expert system presentation pdf

Expert Systems

• December 2016
• In book: Kirk-Othmer Encyclopedia of Chemical Technology
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Expert system

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Dr. Meenu Gupta

Artificial Intelligence is a software or machine that can think and understand complex problems. And Expert System is uses artificial intelligence technology or methodology to solve problems within a specialized domain that requires human expertise. Expert system uses human knowledge to solve the complex problems in various areas as science, engineering, business, medicine, weather forecasting and the organizations employing the technology of expert system has seen an increase in the quality and efficiency. Expert system is a computer software that emulates the decision making ability of a human expert. The expert system represents knowledge acquired from human expert as data or rules within the compute.

Earl Sacerdoti

A pioneer in commercializing expert system technolo gy, Teknowledge released two so-called "expert system shells" in mid-1984. It soon became pparent that product customers were using these tools in ways that differed from what the dev elopers envisioned. Even internal to Teknowledge, there was considerably controversy ove r the value of these tools. The debate centered on the tradeoffs between the leverage they provided for certain portions of the system development task and the restrictions they imposed n the ways knowledge could be represented.

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tilotma sharma

In this paper we will discuss a survey on various work done in different areas using Expert System(ES). Different methods and algorithms are used in different areas to solve the problem. An expert system is created to solve problems in a particular narrow domain of expertise. There are two different ways developers look at application areas for ES i.e. first, functional nature of problem and secondly, the application domain. One of the key characteristics of an ES is the explanation facility. With this capability, an ES can explain how it arrives at its conclusion. This survey is aimed at reviewing the recent scientific aspects of various research done on ES using different techniques like Case Based Reasoning(CBR), Rule-Based, Fuzzy Logic etc. Keywords— Expert System, Case Based Reasoning, CLIPS,

: This appendix provides assistance in selecting the most appropriate tool for a particular expert system development project. A strong foundation in the usage of any specialized tool (e.g., chemical assay equipment or an expert system shell) is prerequisite to tool selection. Familiarity and reasonable facility with expert systems technology and knowledge engineering are necessary prior to applying the techniques presented in this appendix. This appendix outlines a selection method that was adapted from one developed at The RAND corporation for expert system tool evaluation. It is applicable after the decision has been made that such a tool is needed and the characteristics of that need are known. In particular, this involves (1) understanding the problem that shows potential for applied expert system technology and (2) choosing the best solution alternative. The term expert system means a system built using a knowledge-based approach to software development that applies expert kno...

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Chapter 1: Introduction to Expert Systems

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Modelling with expert systems. Expert systems Modelling with expert systems Coaching modelling with expert systems Advantages and limitations of modelling.

Supporting Business Decisions Expert Systems. Expert system definition Possible working definition of an expert system: –“A computer system with a knowledge.

Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall Chapter 7 Technologies to Manage Knowledge: Artificial Intelligence.

Introduction to Expert System Chapter 11. Rule-Based AI 2013/5/2 1.

4 Intelligent Systems.

Chapter 12: Expert Systems Design Examples

Chapter 11 Artificial Intelligence and Expert Systems.

Introduction to Expert Systems

Artificial Intelligence

1 Chapter 9 Rules and Expert Systems. 2 Chapter 9 Contents (1) l Rules for Knowledge Representation l Rule Based Production Systems l Forward Chaining.

© C. Kemke1Reasoning - Introduction COMP 4200: Expert Systems Dr. Christel Kemke Department of Computer Science University of Manitoba.

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall Chapter Chapter 7: Expert Systems and Artificial Intelligence Decision Support.

EXPERT SYSTEMS Part I.

Chapter 12: Intelligent Systems in Business

MSIS 110: Introduction to Computers; Instructor: S. Mathiyalakan1 Specialized Business Information Systems Chapter 11.

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Earlier peak photosynthesis timing potentially escalates global wildfires.

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Gengke Lai, Jialing Li, Jun Wang, Chaoyang Wu, Yongguang Zhang, Constantin M Zohner, Josep Peñuelas, Quansheng Ge, Earlier peak photosynthesis timing potentially escalates global wildfires, National Science Review , 2024;, nwae292, https://doi.org/10.1093/nsr/nwae292

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More intense fire weather due to climate change is implicated as a key driver of recent extreme wildfire events. As fuel stocks, the roles of vegetation and its phenology change in wildfire dynamics, however, are not fully appreciated. Using long-term satellite-based burned areas and photosynthesis observations, we reveal that an earlier peak photosynthesis timing (PPT) potentially acts to escalate subsequent wildfires, with an increase in the global average burned fraction by 0.021% (∼2.20 Mha) for every additional day of PPT advancement. Satellite observations and the Earth System modeling consistently show that this fire escalation is likely due to intensified drought conditions and increased fuel availability associated with the climate feedback arising from earlier PPT. Current fire-enabled dynamic global vegetation models can reproduce the observed negative correlation between PPT and burned area but underestimate the strength of the relationship notably. Given the continued PPT advancement owing to climate change, the bioclimatic effects of vegetation phenology change suggest a potentially pervasive upward pressure on future wildfires.

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GCSE results day 2024: Everything you need to know including the number grading system

Thousands of students across the country will soon be finding out their GCSE results and thinking about the next steps in their education.   

Here we explain everything you need to know about the big day, from when results day is, to the current 9-1 grading scale, to what your options are if your results aren’t what you’re expecting.  

When is GCSE results day 2024?  

GCSE results day will be taking place on Thursday the 22 August.     

The results will be made available to schools on Wednesday and available to pick up from your school by 8am on Thursday morning.  

Schools will issue their own instructions on how and when to collect your results.   

When did we change to a number grading scale?  

The shift to the numerical grading system was introduced in England in 2017 firstly in English language, English literature, and maths.  

By 2020 all subjects were shifted to number grades. This means anyone with GCSE results from 2017-2020 will have a combination of both letters and numbers.  

The numerical grading system was to signal more challenging GCSEs and to better differentiate between students’ abilities - particularly at higher grades between the A *-C grades. There only used to be 4 grades between A* and C, now with the numerical grading scale there are 6.  

What do the number grades mean?  

The grades are ranked from 1, the lowest, to 9, the highest.  

The grades don’t exactly translate, but the two grading scales meet at three points as illustrated below.  

The bottom of grade 7 is aligned with the bottom of grade A, while the bottom of grade 4 is aligned to the bottom of grade C.    

Meanwhile, the bottom of grade 1 is aligned to the bottom of grade G.  

What to do if your results weren’t what you were expecting?  

If your results weren’t what you were expecting, firstly don’t panic. You have options.  

First things first, speak to your school or college – they could be flexible on entry requirements if you’ve just missed your grades.   

They’ll also be able to give you the best tailored advice on whether re-sitting while studying for your next qualifications is a possibility.   

If you’re really unhappy with your results you can enter to resit all GCSE subjects in summer 2025. You can also take autumn exams in GCSE English language and maths.  

Speak to your sixth form or college to decide when it’s the best time for you to resit a GCSE exam.  

Look for other courses with different grade requirements     

Entry requirements vary depending on the college and course. Ask your school for advice, and call your college or another one in your area to see if there’s a space on a course you’re interested in.    

Consider an apprenticeship    

Apprenticeships combine a practical training job with study too. They’re open to you if you’re 16 or over, living in England, and not in full time education.  

As an apprentice you’ll be a paid employee, have the opportunity to work alongside experienced staff, gain job-specific skills, and get time set aside for training and study related to your role.   

You can find out more about how to apply here .  

Talk to a National Careers Service (NCS) adviser    

The National Career Service is a free resource that can help you with your career planning. Give them a call to discuss potential routes into higher education, further education, or the workplace.   

Whatever your results, if you want to find out more about all your education and training options, as well as get practical advice about your exam results, visit the  National Careers Service page  and Skills for Careers to explore your study and work choices.   

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NVIDIA to Present Innovations at Hot Chips That Boost Data Center Performance and Energy Efficiency

A deep technology conference for processor and system architects from industry and academia has become a key forum for the trillion-dollar data center computing market.

At Hot Chips 2024 next week, senior NVIDIA engineers will present the latest advancements powering the NVIDIA Blackwell platform, plus research on liquid cooling for data centers and AI agents for chip design.

They’ll share how:

• NVIDIA Blackwell brings together multiple chips, systems and NVIDIA CUDA software to power the next generation of AI across use cases, industries and countries.
• NVIDIA GB200 NVL72 — a multi-node, liquid-cooled, rack-scale solution that connects 72 Blackwell GPUs and 36 Grace CPUs — raises the bar for AI system design.
• NVLink interconnect technology provides all-to-all GPU communication, enabling record high throughput and low-latency inference for generative AI.
• The NVIDIA Quasar Quantization System pushes the limits of physics to accelerate AI computing.
• NVIDIA researchers are building AI models that help build processors for AI.

An NVIDIA Blackwell talk, taking place Monday, Aug. 26, will also spotlight new architectural details and examples of generative AI models running on Blackwell silicon.

It’s preceded by three tutorials on Sunday, Aug. 25, that will cover how hybrid liquid-cooling solutions can help data centers transition to more energy-efficient infrastructure and how AI models, including large language model (LLM)-powered agents, can help engineers design the next generation of processors.

Together, these presentations showcase the ways NVIDIA engineers are innovating across every area of data center computing and design to deliver unprecedented performance, efficiency and optimization.

Be Ready for Blackwell

NVIDIA Blackwell is the ultimate full-stack computing challenge. It comprises multiple NVIDIA chips, including the Blackwell GPU, Grace CPU, BlueField data processing unit, ConnectX network interface card, NVLink Switch , Spectrum Ethernet switch and Quantum InfiniBand switch.

Ajay Tirumala and Raymond Wong, directors of architecture at NVIDIA, will provide a first look at the platform and explain how these technologies work together to deliver a new standard for AI and accelerated computing performance while advancing energy efficiency.

The multi-node NVIDIA GB200 NVL72 solution is a perfect example. LLM inference requires low-latency, high-throughput token generation. GB200 NVL72 acts as a unified system to deliver up to 30x faster inference for LLM workloads, unlocking the ability to run trillion-parameter models in real time.

Tirumala and Wong will also discuss how the NVIDIA Quasar Quantization System — which brings together algorithmic innovations, NVIDIA software libraries and tools, and Blackwell’s second-generation Transformer Engine — supports high accuracy on low-precision models, highlighting examples using LLMs and visual generative AI.

Keeping Data Centers Cool

The traditional hum of air-cooled data centers may become a relic of the past as researchers develop more efficient and sustainable solutions that use hybrid cooling, a combination of air and liquid cooling.

Liquid-cooling techniques move heat away from systems more efficiently than air, making it easier for computing systems to stay cool even while processing large workloads. The equipment for liquid cooling also takes up less space and consumes less power than air-cooling systems, allowing data centers to add more server racks — and therefore more compute power — in their facilities.

Ali Heydari, director of data center cooling and infrastructure at NVIDIA, will present several designs for hybrid-cooled data centers.

Some designs retrofit existing air-cooled data centers with liquid-cooling units, offering a quick and easy solution to add liquid-cooling capabilities to existing racks. Other designs require the installation of piping for direct-to-chip liquid cooling using cooling distribution units or by entirely submerging servers in immersion cooling tanks. Although these options demand a larger upfront investment, they lead to substantial savings in both energy consumption and operational costs.

Heydari will also share his team’s work as part of COOLERCHIPS , a U.S. Department of Energy program to develop advanced data center cooling technologies. As part of the project, the team is using the NVIDIA Omniverse platform to create physics-informed digital twins that will help them model energy consumption and cooling efficiency to optimize their data center designs.

AI Agents Chip In for Processor Design

Semiconductor design is a mammoth challenge at microscopic scale. Engineers developing cutting-edge processors work to fit as much computing power as they can onto a piece of silicon a few inches across, testing the limits of what’s physically possible.

AI models are supporting their work by improving design quality and productivity, boosting the efficiency of manual processes and automating some time-consuming tasks. The models include prediction and optimization tools to help engineers rapidly analyze and improve designs, as well as LLMs that can assist engineers with answering questions, generating code, debugging design problems and more.

Mark Ren, director of design automation research at NVIDIA, will provide an overview of these models and their uses in a tutorial. In a second session, he’ll focus on agent-based AI systems for chip design.

AI agents powered by LLMs can be directed to complete tasks autonomously, unlocking broad applications across industries. In microprocessor design, NVIDIA researchers are developing agent-based systems that can reason and take action using customized circuit design tools, interact with experienced designers, and learn from a database of human and agent experiences.

NVIDIA experts aren’t just building this technology — they’re using it . Ren will share examples of how engineers can use AI agents for timing report analysis, cell cluster optimization processes and code generation . The cell cluster optimization work recently won best paper at the first IEEE International Workshop on LLM-Aided Design .

Register for Hot Chips , taking place Aug. 25-27, at Stanford University and online.

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