This document provides an introduction to artificial intelligence (AI). It discusses the history and foundations of AI, including early philosophers who discussed the possibility of machine intelligence. It also defines key AI concepts like intelligence, rational thinking, and acting like humans. The document outlines different types of AI systems and why AI is powerful due to combining knowledge from many disciplines. It concludes with an overview of the history of AI from its beginnings in the 1940s to the growth of expert systems.
Artificial intelligence (AI) all in one presentation consists of almost all concepts of Artificial intelligence. i.e.
Artificial intelligence, Robotics, ANN, NLP, NLU, History of AI, History, Pakistan, basharat jehan, agriculture university peshawar, Forward and Backward Chaining, Grammers in AI, Morphology, Examples of Expert system, Laws of Robotics,Expert system languages, Syntactic Analysis in NLP, Lecture Notes
Artificial intelligence (AI) all in one presentation consists of almost all concepts of Artificial intelligence. i.e.
Artificial intelligence, Robotics, ANN, NLP, NLU, History of AI, History, Pakistan, basharat jehan, agriculture university peshawar, Forward and Backward Chaining, Grammers in AI, Morphology, Examples of Expert system, Laws of Robotics,Expert system languages, Syntactic Analysis in NLP, Lecture Notes
Types Of Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/y5swZ2Q_lBw
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka PPT on "Types Of Artificial Intelligence" will help you understand the different stages and types of Artificial Intelligence in depth. The following topics are covered in this Artificial Intelligence Tutorial:
History Of AI
What Is AI?
Stages Of Artificial Intelligence
Types Of Artificial Intelligence
Domains Of Artificial Intelligence
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
What really is Artificial Intelligence about? Harmony Kwawu
AI systems are growing. But what is AI, where did the idea behind it come from, what is intelligence, how does expert level intelligence work, and perhaps most importantly, would AI systems eventually make human beings redundant?
CS6659 Artificial Intelligence
Slides in the features of Artificial Intelligence, Definition of Artificial Intelligence
Can be used by undergraduate students
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Types Of Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/y5swZ2Q_lBw
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka PPT on "Types Of Artificial Intelligence" will help you understand the different stages and types of Artificial Intelligence in depth. The following topics are covered in this Artificial Intelligence Tutorial:
History Of AI
What Is AI?
Stages Of Artificial Intelligence
Types Of Artificial Intelligence
Domains Of Artificial Intelligence
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
What really is Artificial Intelligence about? Harmony Kwawu
AI systems are growing. But what is AI, where did the idea behind it come from, what is intelligence, how does expert level intelligence work, and perhaps most importantly, would AI systems eventually make human beings redundant?
CS6659 Artificial Intelligence
Slides in the features of Artificial Intelligence, Definition of Artificial Intelligence
Can be used by undergraduate students
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Artificial Intelligence (AI) services on the AWS cloud bring deep learning (DL) technologies like natural language understanding (NLU), automatic speech recognition (ASR), image recognition and computer vision (CV), text-to-speech (TTS), and machine learning (ML) within reach of every developer. In this quick overview, you will be introduced to several new AI services: Amazon Lex, to build sophisticated text and voice chatbots; Amazon Rekognition, for deep learning-based image recognition; and Amazon Polly, for turning text into lifelike speech.
After a great trip to Melbourne for Future Assembly, I thought it'd be great to present our thoughts on Design Ethics for Artificial Intelligence.
It's a thought-provoking and engaging presentation and will have you pondering our flawed and highly subjective value systems.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
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.
For more information, visit-www.vavaclasses.com
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
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
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!
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.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
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.
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?
2. 1. To have or not to have mind…this is the question
2. Why AI is interesting for industry?
3. What does “intelligence” mean?
4. What is Artificial Intelligence?
5. Types of AI
6. Why is AI powerful
7. Bases of AI
8. Artificial intelligence history: From 'dark ages' to KBS
9. Artificial intelligence map
10. References
2
Outline
Introduction to Artificial Intelligence - Albert Fornells
3. Philosophers have been trying for over two thousand years to understand and
resolve two big questions of the universe:
how does a human mind work, and
can non-humans have minds?
However, these questions are still unanswered
Some philosophers have picked up the computational approach originated by
computer scientists and accepted the idea that machines can do everything that
humans can do
Others have an opposed idea, claiming that such highly sophisticated behavior as
love, creative discovery and moral choice will always be beyond the scope of any
machine
Engineers and scientists have already built machines that can be called
‘intelligent’.
So what does the word ‘intelligence’ mean? And intelligent machine?
3
To have or not to have mind…this is the question
Introduction to Artificial Intelligence - Albert Fornells
4. Main reasons:
Economical needs
Experts highly qualified are expensive and they cannot afford them
A way for training new experts
Knowledge preservation
Computational efficiency
General decision methods are slowly and inefficient
Massive information needs to be processed
Decision support system
Get fast, efficient and justified decisions
Autonomous decisions systems
Summarizing: Companies want to build specific systems to solve specific
problems using the knowledge from domain and the expertise from experts
They want to introduce our “minds” and “human capabilities” in their systems
4
Why AI is interesting for industry?
Introduction to Artificial Intelligence - Albert Fornells
5. Let us look at a dictionary definition (Essential English Dictionary,Collins, 1990)
Def 1. Someone’s intelligence is their ability to understand and learn things
Def 2. Intelligence is the ability to think and understand instead of doing things by
instinct or automatically
So, it can be defined as ‘the ability to learn and understand, to solve
problems and to make decisions’
Building a machine that is (or seems to be) intelligent is what is called intelligent
machine.
However, the intelligence is not the same for everyone (and also for machines),
5
What does “intelligence” mean?
Introduction to Artificial Intelligence - Albert Fornells
is not the same as
6. John McCarthy coined AI term in 1956 as ‘the science and engineering of making
intelligent machines’ at a conference at Dartmouth College. Intelligent machine
terms refer to the capability of performing intelligent human processes as:
Learning
Reasoning
Problem solving
Perception
Language understanding
AI has become an essential part of the technology industry, providing the heavy
lifting for many of the most difficult problems in computer science.
Prediction
Classification
Regression
Clustering
Function optimization
6
What is Artificial Intelligence (AI)?
Introduction to Artificial Intelligence - Albert Fornells
7. 7
So, an intelligent machine is a…
Introduction to Artificial Intelligence - Albert Fornells
System that acts…
System that thinks…
…like humans ...rationally
Make tasks that,
nowadays, are better
done by humans
(Rich y Knight, 1991)
Exploring and
emulating the intelligent
behavior in
computational process
terms (Shalkoff, 1990)
Study of mental
capabilities through the
study of computational
models (Charniak y
McDermott, 1985)
Computer with minds at
literal sense
(Haugeland, 1985)
8. The goal is to build a system that seems to be a human
At 1950, Alain Turing proposed the Turing test in the article “Computing
machinery and intelligence” in the Mind Journal
Instead of asking, ‘Can machines think?’, Turing said we should ask, ‘Can
machines pass a behavior test for intelligence?’
So, Turing defined the intelligent behavior of a computer as the ability to achieve
the human-level performance in cognitive tasks
A person -acting as a judge- has a conversation for 5 minutes through a text
channel. The judge has to identify if is talking with a computer or a human
Required capabilities:
Natural language processing
Knowledge representation
Reasoning
Learning
8
Systems that act like humans
Introduction to Artificial Intelligence - Albert Fornells
9. Systems need to act as a human.
They have to lie and make mistakes as us!!
Loebner prize: Turing test competition.
Total Turing test: The same but also with signal video.
9
More about Turing test
Introduction to Artificial Intelligence - Albert Fornells
[INT] I heard that a striped rhinoceros flow on
the Mississippi in a pink balloon this morning.
What do you think about?
[COMP] That sound rather ridiculous to me
[INT] Really? My uncle did this one... Why this
sound ridiculous?
[COMP] Option 1: Rhinoceros don't have
stripes
[COMP] Option 2: Rhinoceros can't fly
[INT] What’s the result of 324 x
678?
[COMP] This is too difficult. I’m not
a calculator!
10. The goal is to model the human brain and set a theory about how it works.
Theory should allow the definition of computational models.
This approach is highly influenced by neuroscience and cognitive sciences.
The conscious mystery
Many religions consider that consciousness resides in the soul.
Can it be included in machines?
D. Chalmers tackles the conscious topic from two points of view:
Easy: Distinguish between conscious and unconscious thinking (Freud).
Heart beat control, muscular movements, planning a day, etc.
Difficult: Explain how the subjective experience is born from neurons.
Surprising hypothesis from Francis Crick (1916-2004): brain as a machine
Our thoughts, sensations and feelings are the result of the physiological activity of
the brain tissues.
Conscious is a biological product such as blood circulation or the digestion.
10
Systems that think like humans
Introduction to Artificial Intelligence - Albert Fornells
11. Aristotle (384 BC- 322 BC) was the first to codify “the right thinking”, that is,
irrefutable reasoning process.
Rationally thoughts are based on the logic.
Formal Logic can be introduced and applied in computers.
But….
It is complex formalize the knowledge
There is a big difference between being able to solve a problem “in principle” and
doing so in practice.
11
Systems that think rationally
Introduction to Artificial Intelligence - Albert Fornells
12. To act rationally means to achieve a set of goals using a previous knowledge
base.
Approach focuses on rational agents.
An agent receives and acts taking into account the environment.
The aim is not to imitate the human model, but to complete a set of actions to
achieve a final goal.
An agent needs:
Perception
Natural language processing
Knowledge representation
Reasoning
Machine learning
12
Systems that act rationally
Introduction to Artificial Intelligence - Albert Fornells
13. The power resides in the combination of disciplines that tackle the same
problems as AI: learn and understand, to solve problems and to make decisions.
AI is fed from many disciplines
Philosophy: Logic, methods of reasoning, mind as physical system, foundations of
learning, language, rationality.
Mathematics: Formal representation and proof, algorithms, computation,
(un)decidability, (in)tractability.
Statistics : Modeling uncertainty, learning from data.
Economics: Utility, decision theory, rational economic agents.
Neuroscience: Neurons as information processing units.
Psychology / NeuroScience: How do people behave, perceive, process cognitive
information, represent knowledge.
Computer Engineering: Building fast computers.
Control Theory: Design systems that maximize an objective function over time.
Linguistics: Knowledge representation, grammars.
13
Why AI is powerful?
Introduction to Artificial Intelligence - Albert Fornells
14. 14
Bases of AI
Introduction to Artificial Intelligence - Albert Fornells
Philosophy
• Discussion about
the possibility of a
mechanical
intelligence.
Mathematical.
• Philosophic bases
requires formal
rules.
Computational
linguistic
• Understanding
language requires
understanding of
the subject matter
and the context.
Cognitive
psychology
• Behavior theories,
rational behave
bases.
Computational
engineering
• Some mechanism,
hardware and tools
are required for AI.
15. Philosophy: Discussion about the possibility of a mechanical intelligence.
Descartes (1556-1650), Leibniz (1646-1716): mind is linked to the physical world.
John Locke: In the beginning there was a Mind (1960).
Hume (1711-1776), Russell (1872-1970): the knowledge comes from the
perception, its acquired by the experience (induction) and its represented by the
logical theories.
Darwin (1809-1882): Evolutionary theory by natural selection at 1859.
Mathematical. Philosophic bases require formal rules.
Boole (1815-1864), Frege (1848-1925): Logical mathematical fundamentals.
Gödel (1906–1978), Turing (1912–1954): Computability limits.
Fermat (1601–1665), Bernoulli (1700–1782), Bayes (1702-1761): Probability and
Probabilistic reasoning.
15
Bases of AI
Introduction to Artificial Intelligence - Albert Fornells
16. Cognitive psychology. Behavior theories, rational behave bases.
William James (1842-1910): Brain possess and processes information.
Hermann Von Helmholtz (1821-1894): Perception involves a form of unconscious
logical inference.
Kenneth Craik (1914-1945): Put back the missing mental step between stimulus
and response.
Stimulus must be translated into an internal representation.
The representation is manipulated by cognitive process to derive a new
internal representation.
These are in turn retranslated back into actions.
Computational linguistic: Understanding language requires understanding of
the subject matter and the context.
B.F. Skinner (1904-1990) published Verbal Behavior at 1957 about the language
learning.
Noam Chomsky (1928-present) published Syntactic Structures at 1957 about
knowledge representation and language grammar.
16
Bases of AI
Introduction to Artificial Intelligence - Albert Fornells
17. Computational engineering: Some mechanisms, hardware and tools are
required for AI
Heath Robinson (1940), first operational modern computing for the single purpose
of deciphering German messages built by Alain Turing
Z3 (1943), first operational programmable computer built by Konrad Zuse
ENIAC (1945), first general-purpose, electronic and digital computer built by John
Machly and John Eckert
EDVAC (1949), followed John Von Neumann’s suggestion to use a stored program
6000 wave tube, 12000 diodes, 56kw, 45’5m2 and 7850kg. Need 30 workers
17
Bases of AI
Introduction to Artificial Intelligence - Albert Fornells
ENIAC
EDVAC Microprocessor
18. 18
AI history: From ‘Dark ages’ to KBS
Introduction to Artificial Intelligence - Albert Fornells
early 1990s-onwards
The failure of expert systems and where AI is going
mid 1980s-early 1990s
AI becomes an industry
early 1970s–mid-1980s
The technology of expert systems
late 1960s–early 1970s
Unfulfilled promises
1956–late 1960s
The era of great expectations
1943–1956
The birth of artificial intelligence
19. Situation: There was nothing
First neuron model and demonstration that any function is computable (Warren
McCulloch and Walter Pits, 1943)
Hebbian theory describes a basic mechanism for synaptic plasticity (Donald
Hebb, 1949)
First neural network simulator (Marvin Misky and Dean Edmonds, 1951)
ENIAC (1946) and EDVAC (1949) are built
Demonstrating the need to use heuristics in the search for the chess game
(Claude Shannon, 1950)
Creation of the non numerical reasoning program “Logic Theorist” (Herbert
Newell, Allen Simon, J.C. Shaw, 1955)
Summer Workshop at Dartmouth College where AI concept is founded
(McCarthy, Minsky, Shannon, Rochester, More, Samuel, Solomonoff, Selfridge,
Newell and Simon, 1956)
19
AI history: Beginnings (1943-1956)
Introduction to Artificial Intelligence - Albert Fornells
20. The early years of AI are characterized by tremendous enthusiasm, great
ideas and very limited success.
Only a few years before, computers had been introduced to perform routine
mathematical calculations.
Now AI researchers were demonstrating that computers could do more than that. It
was an era of great expectations.
Definition of the high-level language LISP (John McCarthy, 1958).
Advice Taker is proposed to search for solutions to general problems of the
world. Focus on formal logic (John McCarthy, 1958).
Generate solutions using axioms and new ones can be added.
1st KBS with the principles of knowledge representation and reasoning.
Frame theory for knowledge representation and reasoning (Marvin Minsky,
1975).
Perceptron convergence theorem is proved (Frank Rosenblatt, 1962).
20
AI history: Big expectations (1956-late 1960s)
Introduction to Artificial Intelligence - Albert Fornells
21. Development GPS, a General Problem Solver, to simulate human problem-
solving methods (Herbert Newell and Allen Simon,1961)
First attempt to separate the problem solving-technique from the data
Means-ends analysis: Strategy based on states
GPS fails in complicated problems due to the infinite number of states
Fuzzy logic is defined (Lofti Zadeh, 1965)
By 1980 the euphoria about AI was gone and most of government funding for AI
projects was cancelled
AI researches attempted to simulate the complex thinking process by inventing
general methods for solving broad classes of problems.
The general purpose search mechanism not work with real problems. They were
called weak methods because they use weak information from domain
However, it was a time when new ideas such as knowledge representation,
learning and computing concepts were set
21
AI history: Big expectations (1956-late 1960s)
Introduction to Artificial Intelligence - Albert Fornells
22. From the mid-1950s, AI researchers were making promises to build all-purpose
intelligent machines on a human-scale knowledge base by the 1980s and to
exceed human intelligence by the year 2000.
By 1970, however, they realized that such claims were too optimistic.
Few AI programs could demonstrate some level of machine intelligence in one or
two toy problems.
Main difficulties:
General methods for broad classes of problems. So, NO knowledge about domain
Search strategies based on try and error. They were not scaled for big problems.
Easy or tractable problems can be solved in polynomial time, intractable problems
require times that are exponential functions of the problem size.
So, many problems were NP-hard. “The size matters”
Eliza, the first chatterbot is created (Joseph Weizenbaum, 1966).
Probably the most important development in the 1970s was the realization that
the problem domain for intelligent machines had to be sufficiently restricted
22
AI history: A dose or reality (late 60s-early 70s)
Introduction to Artificial Intelligence - Albert Fornells
23. Knowledge from expert is used to tackle real problems
DENDRAL: A molecular structure interpretation (Feigenbaum,1971)
The first expert system to solve a real problem
The major shift in AI: shift from general purpose, knowledge-sparse, weak methods
to domain-specific, knowledge intensive techniques
A new methodology of expert systems: knowledge engineering, which
encompassed techniques of capturing, analyzing and expressing in rules an
expert’s ‘know-how’. Appears the knowledge acquisition bottleneck problem
MYCIN: Rule-based expert system for the diagnosis of infectious blood
diseases (Edgar ShortLiffe, 1972)
Worked as well as experts and better than junior doctors
Which were the main contributions?
Knowledge in the form of rules was clearly separated from the reasoning
mechanism
No general theoretical model existed: Rules are extracted from interviews
Rules reflect the uncertainty with medical knowledge
23
AI history: Knowledge as key of success (early 70s- mid 80s)
Introduction to Artificial Intelligence - Albert Fornells
24. PROSPECTOR . A computer-based consultation system for mineral exploration
(Duda, 1981)
It used a combined structure that incorporated rules and a semantic network
It had a sophisticated support package including a knowledge acquisition system
It incorporated Bayes’ rules of evidence to propagate uncertainties through the
system
Many applications showed that AI technology could move successfully from the
research laboratory to the commercial environment
Most expert systems were developed with special AI languages, such as LISP,
PROLOG and OPS, based on powerful workstations
Expensive hardware and complicated programming language means few
researchers
Only in 1980, with the arrival of PCs and easy-to-use expert system development
tools made possible the expansion to more modest laboratories
Knowledge representation and reasoning were the key for success
24
AI history: Knowledge as key of success (early 70s- mid 80s)
Introduction to Artificial Intelligence - Albert Fornells
25. A survey found nearly 200 expert systems, most of them applications in the field
of medical diagnosis (Waterman, 1986)
7 years later, the survey found 2500 developed expert systems (Durkin, 1994).
The new growing area was business and manufacturing
Expert system technology had clearly matured, but are the key of success in
any field? It would be a mistake to overestimate the capability of this technology
Expert systems are restricted to a very narrow domain of expertise
Expert systems are not as robust and flexible as a user might want
Expert systems have limited explanation capabilities
Expert systems are also difficult to verify and validate
Expert systems, especially the first generation, have little or no ability to learn from
their experience
Despite all these difficulties, expert systems have made the breakthrough and
proved their value in a number of important applications
25
AI history: AI becomes an industry (mid 80s-begin90s)
Introduction to Artificial Intelligence - Albert Fornells
26. Disillusion about the applicability of expert system technology
Building an expert system required much more than just buying a reasoning system or
expert system shell and putting enough rules in it
Expert systems were too expensive to maintain
They were difficult to update, they could not learn, they were "brittle“ (i.e. they
could make grotesque mistakes when given unusual inputs)
They proved useful, but only in a few special contexts
So, the next step was to evolve the Expert System to Knowledge Base Systems
26
AI history: The failure of expert systems (begin 90s – onwards)
Introduction to Artificial Intelligence - Albert Fornells
Emulate the resolution capability of an
expert human
Building through knowledge engineering
Mainly based on rules
Reduced learning capabilities
Use the knowledge from domain to solve
problems
Include automatic acquisition process
inside the knowledge engineering process
Heterogeneous methodologies and
architectures (rules, cases, models, etc.)
Extended learning capabilities
27. 27
A possible map of the current AI
Introduction to Artificial Intelligence - Albert Fornells
• Robotics and control
• Natural Language Processing
• Artificial vision
• Speech recognition
• Logic
• Multiagents systems
• Decision Support System
• Knowledge management
• Knowledge representation
• Ontology and semantic web
• Computer-Human interaction
• Evolutionary Computation
• Case-Based Reasoning
• Reinforcement Learning
• Neural Network
• Data Analysis
• Non monotonic reasoning
• Model based reasoning
• Constraint satisfaction
• Qualitative reasoning
• Uncertain reasoning
• Temporal reasoning
• Heuristic search
Reasoning
Machine
Learning
Robotics,
perception
and natural
language
processing
Knowledge
Management
28. 1. For each of the following, give reasons why:
a) A dog is more intelligent than a worm
b) A human is more intelligent than a dog
c) An organization is more intelligent than an individual human
Based on these, give a definition of what “more intelligent” may mean
2. Choose a particular word, for example, what is on some part of your desk at
the current time
i. Get someone to list all of the things that exist in this world (or try it yourself as a
thought experiment)
ii. Try to think of ten things that they missed. Make these as different from each other
as possible
iii. Try to find a thing that can’t be described using natural language
iv. Choose a particular task, such as making the desk tidy, and try to write down all of
the things in the world at a level of description that is relevant to this task
28
Exercises
Introduction to Artificial Intelligence - Albert Fornells
29. 3. Look for the “Chinese room argument” significance (Searle, 1980). Which
was its goal?
29
Exercises
Introduction to Artificial Intelligence - Albert Fornells
30. S. Russell, P. Norvig. Artificial Intelligence: a Modern Approach, 3rd ed.
Pearson Higher Education, 2010 (*).
Also available in Spanish.
M. Negnevitsky, Artificial Intelligence, A Guide to Intelligent Systems. Addison
Wesley, 2002 (*).
N. J. Nilsson, Artificial Intelligence: a new synthesis, The Morgan Kaufmann
Series in Artificial Intelligence Series. Morgan Kaufmann, 1998 (*).
(*) Partially published in Google Books
30
References
Introduction to Artificial Intelligence - Albert Fornells