning methods have revolutionized the areas in Pattern Recognition, namely, Computer Vision, Speech Recognition, Natural Language Processing etc. These techniques have been mainly developed by academics, closely working with tech giants such as Google, Microsoft and Facebook where the research outcomes have been successfully integrated into commercial products such as Google image and voice search, Google Translate, Microsoft Cortana, Facebook M and many more interesting applications that are yet to come. More recently, Google DeepMind Technologies has been working on Artificial General Intelligence using Deep Reinforcement Learning methods, where their AlphaGo system beat the world champion of the complex Chinese game 'Go' in March 2016. This talk will present a thorough introduction to major Deep Learning techniques, recent breakthroughs and some exciting applications.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
Get to Know about Deep Learning and it's timeline and what is the difference between M.L,D.L and A.I. how D.L is commercially used some startups using deep learning
Webinar on AI in IoT applications KCG Connect Alumni Digital Series by RajkumarRajkumar R
The Artificial Intelligence in IoT Applications. Take your first step towards a bright future with our renowned alumnus,
Prof R. Raj Kumar on AI for IoT Applications.
He is an award wining author of the book, ‘India 2030’.
To get access to the webinar kindly contact your respective department heads.
Looking forward to having you on the webinar.
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#KCGCollege #KCGStudentlife #KCGConnect #Education #EmergingTechnologies #ArtificialIntelligence #IoT #MachineLearning #BlockChain #ElectricVehicle #QuantumTechnology #CAD
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
3. CONTENTS
INTRODUCTION
WHAT IS ARTIFICIAL INTELLIGENCE?
WHAT IS PATTERN RECOGNITION?
HISTORY OF AI AND CONTRIBUTION OF
JOHN Mc CARTHY.
THE TEST THAT CHANGED AI FOR ONCE AND
FOR ALL.
BRANCHES OF AI.
APPLICATIONS OF AI.
CONCLUSION.
4. WHAT IS AI
• Artificial intelligence (AI) is
the intelligence of machines and
the branch of computer
science that aims to create it.
• It is also defined as "the study
and design of intelligent agents“
where an intelligent agent is a
system that perceives its
environment and takes actions
that maximize its chances of
success.
• John McCarthy, who coined the
term in 1955, defines it as "the
science and engineering of
making intelligent machines."
5. DEFINITIONS :
• AI is a branch of computer science
dealing with symbolic, non algorithmic
methods of problem solving
• AI is a branch of computer science that
deals with ways of knowledge using
symbols rather than numbers and with
Heuristics, method for processing
information.
• AI works with pattern matching methods
which attempt to describe objects ,
events or processes in terms of their
qualitative features and logical and
computational Relationship.
6. What is Pattern Recognition
• In machine learning, pattern
recognition is the assignment
of a label to a given input
value. An example of pattern
recognition is classification,
which attempts to assign each
input value to one of a given
set of classes (for example,
determine whether a given
email is "spam" or "non-
spam").
• However, pattern recognition is
a more general problem that
encompasses other types of
output as well.
7. • Other examples are regression,
which assigns a real-valued
output to each input;
sequence labeling, which assigns
a class to each member of a
sequence of values (for
example, part of speech tagging,
which assigns a part of speech to
each word in an input sentence);
and
parsing, which assigns a parse
tree to an input sentence,
describing the syntactic
structure of the sentence
8. History of AI
• In late 1955, Newell and Simon developed
The logic Theorist, the first AI program.
• This was a crucial stepping stone in
Developing the AI field.
• From its birth 4 decades ago, there have
been a variety of AI programs, and they
have impacted other technological
advancements.
• In the early seventies, the capabilities of
AI programs were limited. Even the most
impressive could only handle trivial
versions of the problems they were
supposed to solve; all the programs were,
in some sense, "toys"
10. Google artificial intelligence 'invents' cat
• Google scientists have claimed a breakthrough in
technology that is able to “learn” like a human
brain by building a computer able to recognize a
picture of a cat
• The computer is based on a “neural network” of
16,000 processing cores with more than a billion
interconnections, each very roughly simulating a
connection in a human brain.
• A team from Google’s cutting-edge research lab,
Google X, and Stanford University, fed the system
10 million thumbnail images taken from YouTube
as “training” and then tested whether it was able
to recognize 20,000 objects in new images.
11. • Among the objects the system learned to
recognize was a cat, one of the most regulars
star of viral clips uploaded by YouTube
members.
• “We never told it during the training, ‘This is a
cat,’” said Google fellow Dr. Jeff Dean. “It
basically invented the concept of a cat.”
• Overall, the neural network achieved 15.8 per
cent accuracy. As well as cats’ faces, it learned
the “concepts” of human faces and bodies, by
compiling a ghostly image of their general
features.
12. MAJOR BRANCHES OF AI
• LOGICAL AI :
What a program knows about the world.
In general the facts of the specific
situation in which it must act and it’s
goals are all represented by sentences of
some mathematical logical language.
• PATTERN RECOGNITION :
When a program makes observation of
some kind, it is often programmed to
compare what it sees with already
stored patterns.
13. • PLANNING :
Planning programs start with general facts about
the world. They generate a strategy for achieving
the goal, the strategy is just a sequence of
action.
• EPISTEMOLOGY :
This is a study of the kinds of knowledge that are
required for solving problems in the world.
• ONTOLOGY :
It is the study of kinds of things that exist. In AI,
things deal with various kinds of object
14. APPLICATIONS OF AI
• GAME PLAYING :
You can buy machines that can play master level
chess for a few hundred dollars.
• 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.
15. • NATURAL LANGUAGE PROCESSOR
The goal of NLP is to enable people and
computers to communicate in a natural
(humanly) language(such as, English)
rather than in a computer language.
The field of NLP is divided in 2
categories—
Natural Language understanding.
Natural Language generation
16. • AUTOMATIC PROGRAMMING :
Programming is a process of telling a computer
exactly what you want it to do. Writing a program
is a tedious job. It must be designed, written,
tested, debugged and evaluated.
The goal of automatic planning is to create
special programs that act intelligent tools to assist
programmers and expedite each phase of
programming process. Ultimate aim is computer
itself should develop a program in accordance
with specifications of programmer.
17. • ROBOTICS :
A Robot is an electro-mechanical
device that can be programmed to
perform manual tasks or a
reprogrammable multi functional
manipulator designed to move
materials, parts, tools, or specialized
devices through variable
programmed motions for
performance of variety of tasks.
18. • COMPUTER VISION
People generally use vision as their
primary means of sensing their
environment, we generally see more
than we hear, feel or smell or taste
The goal of computer vision research
is to give computers this same
powerful facility for understanding
their surrounding.
Here AI helps computer to
understand what they see through
attached cameras.