The document discusses concepts and components of artificial intelligence (AI) and expert systems. It defines AI as concerned with studying human thought processes and duplicating them in machines. Expert systems are computer-based systems that use expert knowledge to make decisions. The key components of expert systems are the knowledge base containing rules and heuristics, the inference engine that interprets rules to solve problems, and the user interface. Common applications of expert systems include medical diagnosis, credit analysis, and market surveillance.
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
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Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
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# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
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Artificial Intelligence lecture notes. AI summarized notes for expert systems, inference mechanisms and so on, this is reading and may be for self-learning, I think.
Artificial Intelligence lecture notes. AI summarized notes for expert systems, inference mechanisms and so on, this is reading and may be for self-learning, I think.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
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Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
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Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
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#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. Concepts and Definitions of Artificial
Intelligence
• The term has many different definitions most
experts agree that AI is concerned with two
basic ideas
• The study of human thought processes
• The representation and duplication of thought
processes in machines
3. Concepts and Definitions of Artificial
Intelligence
• To understand AI we need to examine those abilities that are
considered to be signs of intelligence:
• Learning or understanding from experience
• Making sense out of ambiguous messages
• Responding quickly and successfully to a new situation
• Using reasoning in solving problems
• Understanding and inferring in a rational way
• Applying knowledge to manipulate environment
• Thinking and reasoning
• Recognizing and judging relative importance of different elements
in a situation
•
4. Characteristics of AI:
• AI techniques usually have features describe below:
• 1. Symbolic processing:
• AI is a branch of science that deals with symbolic, non- algorithmic methods of
problem solving.
• This definition focuses on two characteristics
• Numeric versus symbolic
• Symbolic processing being core f AI still that doesn’t mean AI cannot use math
• Algorithmic versus heuristic
• An algorithm is a step by step process and is intended to find same solution for a
specific problem.
• Human processes are usually non-algorithmic rather human thinking relies more
on rules, opinions and gut feelings, learned from previous experiences
• 2. Heuristics
• Heuristics are intuitive knowledge learned from previous experience
• By using heuristics in AI we don’t have to rethink completely what we have if we
encounter a similar problem
• Many AI methods uses heuristics to reduce complexity of problem solving
5. Characteristics of AI:
3) Inferencing
• AI also includes reasoning capabilities that can build higher level
knowledge using existing knowledge represented as heuristics in
the form of rules.
• Inference is the process of deriving logical outcome using set of
facts and rules
4. Machine learning
• Learning is an important capability of human being it separates
human from other creatures.
• AI have simplest learning capabilities called machine learning
• Machine learning allow computer systems to monitor and sense
environmental factors and adjust their behavior to react to changes
7. Basic Concepts of Expert Systems
Expert system are computer based systems that uses expert
knowledge to take decisions.
The basic concept of ES is to determine who experts are ,the
definition of expertise, how expertise can be extracted and
transferred from person to computer and how the expert system
should mimic the reasoning process of human experts
8. Basic Concepts of Expert Systems
• Experts:
• An expert is a person who has special knowledge or experience in an area
and skill to put his knowledge into action to provide advice and solve
complex problem in that area.
• Its expert job to provide knowledge about how he or she performs task
that KBS will perform
• Typically human experts are capable of doing following:
• Recognizing and formulating problem
• Solving a problem quickly and correctly
• Explaining a solution
• Learning from experience
• Restructuring knowledge
• Breaking rules
• Determining relevance and associations
• Declining gracefully
9. Basic Concepts of Expert Systems
• Expertise:
• Expertise is task specific knowledge that expert
possesses
• The level of expertise determines the performance of
decision
• Expertise is acquired through training, reading and
experience in practice
• Following is the list of possible knowledge types that
can be possessed by expert
•
10.
11. • ES must have following features:
• 1. Expertise
• ES must possess expertise to make expert level decision
• 2. Symbolic reasoning
• Knowledge must be represented symbolically
• 3.Deep knowledge
• Knowledge base must contain complex knowledge which
cannot be found among non-experts
• 4. Self-knowledge
• ES must be able evaluate its own reasoning and must be
able to provide proper explanation as to why a particular
conclusion was reached
12. APPLICATION OF EXPERT SYSTEMS
• ES have been applied to may technological areas to support decision making.
• Early ES application such as DENDRAL for molecular identification and MYCIN for medical was
primarily in the science domain.
• XCON for configuration of VAX computer system
•
• DENDRAL
• It uses set of knowledge or rule based reasoning commands to deduce structure of organic
compounds from known chemical analyses
•
• MYCIN
• MYCIN is rule based ES that diagnoses bacterial infections of the blood.
• MYCIN can recognize 100 causes of bacterial infections which allow system to recommend effective
drug prescriptions.
• In a controlled test its performance was rated to be equal that of human specialist
•
• XCON
• It uses rules to determine optimal system configuration that fits customer requirements.
• The system was able to handle request in 1 minute that typically took sales team 20-30 minutes
•
13. Newer Application of ES
• CREDIT ANALYSIS SYSTEM
• ES can help lender analyze customers credit record and determine proper credit line
• Rules in knowledge base can help assess risk and risk management policies
•
PENSION FUND ADVISORS
• This system maintains an up to date knowledge base to give participants advice concerning impact of regulation
changes and conformance with new standard
•
AUTOMATED HELP DESKS
• remedy.com is a rule based help desk solution for small business to deal with customers request efficiently .
• Incoming mails are passed on to business rule engine and messages are sent too proper technicians who resolves
problem and track issues more effectively
•
HOMELAND SECURITY
• Such systems are designed to asses terrorist threats and provide
• An assessment of vulnerability to terrorist attack
• Indicators of terrorist surveillance activities
• Guidance for managing interaction with potential terrorist
•
14. Newer Application of ES
• MARKET SURVEILLANCE SYSTEM
• This are systems that uses rule based inference and data mining to
monitor stocks and futures markets for suspicious pattern.
•
• BUSINESS PROCESS REENGINEERING SYSTEMS
• Reengineering requires exploitation of information technology to
improve business process
• KBS are used to analysing the workflow of businesses process
reengineering
•
•
15.
16. STRUCTURE OF EXPERT SYSTEMS
• ES can be viewed as two environments
• Development environment- Populate knowledgebase with expert
knowledge
• Consultation Environment-To obtain advice and solve problems using
expert knowledge
• The three major components that appear in every ES are
• Knowledgebase
• Inference engine
• User interface
• In general ES that interacts with user has following additional components
• Knowledge acquisition subsystem
• Blackboard(workplace)
• Explanation subsystem
• Knowledge refining system
17. STRUCTURE OF EXPERT SYSTEMS
• KNOWLEDGE ACQUISITION SUBSYSTEM
• Knowledge question is accumulation, transfer and transformation of expertise from experts or documented
knowledge sources into computer system.
• Acquiring knowledge from experts is complex task and is typically done but knowledge engineer who interacts
with one or more experts and builds knowledge base.
•
• KNOWLEDGEBASE
• It is the foundation of ES and contains relevant knowledge
• A typical Knowledge base include
• Facts that scribe a specific problem
• Special heuristics or rules that represent deep expert knowledge to solve the problem
•
INFERENCE ENGINE
• It is also called brain of ES.
• It is rule interpreter
• It is basically a copter program that provides methodology or reasoning about information in knowledgebase and
formulates appropriate decisions.
•
USER INTERFACE
• I provides communication user and computer
• Existing system uses graphical or textual question and answer approach to interact with user
•
18. • BLACKBOARD(WORKPLACE)
• It’s an area of working memory set aside for
description of current problem
• Three types of decision can be recorded on
workplace
• Plan (how to attack problem)
• An agenda(potential action awaiting execution)
• Solution(courses of action system has generated
so far)
19.
20. • KNOWLEDGE REFINING SYSTEM
• Human experts have knowledge refining system that is
they can analyze their own knowledge and its effectiveness
and learn from it and improve it on for future consultations.
• Similarly such evaluation is necessary in expert systems too.
• The critical component of knowledge refinement system is
self-learning mechanism that allow it to adjust its
knowledgebase and its processing of knowledge based on
evaluation of its past performances.