This document provides an overview of artificial intelligence including:
1) It discusses what AI is, its history, and some of the key subfields like games playing, expert systems, natural language processing, and neural networks.
2) It outlines several applications of AI including in computer science, finance, medicine, heavy industry, transportation, telecommunications, toys/games, music, aviation, and news/publishing.
3) It provides a brief history of AI from the 15th century to modern day, highlighting milestones like the first mechanical calculator and Deep Blue's victory over Kasparov in chess.
A presentation on Artificial Intelligence which covers definition, introduction , advantages/disadvantages, AI Tree and application of artificial intelligence
A presentation on Artificial Intelligence which covers definition, introduction , advantages/disadvantages, AI Tree and application of artificial intelligence
Human intelligence is the intellectual powers of humans, Learning
Decision Making
Solve Problems
Feelings(Love,Happy,Angry)
Understand
Apply logic
Experience
making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.
Robots are autonomous or semi-autonomous machines meaning that they can act independently of external commands. Artificial intelligence is software that learns and self-improves.
Why Artificial Intelligence?
• Computers can do computations, by fixed programmed rules
• A.I machines perform tedious tasks efficiently & reliably.
• computers can’t understanding & adapting to new situations.
• A.I aims to improve machine to do such complex tasks.
Advantages of A.I:
Error Reduction
Difficult Exploration(mining & exploration processes)
Daily Application(Siri, Cortana)
Digital Assistants(interact with users)
Medical Applications(Radiosurgery)
Repetitive Jobs(monotonous)
No Breaks
Some disadvantages of A.I:
High Cost
Unemployment
Weaponization
No Replicating Humans
No Original Creativity
No Improvement with Experience
Safety/Privacy Issues
Artificial intelligence will be a Greatest invention Until Machines under the human control. Otherwise The new ERA will be There…..!
Understanding artificial intelligence and it's future scopeChaitanya Shimpi
In the field of computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
Contains a detailed Slides on Artificial Intelligence.
What is artificial intelligence?
What are its uses?
advantages?
disadvantages?
Charasteristics?
examples?
functions
and other criterias.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
This presentation will give you a brief about the Artificial intelligence concept with the below-mentioned contents
- What is AI?
- Need for AI
- Languages used for AI development
- History of AI
- Types of AI
- Agents in AI
- How AI works
- Technologies of AI
- Application of AI
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).
Over 23 million software developers in 2018, this
number is expected to reach 26.4 million by the end of 2019 and
27.7 million by 2023 according to Evans Data Corporation. The
number of programmers continues to grow to this day as
technology is the Forthcoming, especially in the AI field. Where
there are 300,000 “AI researchers and practitioners” in the world,
but the market demand is for millions of roles, so many people
Siding to this field. Nowadays, most people learn the
programming field as inquisitiveness but for their interest,
however, they delve deeper into this field, which enhances their
passion for and leaves their work to practice programming as
occupation due to the availability of jobs and the most request for
it. Over time, new languages have emerged, it has evolved to
meet human needs in the form of programming languages. You
can instruct the computer in the human-readable form where
programming will enable you to learn the significance of clarity
of expression, many determinations can be achieved,
importantly, relationships, semantics, and grammar can be
defined.
Human intelligence is the intellectual powers of humans, Learning
Decision Making
Solve Problems
Feelings(Love,Happy,Angry)
Understand
Apply logic
Experience
making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.
Robots are autonomous or semi-autonomous machines meaning that they can act independently of external commands. Artificial intelligence is software that learns and self-improves.
Why Artificial Intelligence?
• Computers can do computations, by fixed programmed rules
• A.I machines perform tedious tasks efficiently & reliably.
• computers can’t understanding & adapting to new situations.
• A.I aims to improve machine to do such complex tasks.
Advantages of A.I:
Error Reduction
Difficult Exploration(mining & exploration processes)
Daily Application(Siri, Cortana)
Digital Assistants(interact with users)
Medical Applications(Radiosurgery)
Repetitive Jobs(monotonous)
No Breaks
Some disadvantages of A.I:
High Cost
Unemployment
Weaponization
No Replicating Humans
No Original Creativity
No Improvement with Experience
Safety/Privacy Issues
Artificial intelligence will be a Greatest invention Until Machines under the human control. Otherwise The new ERA will be There…..!
Understanding artificial intelligence and it's future scopeChaitanya Shimpi
In the field of computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
Contains a detailed Slides on Artificial Intelligence.
What is artificial intelligence?
What are its uses?
advantages?
disadvantages?
Charasteristics?
examples?
functions
and other criterias.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
This presentation will give you a brief about the Artificial intelligence concept with the below-mentioned contents
- What is AI?
- Need for AI
- Languages used for AI development
- History of AI
- Types of AI
- Agents in AI
- How AI works
- Technologies of AI
- Application of AI
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).
Over 23 million software developers in 2018, this
number is expected to reach 26.4 million by the end of 2019 and
27.7 million by 2023 according to Evans Data Corporation. The
number of programmers continues to grow to this day as
technology is the Forthcoming, especially in the AI field. Where
there are 300,000 “AI researchers and practitioners” in the world,
but the market demand is for millions of roles, so many people
Siding to this field. Nowadays, most people learn the
programming field as inquisitiveness but for their interest,
however, they delve deeper into this field, which enhances their
passion for and leaves their work to practice programming as
occupation due to the availability of jobs and the most request for
it. Over time, new languages have emerged, it has evolved to
meet human needs in the form of programming languages. You
can instruct the computer in the human-readable form where
programming will enable you to learn the significance of clarity
of expression, many determinations can be achieved,
importantly, relationships, semantics, and grammar can be
defined.
A Seminar Report on Artificial IntelligenceAvinash Kumar
This is a seminar report on Artificial Intelligence. This is mainly concerned for engineering projects & reports. This is actually done for presentation purpose.
Artificial intelligence is already all around you, from web search to video games. AI methods plan your driving directions, filter your spam, and focus your cameras on faces.
just hvae a look, m sure u whould lyk it...............................................................................................................................................................................its all about artificial machines.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
AiArtificial Itelligence
1. Report:
Artificial
Intelligence
Submitted to: Submitted by:
Prof. Jatinder Pal Singh Alisha Korpal
David Kochar
Nivia Jain
Sharuti Jain
1|Page
2. Index
Sno Topic Page no
1 Introduction 3
2 AI includes 4
3 History 5
4 Applications 7
5 Positive points 12
6 Negative points 13
7 References 14
2|Page
3. Introduction:
Artificial intelligence (AI) is the intelligence of machines and the branch of computer
science that aims to create it. AI textbooks define the field 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 1956, defines it as
"the science and engineering of making intelligent machines."
The field was founded on the claim that a central property of humans, intelligence—
the sapience of Homo sapiens—can be so precisely described that it can be simulated by a
machine. This raises philosophical issues about the nature of the mind and the ethics of creating
artificial beings, issues which have been addressed by myth, fiction and philosophy since
antiquity. Artificial intelligence has been the subject of optimism, but has also suffered
setbacks and, today, has become an essential part of the technology industry, providing the heavy
lifting for many of the most difficult problems in computer science.
AI research is highly technical and specialized, and deeply divided into subfields that often fail
to communicate with each other. Subfields have grown up around particular institutions, the
work of individual researchers, the solution of specific problems, longstanding differences of
opinion about how AI should be done and the application of widely differing tools. The central
problems of AI include such traits as reasoning, knowledge, planning, learning, communication,
perception and the ability to move and manipulate objects. General intelligence (or "strong AI")
is still among the field's long term goals.
3|Page
4. AI includes
Games playing
Programming computers play games such as chess and checkers. Currently, no computers exhibit
AI (that are able to stimulate human behavior), the greatest advances have occurred in the field
of games playing. The best computer chess programs are now capable of beating humans. In
May 1997 an IBM super computer called Deep Blue defeated world chess champion Gary
Kasparov in chess match.
Expert systems
Programming computers to make decision in real life situations (for example, some expert
system help doctors diagnose diseases based on symptoms)
Natural language
Programming computer understand natural languages. Natural language processing offers the
greatest potential rewards because it would allow people to interact with computer without
needing any specialized knowledge. You could simply walk up to a computer and talk to it.
Neural networks
Systems that simulate intelligence by attempting to reproduce the types of physical connections
that occur in brains
Robotics
Programming computers to see hear and react to other stimuli.In the area of robotics, computers
are now widely used in assembly plants, but they are capable only of very limited tasks. Robots
have great difficulty identifying objects based on appearance or feel and they still move and
handle objects clumsily.
4|Page
5. History
15th century
Aristoltle invents first formal deductive reasoning system.
16th century
Rabbi invents an artificial man made out of clay.
17th century
Pascal creates first mechanical calculator
18th century
Wolfgang von invents fake chess playing machine
19th century
Charles Babbage and Lady Lovelace develop sophisticated programmable
mechanical computer
20th century
Karel Kapek invents Robots
In the early 1980s, AI research was revived by the commercial success of expert systems, a form
of AI program that simulated the knowledge and analytical skills of one or more human experts.
By 1985 the market for AI had reached over a billion dollars. At the same time, Japan's fifth
generation computer project inspired the U.S and British governments to restore funding for
academic research in the field. However, beginning with the collapse of the Lisp Machine market
in 1987, AI once again fell into disrepute, and a second, longer lasting AI winter began.
In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind
the scenes. Artificial intelligence is used for logistics, data mining, diagnosis and many other
areas throughout the technology industry. The success was due to several factors: the increasing
computational power of computers (see Moore's law), a greater emphasis on solving specific sub
problems, the creation of new ties between AI and other fields working on similar problems, and
a new commitment by researchers to solid mathematical methods and rigorous scientific
standards.
5|Page
6. On 11 May 1997, Deep Blue became the first computer chess-playing system to beat a reigning
world chess champion, Garry Kasparov In 2005, a Stanford robot won the DARPA Grand
Challenge by driving autonomously for 131 miles along an unrehearsed desert trail.
The leading-edge definition of artificial intelligence research is changing over time. One
pragmatic definition is: "AI research is that which computing scientists do not know how to do
cost-effectively today." For example, in 1956 optical character recognition (OCR) was
considered AI, but today, sophisticated OCR software with a context-sensitive spell checker and
grammar checker software comes for free with most image scanners. No one would any longer
consider already-solved computing science problems like OCR "artificial intelligence" today.
Low-cost entertaining chess-playing software is commonly available for tablet
computers. DARPA no longer provides significant funding for chess-playing computing system
development. The Kinect which provides a 3D-body-motion interface for the Xbox 360 uses
algorithms that emerged from lengthy AI research, but few consumers realize the technology
source.AI applications are no longer the exclusive domain of Department of defense R&D, but
are now common place consumer items and inexpensive intelligent toys. In common usage, the
term "AI" no longer seems to apply to off-the-shelf solved computing-science problems, which
may have originally emerged out of years of AI research.
6|Page
7. Applications
1 Computer science
2 Finance
3 Medicines
4 Heavy industry
5 Online and telephone customer service
6 Transportation
7 Telecommunications
8 Toys and games
9 Music
10 Aviation
11 News and publishing
7|Page
8. Computer science
AI researchers have created many tools to solve the most difficult problems in computer science.
Many of their inventions have been adopted by mainstream computer science and are no longer
considered a part of AI.
Time sharing
Interactive interpreters
Graphical user interfaces and the computer mouse
Rapid development environments
The linked list data type
Automatic storage management
Symbolic programming
Functional programming
Dynamic programming
Object-oriented programming
Finance
Banks use artificial intelligence systems to organize operations, invest in stocks, and manage
properties. In August 2001, robots beat humans in a simulated financial trading competition.
Financial institutions have long used artificial neural network systems to detect charges or claims
outside of the norm, flagging these for human investigation
Medical
A medical clinic can use artificial intelligence systems to organize bed schedules, make a staff
rotation, and provide medical information.
Artificial neural networks are used as clinical decision support systems for medical diagnosis,
such as in Concept Processing technology in EMR software.
8|Page
9. Heavy Industry
Robots have become common in many industries. They are often given jobs that are considered
dangerous to humans. Robots have proven effective in jobs that are very repetitive which may
lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may
find degrading. Japan is the leader in using and producing robots in the world. In 1999,
1,700,000 robots were in use worldwide. For more information, see survey about artificial
intelligence in business.
Transportation
Telecommunication
Many telecommunications companies make use of heuristic search in the management of their
workforces, for example BT Group has deployed heuristic search in a scheduling application that
provides the work schedules of 20,000 engineers.
Toys and Games
The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic
Artificial Intelligence for education, or leisure. This prospered greatly with the Digital
Revolution, and helped introduce people, especially children, to a life of dealing with various
types of AI, specifically in the form of Tamagotchis and Giga Pets, the Internet (example: basic
search engine interfaces are one simple form), and the first widely released robot, Furby. A mere
year later an improved type of domestic robot was released in the form of Aibo, a robotic dog
with intelligent features and autonomy. AI has also been applied to video games.
9|Page
10. Aviation
The Air Operations Division AOD, uses AI for the rule based expert systems. The AOD has use
for artificial intelligence for surrogate operators for combat and training simulators, mission
management aids, support systems for tactical decision making, and post processing of the
simulator data into symbolic summaries.
The use of artificial intelligence in simulators is proving to be very useful for the AOD. Airplane
simulators are using artificial intelligence in order to process the data taken from simulated
flights. Other than simulated flying, there is also simulated aircraft warfare. The computers are
able to come up with the best success scenarios in these situations. The computers can also create
strategies based on the placement, size, speed, and strength of the forces and counter forces.
Pilots may be given assistance in the air during combat by computers. The artificial intelligent
programs can sort the information and provide the pilot with the best possible maneuvers, not to
mention getting rid of certain maneuvers that would be impossible for a sentient being to
perform. Multiple aircraft are needed to get good approximations for some calculations so
computer simulated pilots are used to gather data. These computer simulated pilots are also used
to train future air traffic controllers.
10 | P a g e
11. Positive points
Tireless
Copying
Accurate decisions
Not human bias
11 | P a g e
12. Negative points
"Can a machine act intelligently?" is still an open problem. Taking "A machine can act
intelligently" as a working hypothesis, many researchers have attempted to build such a machine.
The general problem of simulating (or creating) intelligence has been broken down into a
number of specific sub-problems. These consist of particular traits or capabilities that researchers
would like an intelligent system to display. The traits described below have received the most
attention.
Deduction, reasoning, problem solving
Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans
were often assumed to use when they solve puzzles, play board games or make logical
deductions. By the late 1980s and '90s, AI research had also developed highly successful
methods for dealing with uncertain or incomplete information, employing concepts from
probability and economics.
For difficult problems, most of these algorithms can require enormous computational resources
— most experience a "combinatorial explosion": the amount of memory or computer time
required becomes astronomical when the problem goes beyond a certain size. The search for
more efficient problem solving algorithms is a high priority for AI research. Human beings solve
most of their problems using fast, intuitive judgments rather than the conscious, step-by-step
deduction that early AI research was able to model. AI has made some progress at imitating this
kind of "sub-symbolic" problem solving: embodied agent approaches emphasize the importance
of sensor motor skills to higher reasoning; neural net research attempts to simulate the structures
inside human and animal brains that give rise to this skill.
Knowledge representation
Ontology represents knowledge as a set of concepts within a domain and the relationships
between those concepts.
Knowledge representation and knowledge engineering are central to AI research. Many of the
problems machines are expected to solve will require extensive knowledge about the world.
Among the things that AI needs to represent are: objects, properties, categories and relations
between objects; situations, events, states and time; causes and effects; knowledge about
knowledge (what we know about what other people know); and many other, less well researched
12 | P a g e
13. domains. A representation of "what exists" is an ontology (borrowing a word from
traditional philosophy), of which the most general are called upper ontologism.
References
http://docs.google.com/viewer?
a=v&q=cache:XthIrYYYODMJ:www.cse.yorku.ca/~mack/1010/1010-
Chapter11.ppt+ppt+future+of+artificial+intelligence&hl=en&gl=in&pid=bl&srcid=ADG
EESiFCTrV2bsrOZ1VJQt6SY1uarV9NfzHdG5jpt_K-BJ7AH1aO-
fCxrSFdEBRkPgpz2OcdnlAzcNrckqu6cR41mshgxPKuYYWDTiJnfGNPskufuQtKdiqM
qlw6KnoBJVpCOjCYW3C&sig=AHIEtbRN-O2LlQ8vSmAJFRkotUz6uFSL1A&pli=1
http://www.youtube.com/watch?v=r6ye5fnnCyc&feature=related
http://www.youtube.com/watch?v=S-_eVzxDXB0
http://en.wikipedia.org/wiki/Artificial_intelligence
http://www.google.co.in/search?
q=ppt+positive+and+negative+of+artificial+intelligence&hl=en&pwst=1&prmd=ivns&e
i=LhhLTqbZEofUiALQBw&start=10&sa=N&biw=1280&bih=869&cad=cbv
13 | P a g e