The concept of intelligent system has emerged in information technology as a type of system derived from successful applications of artificial intelligence. The goal of this presentation is to give a general description of an intelligent system, which integrates classical approaches and recent advances in artificial intelligence. The presentation describes an intelligent system
in a generic way, identifying its main properties and functional components.
Expert System Lecture Notes Chapter 1,2,3,4,5 - Dr.J.VijiPriyaVijiPriya Jeyamani
Chapter 1 Introduction to AI
Chapter 2 Introduction to Expert Systems
Chapter 3 Knowledge Representation
Chapter 4 Inference Methods and Reasoning
Chapter 5 Expert System Design and Pattern Matching
Application of Expert Systems inSystem Analysis & Designfaiza nahin
Design is a field in which a large part of the processes involved is knowledge-based rather than computation-based. Much of this knowledge is experiential and as such lends itself to be encapsulated in an expert system. An analogy is made between analysis and interpretation and between evaluation and comparison of interpretations. Three examples of expert systems carrying out design analysis and evaluation in different domains are described. It is argued that a graphical interface and a model of the elements within the domain are essential parts of any design system.
This presentation is an introduction to artificial intelligence: knowledge engineering. Topics covered are the following: knowledge engineering, requirements of expert systems (ES), functional requirements of ES, structural requirements of ES, components of ES/KBS, knowledge base, inference engine, working memory, expert system, explanation facility, user interface, will ES work for my problem.
The concept of intelligent system has emerged in information technology as a type of system derived from successful applications of artificial intelligence. The goal of this presentation is to give a general description of an intelligent system, which integrates classical approaches and recent advances in artificial intelligence. The presentation describes an intelligent system
in a generic way, identifying its main properties and functional components.
Expert System Lecture Notes Chapter 1,2,3,4,5 - Dr.J.VijiPriyaVijiPriya Jeyamani
Chapter 1 Introduction to AI
Chapter 2 Introduction to Expert Systems
Chapter 3 Knowledge Representation
Chapter 4 Inference Methods and Reasoning
Chapter 5 Expert System Design and Pattern Matching
Application of Expert Systems inSystem Analysis & Designfaiza nahin
Design is a field in which a large part of the processes involved is knowledge-based rather than computation-based. Much of this knowledge is experiential and as such lends itself to be encapsulated in an expert system. An analogy is made between analysis and interpretation and between evaluation and comparison of interpretations. Three examples of expert systems carrying out design analysis and evaluation in different domains are described. It is argued that a graphical interface and a model of the elements within the domain are essential parts of any design system.
This presentation is an introduction to artificial intelligence: knowledge engineering. Topics covered are the following: knowledge engineering, requirements of expert systems (ES), functional requirements of ES, structural requirements of ES, components of ES/KBS, knowledge base, inference engine, working memory, expert system, explanation facility, user interface, will ES work for my problem.
Artificial Intelligence is branch of computer science concerned with the study and creation of computer system that exhibits some form of intelligence.
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
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What are Cognitive Applications? What is exciting about them? They represent a whole new way of human computer interaction and acting on data insights. Introducing IBM Watson and how to develop Cognitive applications. AI, Machine Learning compared and contrasted.
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It gives an individual a general Idea about Expert System and the wide variety of it's Applications.We discuss the scope of Expert System in upcoming Future in various Domains and various Challenges.Some examples are also given of a few Expert Systems.
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.
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.
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.
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
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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/
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
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
2. One of the most successful applications of artificial
intelligence reasoning techniques using facts and rules
has been in building expert systems that embody
knowledge about a specialized field of human
endeavor such as medicine, engineering or business.
An expert system has a unique structure, different
from traditional programs. It is divided into two parts,
one fixed, independent of the expert system: the
inference engine, and one variable: the knowledge
base. To run an expert system, the engine reasons
about the knowledge base like a human.
3. A knowledge based information system that uses its
knowledge about a specific, complex application to act
as an expert consultant to end users.
4. Expert systems were introduced by researchers in the
Stanford Heuristic Programming Project, including
the "father of expert systems" Edward Feigenbaum,
with the Dendral and Mycin systems. Principal
contributors to the technology were Bruce Buchanan,
Edward Shortliffe, Randall Davis, William vanMelle,
Carli Scott and others at Stanford.
5. Knowledge base-facts about specific subject area and
heuristics that express the reasoning procedures of an
expert.
Software resources-inference engine and other
programs refining knowledge and communicating
with users.
6. Interpretation-inferring situation descriptions from
sensor data.
Prediction-inferring likely consequences of given
situations.
Diagnosis-inferring malfunctions from observations.
Design-configuring objects under constraints.
Planning-designing actions.
Control-governing overall system behavior.
7. Case based-examples of past performance, occurances
and experiences.
Frame based-network of entities consisting of a
complex package of data values.
Object based-date and the methods that act on those
data.
Rule based-rules and statements that typically take the
form of a premise and a conclusion.
8. REASONS FOR GROWTH OF DECISION MAKING:
People need to analyze large amounts of information.
People must make decisions quickly.
People must protect the corporate asset of
organizational information.
People must apply sophisticated analysis techniques
such as modeling and forecasting to make good
decisions.
9. Domain : The domain or subject area of the problem is relatively
small and limited to a well – defined problem area.
Expertise : Solutions to the problem require the efforts of an
expert. That is, a body of knowledge, techniques and intuition is
needed that only a few people possess.
Complexity : Solution of the problem is a complex task that
requires logical inference processing, which would not be
handled as well by conventional information processing.
Structure : The solution process must be able to lope with ill –
structured, uncertain, missing and conflicting data and a
problem situation that changes with the passage of time.
Availability : An expert exists who is articulate and cooperative
and who has the support of the management and users involved
in the development of the proposed system.
10. Permanence - Expert systems do not forget, but human experts may
Reproducibility - Many copies of an expert system can be made, but training
new human experts is time-consuming and expensive
Efficiency - can increase throughput and decrease personnel costs. Although
expert systems are expensive to build and maintain, they are inexpensive to
operate. Development and maintenance costs can be spread over many users.
The overall cost can be quite reasonable when compared to expensive and
scarce human experts.
Consistency - With expert systems similar transactions handled in the same
way. The system will make comparable recommendations for like situations.
Documentation - An expert system can provide permanent documentation
of the decision process
Completeness - An expert system can review all the transactions, a human
expert can only review a sample
Timeliness - Fraud and/or errors can be prevented. Information is available
sooner for decision making
11. Common sense - In addition to a great deal of technical
knowledge, human experts have common sense. It is not yet
known how to give expert systems common sense.
Creativity - Human experts can respond creatively to unusual
situations, expert systems cannot.
Learning - Human experts automatically adapt to changing
environments; expert systems must be explicitly updated.
Sensory Experience - Human experts have available to them a
wide range of sensory experience; expert systems are currently
dependent on symbolic input.
Degradation - Expert systems are not good at recognizing when
no answer exists or when the problem is outside their area of
expertise.
12. Experts can make fast and good decisions regarding
complex situations.
Expertise is a task-specific knowledge acquired from
training, reading and experience.
Expert systems must be constantly updated with new
information.
Human problem solvers are good only if they operate
in a very narrow domain.
Expert systems provide limited explanation
capabilities.