The document discusses the evolution of artificial intelligence and the development of knowledge-based systems, which apply domain-specific knowledge rather than general problem-solving techniques. It provides an overview of the components of a KBS, examples of widely used systems, and the advantages and limitations of the approach.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
This presentation discusses about the following topics:
Hybrid Systems
Hybridization
Combinations
Comparison of Expert Systems, Fuzzy Systems, Neural Networks and Genetic Algorithms
Current Progress
Primary Components
MultiComponents
Degree of Integration
Transformational, hierarchial and integrated
Stand Alone Models
Integrated – Fused Architectures
Generalized Fused Framework
System Types for Hybridization
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
This presentation discusses about the following topics:
Hybrid Systems
Hybridization
Combinations
Comparison of Expert Systems, Fuzzy Systems, Neural Networks and Genetic Algorithms
Current Progress
Primary Components
MultiComponents
Degree of Integration
Transformational, hierarchial and integrated
Stand Alone Models
Integrated – Fused Architectures
Generalized Fused Framework
System Types for Hybridization
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
This presentation discusses the following ANN concepts:
Introduction
Characteristics
Learning methods
Taxonomy
Evolution of neural networks
Basic models
Important technologies
Applications
AI and expert system
What is TMS?
Enforcing logical relations among beliefs.
Generating explanations for conclusions.
Finding solutions to search problems
Supporting default reasoning.
Identifying causes for failure and recover from inconsistencies.
TMS applications
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
This presentation discusses the following ANN concepts:
Introduction
Characteristics
Learning methods
Taxonomy
Evolution of neural networks
Basic models
Important technologies
Applications
AI and expert system
What is TMS?
Enforcing logical relations among beliefs.
Generating explanations for conclusions.
Finding solutions to search problems
Supporting default reasoning.
Identifying causes for failure and recover from inconsistencies.
TMS applications
Brains, Data, and Machine Intelligence (2014 04 14 London Meetup)Numenta
Jeff will discuss the Brains, Data, Machine Intelligence, Cortical Learning Algorithm he developed and the Numenta Platform for Intelligent Computing (NuPIC).
State-Of-The Art Machine Learning Algorithms and How They Are Affected By Nea...inside-BigData.com
In this deck from the HPC Knowledge Portal 2017 Conference, Rob Farber from TechEnablement presents: State-Of-The Art Machine Learning Algorithms and How They Are Affected By Near-Term Technology Trends.
"Industry and Wall Street projections indicate that Machine Learning will touch every piece of data in the data center by 2020. This has created a technology arms race and algorithmic competition as IBM, NVIDIA, Intel, and ARM strive to dominate the retooling of the computer industry to support ubiquitous machine learning workloads over the next 3-4 years. Similarly, algorithm designers compete to create faster and more accurate training and inference techniques that can address complex problems spanning speech, image recognition, image tagging, self-driving cars, data analytics and more. The challenges for researchers and technology providers encompass big data, massive parallelism, distributed processing, and real-time processing. Deep-learning and low-precision inference (based on INT8 and FP16 arithmetic) are current hot topics."
Watch the video: https://wp.me/p3RLHQ-i2K
Learn more: http://www.hpckp.org/index.php/conference/2017
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This ppt gives a insight of AI and Machine learning there working there application risk and benefits and some future scope
The Various Content and images has been gathered from various sites on the internet some of them are
https://www.wikipedia.org
http://scikit-learn.org/stable/
In this deck from the Perth HPC Conference, Rob Farber from TechEnablement presents: AI is Impacting HPC Everywhere.
"The convergence of AI and HPC has created a fertile venue that is ripe for imaginative researchers — versed in AI technology — to make a big impact in a variety of scientific fields. From new hardware to new computational approaches, the true impact of deep- and machine learning on HPC is, in a word, “everywhere”. Just as technology changes in the personal computer market brought about a revolution in the design and implementation of the systems and algorithms used in high performance computing (HPC), so are recent technology changes in machine learning bringing about an AI revolution in the HPC community. Expect new HPC analytic techniques including the use of GANs (Generative Adversarial Networks) in physics-based modeling and simulation, as well as reduced precision math libraries such as NLAFET and HiCMA to revolutionize many fields of research. Other benefits of the convergence of AI and HPC include the physical instantiation of data flow architectures in FPGAs and ASICs, plus the development of powerful data analytic services."
Learn more: http://www.techenablement.com/
and
http://hpcadvisorycouncil.com/events/2019/australia-conference/agenda.php
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1. AI & KBS
AI & KBS
Overview
* A brief history of Artificial Intelligence (AI)
- requirements of KBS
* Introduction to Knowledge-Based Systems (KBS)
- definition
- architecture
- development tools
* Examples of some KBS
* Characteristics of KBS
2. AI & KBS
1. The Evolution of AI
(1) The request of an intelligent machine
- early robots ---- electro-mechanical devices
- later robots ---- use computers, ‘dumb’
limited and pre-specified tasks
- ideal robots ---- intelligent machine combines the memory
accuracy, and speed of computers with the
intelligence and flexibility of humans.
AI - Computer programs that undertake tasks that, if done
by people would be described as requiring intelligence.
* Can computers think?
- Turing Test
Imitation game - machine mistaken for a human
“Computing machinery and intelligence”, Alan Turing, 1950
3. AI & KBS
Wall
In the Turing test a human communicates with an unseen respondent through
a terminal, not knowing if the respondent is a person or a machine. If the tester
mistakes computer answers for human answers, the computer successfully
passes the Turning test.
4. AI & KBS
ELIZA
• Developed by MIT 1966
• Called ELIZA after Shaws play - it could be taught to speak
increasing well
• It picked up words from its conversational partner
• Transform this into a canned response
ELIZA’s opening statement (appears on the terminal screen):
Do you have any problems?
Human: Yes. I am unhappy. (types response on the terminal screen)
ELIZA: Why are you unhappy?
Human: My friend is mean to me.
ELIZA: Tell me about your friend. (Rogerian Psychoanalyst)
It could fool people into thinking it was a real person but it contained
no intelligence.
5. AI & KBS
(2) Game playing - early AI emphasis
- Board games: chess, checkers, & 16-puzzle
- No ambiguity in representation of the board
configuration
- Rules generate large search space: require heuristics
Move
1-X
2-O
3-X
4-O
5-X
Tic-Tac-Toe game
6. AI & KBS
(3) Theorem proving
- The proving of mathematical theorems by a
computer program
- Theorems automatically proven from a given
set of axioms
- Theorems & axioms expressed in logic and
logical inferences applied
- First theorem prover developed in mid-50s but
breakthrough in 1960s
- Breakthrough came after introduction of
Resolution inference rule
7. AI & KBS
Theorem proving -Resolution
All Irish are Europeans.
Dave is a Irish.
Therefore, Dave is a European
8. AI & KBS
(4) Problem solving
- GPS (General Problem Solver)
focus on systems with general capability for solving
different types of problems
- Problem represented in terms of initial state,
wished-for final state (goal) and a set of legal
transitions to transfer states into new states
- Using states & operators, GPS generates sequence of
transitions that transform initial state into final state
9. AI & KBS
- Problems with GPS:
* efficiency in choosing path to reach the goal
* GPS did not use specific info about problem at hand
in selection of state transition
* GPS examined all states leading to exponential time
complexity
* breakthrough in AI towards more specialised
problem-solving system, i.e.,
Knowledge-based systems
10. AI & KBS
(5) Other AI fields - a tree representation
11. AI & KBS
(6) KBS as real-world problem solvers
- Problem-solving power does not lie with smart reasoning
techniques nor clever search algorithms but
domain dependent real-world knowledge
- Real-world problems do not have well-defined
solutions
- Expertise not laid down in algorithms but are domain
dependent rules-of-thumb or heuristics (cause-and-effect)
- KBS allow this knowledge to be represented in
computer & solution explained
12. AI & KBS
2. Knowledge-based Systems: A definition
- A system that draws upon the knowledge of
human experts captured in a knowledge-base to solve
problems that normally require human expertise.
- Heuristic rather than algorithmic
- Heuristics in search vs. in KBS
general vs. domain-specific
- Highly specific domain knowledge
- Knowledge is separated from how it is used
KBS = knowledge-base + inference engine
13. AI & KBS
3. KBS Architecture
Facts Heuristics, etc.
Explanation
End-user Inference Knowledge
Queries -base
interface engine
Conclusions
Expertise Knowledge-
Recommendations representation
for action schema
14. AI & KBS
(1) Knowledge-base
Heuristics
Hypothesis Rules
Facts Objects
Knowledge-
base
Processes Attributes
Events
Relationships
Definitions
15. AI & KBS
(2) Knowledge representation formalisms
& Inference
KR Inference
* Logic Resolution principle
* Production rules backward (top-down, goal directed)
forward (bottom-up, data-driven)
* Semantic nets &
Frames Inheritance & advanced reasoning
* Case-based
Reasoning Similarity based
16. AI & KBS
(3) KBS tools - Shells
- Consist of KA Tool, Database &
Development Interface
- Inductive Shells
- simplest
- example cases represented as matrix of known data
(premises) and resulting effects (conclusions)
- matrix converted into decision tree or IF-THEN statements
- examples selected for the tool
- Rule-based shells
- simple to complex
- IF-THEN rules
17. AI & KBS
- Hybrid shells
- sophisticate & powerful
- support multiple KR paradigms & reasoning schemes
- generic tool applicable to a wide range
- Special purpose shells
- specifically designed for particular types of problems
- restricted to specialised problems
-Scratch
- require more time and effort
- no constraints like shells
- shells should be investigated first
18. AI & KBS
4. Some example KBSs
(1) DENDRAL (chemical)
- Pioneering work developed in 1965 for NASA at
Stanford University by Buchanan & Feigenbaum
- DENDRAL infers the molecular structure given mass
spectral data
- Traditional method of generate-and-test, difficulty:
millions of possible structures might be generated
to account for data
- Experts used rules-of-thumb to weed-out structures
that are unlikely to account for the data
- Encoded this expertise & produced program which
performed as well as an expert chemist
19. AI & KBS
(2) MYCIN (medicine)
- Developed in 1970 at Stanford by Shortcliffe
- Assist internists in diagnosis and treatment of
infectious diseases: meningitis & bacterial septicemia
- When patient shows signs of infectious disease, culture
of blood and urine set to lab (>24hrs) to determine
bacterial species
- Given patient data (incomplete & inaccurate) MYCIN
gives interim indication of organisms that are most likely
cause of infection & drugs to control disease
- Drug interactions & already prescribed drugs taken into
account
- Able to provide explanation of diagnosis (limited)
20. AI & KBS
(3) XCON/RI (computer)
- Configures DEC’s VAX, PDP11 and µVAX
- DEC offers the customer a wide choice of components
when purchasing computer equipment, so that
client achieves a custom-made system
- Given the customer’s order, configuration is made,
perhaps involving component replacement or addition
- Problem: information subject to rapid change &
configuring a computer system requires
skills and effort
- Since 1981, XCON with XSEL assists DEC agents
in drawing up orders.
21. AI & KBS
(4) DRILLING ADVISOR (industry)
- Developed in 1983 by Teknowledge for oil company
to replace human drilling advisor
- Problem:drill bits becoming stuck
- Difficulty: lack of subsurface information on
location & condition on end of drill
- (scarcity) expert examines rock pieces, mud, lubricant
brought up by drilling to determine cause
- Drilling Advisor uses geological site information,
conditions of problem, historical information about
other problems experienced in the past
- Provide recommendation to correct problem & advice
on how to change current practices to avoid problem
22. AI & KBS
(5) Human Resource Management
y HRM facilitates the most effective use of employees to
achieve organisational and individual goals
y HRM KBS forms part of overall strategy (includes DSS &
EIS)
y KBS helps decision making for HRM managers with
heuristic knowledge in unstructured & semi-structured
problems (job placement & pay rises)
y Using semantic nets & Prolog, illustrates use of KBS in
HR planning, recruiting, compensation & labour-
management relations
(see Human resource management expert systems
technology, Byun & Suh, ES, May 94, 11:2)
23. AI & KBS
5. Typical tasks of KBS
(1) Diagnosis - To identify a problem given a set of symptoms
or malfunctions.
e.g. diagnose reasons for engine failure
(2) Interpretation - To provide an understanding of a situation
from available information. e.g. DENDRAL
(3) Prediction - To predict a future state from a set of data or
observations. e.g. Drilling Advisor, PLANT
(4) Design - To develop configurations that satisfy constraints
of a design problem. e.g. XCON
(5) Planning - Both short term & long term in areas like project
management, product development or financial planning.
e.g. HRM
24. AI & KBS
(6) Monitoring - To check performance & flag exceptions.
e.g., KBS monitors radar data and estimates the position of
the space shuttle
(7) Control - To collect and evaluate evidence and form opinions
on that evidence.
e.g. control patient’s treatment
(8) Instruction - To train students and correct their performance.
e.g. give medical students experience diagnosing illness
(9) Debugging - To identify and prescribe remedies for
malfunctions.
e.g. identify errors in an automated teller machine network and
ways to correct the errors
25. AI & KBS
6. Advantages & Limitations
(1) Advantages
- Increase availability of expert knowledge
expertise not accessible
training future experts
- Efficient and cost effective
- Consistency of answers
- Explanation of solution
- Deal with uncertainty
26. AI & KBS
(2) Limitations
-Lack of common sense
-Inflexible, Difficult to modify
- Restricted domain of expertise
- Lack of learning ability
- Not always reliable
27. AI & KBS
Overview
- Traditional AI & its limitations for real-world problem
solving
- KBS emergence in 60’s
emphasis on specific domain-knowledge rather than GPS
separation of knowledge and reasoning
- KBS basic components:
knowledge-base, inference engine & user-interface
- Examples
- Advantages & limitations