CS 188 is an introductory artificial intelligence course taught at UC Berkeley by professors Dan Klein and Pieter Abbeel. The course covers topics such as search, planning, constraint satisfaction, reasoning under uncertainty, Bayes' nets, decision theory, and machine learning. Students will complete 5 programming projects and 9 homework assignments over the course of the semester to learn about applying AI techniques to applications like natural language, computer vision, robotics, and game playing.
This document provides an introduction to the CS 188: Artificial Intelligence course at UC Berkeley. It discusses key topics that will be covered in the course, including rational decision making, computational rationality, a brief history of AI, current capabilities in areas like natural language processing, computer vision, robotics, and game playing. The course will cover general techniques for designing rational agents and making decisions under uncertainty, with applications to domains like language, vision, games, and more. Students will learn how to apply existing AI techniques to new problem types.
This document provides information about the COMPSCI 270: Artificial Intelligence course at Duke University. The course will be taught in the spring of 2019 by Professor Vincent Conitzer. It will cover topics such as search, constraint satisfaction, game playing, logic, knowledge representation, and planning. Assignments will count for 30% of the grade, midterms for 40%, and a final exam for 30%. The course assumes some programming experience and background in algorithms, probability, and discrete mathematics. It aims to cover general AI techniques applied to tasks like solving Rubik's cubes, scheduling meetings, and playing games like chess.
This document provides information about the COMPSCI 570: Artificial Intelligence course at Duke University taught by Professor Vincent Conitzer. It includes basic course details like meeting times, prerequisites, grading, and an overview of topics to be covered. It also briefly discusses some successes in AI like game playing as well as challenges and concerns regarding superintelligence, consciousness, technological unemployment, and autonomous weapons.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
computer science engineering spe ialized in artificial IntelligenceKhanKhaja1
Dr. C. Lee Giles is a professor at Penn State University who teaches a course on artificial intelligence and information sciences. The document provides an overview of artificial intelligence including definitions, theories, impact on information science, and topics covered in the course such as machine learning, information retrieval, text processing, and social networks. It also discusses the scientific method applied to developing theories in information sciences and contrasts weak and strong definitions of artificial intelligence.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
Dr. C. Lee Giles is a professor at Penn State University who teaches a course on artificial intelligence and information sciences. The document provides an overview of artificial intelligence including definitions, theories, impact on information science, and topics covered in the course such as machine learning, information retrieval, text processing, and social networks. It also discusses the scientific method applied to developing theories in information sciences and contrasts weak and strong definitions of artificial intelligence.
This document provides an introduction to the CS 188: Artificial Intelligence course at UC Berkeley. It discusses key topics that will be covered in the course, including rational decision making, computational rationality, a brief history of AI, current capabilities in areas like natural language processing, computer vision, robotics, and game playing. The course will cover general techniques for designing rational agents and making decisions under uncertainty, with applications to domains like language, vision, games, and more. Students will learn how to apply existing AI techniques to new problem types.
This document provides information about the COMPSCI 270: Artificial Intelligence course at Duke University. The course will be taught in the spring of 2019 by Professor Vincent Conitzer. It will cover topics such as search, constraint satisfaction, game playing, logic, knowledge representation, and planning. Assignments will count for 30% of the grade, midterms for 40%, and a final exam for 30%. The course assumes some programming experience and background in algorithms, probability, and discrete mathematics. It aims to cover general AI techniques applied to tasks like solving Rubik's cubes, scheduling meetings, and playing games like chess.
This document provides information about the COMPSCI 570: Artificial Intelligence course at Duke University taught by Professor Vincent Conitzer. It includes basic course details like meeting times, prerequisites, grading, and an overview of topics to be covered. It also briefly discusses some successes in AI like game playing as well as challenges and concerns regarding superintelligence, consciousness, technological unemployment, and autonomous weapons.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
computer science engineering spe ialized in artificial IntelligenceKhanKhaja1
Dr. C. Lee Giles is a professor at Penn State University who teaches a course on artificial intelligence and information sciences. The document provides an overview of artificial intelligence including definitions, theories, impact on information science, and topics covered in the course such as machine learning, information retrieval, text processing, and social networks. It also discusses the scientific method applied to developing theories in information sciences and contrasts weak and strong definitions of artificial intelligence.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
Dr. C. Lee Giles is a professor at Penn State University who teaches a course on artificial intelligence and information sciences. The document provides an overview of artificial intelligence including definitions, theories, impact on information science, and topics covered in the course such as machine learning, information retrieval, text processing, and social networks. It also discusses the scientific method applied to developing theories in information sciences and contrasts weak and strong definitions of artificial intelligence.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system checked.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery problems, like the crankshaft position sensor
This presentation give an introduction to Artificial Intelligence subjectiveness and history. The primary goal of the presentation is to provide a deep enough understanding of Artificial Narrow Intelligence and Artificial General Intelligence so that the people can appreciate the strengths or weaknesses of the AI. The presentation also includes a classification(the main domains of AI) and the most relevant examples from the past decades. In the second part it provides some statistics and future possible applications and forecasts.
The document provides an overview of artificial intelligence (AI), including its main areas of study, progress made, applications, and ongoing challenges. It discusses how AI involves automated perception, learning, reasoning and planning. While recognition and learning have advanced, planning and general reasoning remain challenging. The document outlines applications in industries like finance, medicine and transportation, but notes that many problems remain unsolved, making AI an active area of research.
This document provides information about an artificial intelligence course, including the instructor, grading breakdown, schedule, and topics. Some key areas of AI discussed are search techniques, constraint satisfaction problems, game playing, logic, classification, and intelligent agents. The history and current state of the art in AI are also reviewed, covering successes in robotics, speech recognition, planning, and other domains.
This document summarizes Jim Gray's 1998 Turing Lecture which discusses remaining challenges in information technology research. It identifies the need for long-term, university-led research projects supported by government funding. Specific challenges mentioned include making parallel programming easier, improving the scalability of databases and transaction processing systems, and advancing the state of artificial intelligence to pass the Turing Test within the next 50 years. The document outlines properties of effective long-term research goals and provides examples like devising an architecture that scales indefinitely.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
This document provides an overview of an introductory lecture on artificial intelligence and expert systems. It discusses the Turing Test, definitions of artificial intelligence, a brief history of AI including important figures and milestones, and examples of what current AI systems can and cannot do.
- The document discusses artificial intelligence, including its history, key areas such as knowledge representation and learning, and applications in areas like consumer marketing, identification technologies, predicting stock markets, and machine translation.
- While progress has been made in areas like recognition and learning, challenges remain in full natural language understanding, human-level planning and decision making. AI is being applied across many industries but remains an active area of research.
This document provides an overview of the history and current state of artificial intelligence. It discusses key events like the Dartmouth workshop in 1956 which is seen as the official birth of AI. The document also explores different applications of AI like in movies, news, and real world tasks. It discusses challenges for the future like ensuring AI is beneficial to humanity and aligned with human values and preferences.
This document discusses artificial intelligence (AI) and provides several quotes about AI from experts such as Stephen Hawking, Ray Kurzweil, Elon Musk, and others. It then summarizes the history of AI and key developments that led to the current "third AI boom". These include advances in machine learning, deep learning, self-driving cars, smart assistants, and more. The document also discusses challenges for AI such as the need for AI systems to interact and react, as well as the impact of AI on jobs and the need for reskilling workers.
This is my talk delivered 06/04/2024 at the CUBE event (https://www.uni-corvinus.hu/post/landing-page/cube/?lang=en) at the Gellért Campus of the Corvinus University.
The document discusses artificial intelligence and provides information on various AI topics. It includes a list of 9 NPTEL video links on topics related to unit 1 of an AI course, learning outcomes of the course, definitions and descriptions of AI, areas and applications of AI, a brief history of AI, task domains and techniques in AI, and examples of search problems and search methods. Depth-first search is described as a method that exhaustively explores branches in a search tree to the maximum depth until a solution is found.
AI Introduction: AI is the new electricity (by Slash)Andries De Vos
Introductory talk on AI, given at Emerald Hub, co-working space, located at the Phnom Penh International University (PPIU) on 3 May 2017 to a tech audience.
Note:
Title based on talk from Andrew Ng with the same name.
Source material where relevant referenced in speaker notes.
Originally prepared by Andries, CEO of Slash, for an internal Slash team talk.
Artificial Intelligence (AI) is quickly moving from a science-fiction concept to reality where machines now have the capability to perform tasks commonly associated with humans. We are starting to see our society transformed because of AI, so having a better understanding of what it is and what it is capable of doing is essential. AI helps power Amazon’s Alexa personal assistant, Google’s Deep Dream neural network, various marketing initiatives, health applications, the aviation industry, and much more. In this webinar:
- Discover what Artificial Intelligence (AI) is and how it is becoming a “machine trait.”
- Gain an appreciation of AI pioneers like John McCarthy, Alan Turing, Marvin Minsky et al.
- Learn how AI works and explore some applications that could play a role in your library.
- Reflect on the future of AI and the implications for libraries and society in general.
- Special Guest, Owen Cegielski from STEM School and Academy in Highlands Ranch, Colorado, will discuss various AI projects.
The document discusses various topics related to artificial intelligence including the Turing test, machine learning, and natural language processing. It provides definitions of AI, discusses its early history and development, comparisons to human intelligence, examples of applied AI, and challenges remaining for achieving human-level intelligence.
The document discusses concepts related to programming, machine learning, and the development of software and computers. It provides definitions of terms like program, learning, and machine learning. It also contrasts analog and digital data as well as qualitative and quantitative information. The document discusses the evolution from analog to digital and provides examples of how analog information is translated into binary code. It examines different organizational cultures that contributed to the development of personal computers like IBM, Homebrew Computer Club, and Xerox PARC. It also analyzes leadership styles and personalities based on a framework of artist, craftsman, and technocrat.
BSidesLV 2013 - Using Machine Learning to Support Information SecurityAlex Pinto
Big Data, Data Science, Machine Learning and Analytics are a few of the new buzzwords that have invaded out industry of late. Again we are being sold a unicorn-laden, silver-bullet panacea by heavy handed marketing folks, evoking an expected pushback from the most enlightened members of our community. However, as was the case before, there might just be enough technical meat in there to help out with our security challenges and the overwhelming odds we face everyday. And if so, what do we as a community have to know about these technologies in order to be better professionals? Can we really use the data we have been collecting to help automate our security decision making? Is a robot going to steal my job?
If you are interested in what is behind this marketing buzz and are not scared of a little math, this talk would like to address some insights into applying Machine Learning techniques to data any of us have easy access to, and try to bring home the point that if all of this technology can be used to show us “better” ads in social media and track our behavior online (and a bit more than that) it can also be used to defend our networks as well.
This document provides a summary of artificial intelligence including definitions, history, and whether computers can perform certain tasks. It discusses four approaches to defining AI: (1) thinking like humans through cognitive science, (2) thinking rationally using logic, (3) acting like humans as in the Turing test, and (4) acting rationally to achieve the best outcomes. The document also summarizes key events in the history of AI and whether computers can beat humans at games, recognize speech, understand language, learn, see, plan, and more.
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, including early milestones and the state of the art, such as Deep Blue defeating Kasparov in chess in 1997.
3) An overview of different views of AI, including acting humanly (Turing test), thinking humanly (cognitive modeling), thinking rationally (logic), and the textbook's approach of acting rationally as a rational agent.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
More Related Content
Similar to SP14 CS188 Lecture 1 -- Introduction.pptx
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system checked.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery problems, like the crankshaft position sensor
This presentation give an introduction to Artificial Intelligence subjectiveness and history. The primary goal of the presentation is to provide a deep enough understanding of Artificial Narrow Intelligence and Artificial General Intelligence so that the people can appreciate the strengths or weaknesses of the AI. The presentation also includes a classification(the main domains of AI) and the most relevant examples from the past decades. In the second part it provides some statistics and future possible applications and forecasts.
The document provides an overview of artificial intelligence (AI), including its main areas of study, progress made, applications, and ongoing challenges. It discusses how AI involves automated perception, learning, reasoning and planning. While recognition and learning have advanced, planning and general reasoning remain challenging. The document outlines applications in industries like finance, medicine and transportation, but notes that many problems remain unsolved, making AI an active area of research.
This document provides information about an artificial intelligence course, including the instructor, grading breakdown, schedule, and topics. Some key areas of AI discussed are search techniques, constraint satisfaction problems, game playing, logic, classification, and intelligent agents. The history and current state of the art in AI are also reviewed, covering successes in robotics, speech recognition, planning, and other domains.
This document summarizes Jim Gray's 1998 Turing Lecture which discusses remaining challenges in information technology research. It identifies the need for long-term, university-led research projects supported by government funding. Specific challenges mentioned include making parallel programming easier, improving the scalability of databases and transaction processing systems, and advancing the state of artificial intelligence to pass the Turing Test within the next 50 years. The document outlines properties of effective long-term research goals and provides examples like devising an architecture that scales indefinitely.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
This document provides an overview of an introductory lecture on artificial intelligence and expert systems. It discusses the Turing Test, definitions of artificial intelligence, a brief history of AI including important figures and milestones, and examples of what current AI systems can and cannot do.
- The document discusses artificial intelligence, including its history, key areas such as knowledge representation and learning, and applications in areas like consumer marketing, identification technologies, predicting stock markets, and machine translation.
- While progress has been made in areas like recognition and learning, challenges remain in full natural language understanding, human-level planning and decision making. AI is being applied across many industries but remains an active area of research.
This document provides an overview of the history and current state of artificial intelligence. It discusses key events like the Dartmouth workshop in 1956 which is seen as the official birth of AI. The document also explores different applications of AI like in movies, news, and real world tasks. It discusses challenges for the future like ensuring AI is beneficial to humanity and aligned with human values and preferences.
This document discusses artificial intelligence (AI) and provides several quotes about AI from experts such as Stephen Hawking, Ray Kurzweil, Elon Musk, and others. It then summarizes the history of AI and key developments that led to the current "third AI boom". These include advances in machine learning, deep learning, self-driving cars, smart assistants, and more. The document also discusses challenges for AI such as the need for AI systems to interact and react, as well as the impact of AI on jobs and the need for reskilling workers.
This is my talk delivered 06/04/2024 at the CUBE event (https://www.uni-corvinus.hu/post/landing-page/cube/?lang=en) at the Gellért Campus of the Corvinus University.
The document discusses artificial intelligence and provides information on various AI topics. It includes a list of 9 NPTEL video links on topics related to unit 1 of an AI course, learning outcomes of the course, definitions and descriptions of AI, areas and applications of AI, a brief history of AI, task domains and techniques in AI, and examples of search problems and search methods. Depth-first search is described as a method that exhaustively explores branches in a search tree to the maximum depth until a solution is found.
AI Introduction: AI is the new electricity (by Slash)Andries De Vos
Introductory talk on AI, given at Emerald Hub, co-working space, located at the Phnom Penh International University (PPIU) on 3 May 2017 to a tech audience.
Note:
Title based on talk from Andrew Ng with the same name.
Source material where relevant referenced in speaker notes.
Originally prepared by Andries, CEO of Slash, for an internal Slash team talk.
Artificial Intelligence (AI) is quickly moving from a science-fiction concept to reality where machines now have the capability to perform tasks commonly associated with humans. We are starting to see our society transformed because of AI, so having a better understanding of what it is and what it is capable of doing is essential. AI helps power Amazon’s Alexa personal assistant, Google’s Deep Dream neural network, various marketing initiatives, health applications, the aviation industry, and much more. In this webinar:
- Discover what Artificial Intelligence (AI) is and how it is becoming a “machine trait.”
- Gain an appreciation of AI pioneers like John McCarthy, Alan Turing, Marvin Minsky et al.
- Learn how AI works and explore some applications that could play a role in your library.
- Reflect on the future of AI and the implications for libraries and society in general.
- Special Guest, Owen Cegielski from STEM School and Academy in Highlands Ranch, Colorado, will discuss various AI projects.
The document discusses various topics related to artificial intelligence including the Turing test, machine learning, and natural language processing. It provides definitions of AI, discusses its early history and development, comparisons to human intelligence, examples of applied AI, and challenges remaining for achieving human-level intelligence.
The document discusses concepts related to programming, machine learning, and the development of software and computers. It provides definitions of terms like program, learning, and machine learning. It also contrasts analog and digital data as well as qualitative and quantitative information. The document discusses the evolution from analog to digital and provides examples of how analog information is translated into binary code. It examines different organizational cultures that contributed to the development of personal computers like IBM, Homebrew Computer Club, and Xerox PARC. It also analyzes leadership styles and personalities based on a framework of artist, craftsman, and technocrat.
BSidesLV 2013 - Using Machine Learning to Support Information SecurityAlex Pinto
Big Data, Data Science, Machine Learning and Analytics are a few of the new buzzwords that have invaded out industry of late. Again we are being sold a unicorn-laden, silver-bullet panacea by heavy handed marketing folks, evoking an expected pushback from the most enlightened members of our community. However, as was the case before, there might just be enough technical meat in there to help out with our security challenges and the overwhelming odds we face everyday. And if so, what do we as a community have to know about these technologies in order to be better professionals? Can we really use the data we have been collecting to help automate our security decision making? Is a robot going to steal my job?
If you are interested in what is behind this marketing buzz and are not scared of a little math, this talk would like to address some insights into applying Machine Learning techniques to data any of us have easy access to, and try to bring home the point that if all of this technology can be used to show us “better” ads in social media and track our behavior online (and a bit more than that) it can also be used to defend our networks as well.
This document provides a summary of artificial intelligence including definitions, history, and whether computers can perform certain tasks. It discusses four approaches to defining AI: (1) thinking like humans through cognitive science, (2) thinking rationally using logic, (3) acting like humans as in the Turing test, and (4) acting rationally to achieve the best outcomes. The document also summarizes key events in the history of AI and whether computers can beat humans at games, recognize speech, understand language, learn, see, plan, and more.
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, including early milestones and the state of the art, such as Deep Blue defeating Kasparov in chess in 1997.
3) An overview of different views of AI, including acting humanly (Turing test), thinking humanly (cognitive modeling), thinking rationally (logic), and the textbook's approach of acting rationally as a rational agent.
Similar to SP14 CS188 Lecture 1 -- Introduction.pptx (20)
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
1. CS 188: Artificial Intelligence
Introduction
Instructors: Dan Klein and Pieter Abbeel
University of California, Berkeley
[These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All materials available at http://ai.berkeley.edu.]
3. Course Information
Communication:
Announcements on webpage
Questions? Discussion on piazza
Staff email: cs188-staff@lists
This course is webcast (Sp14 live videos)
+ Fa12 edited videos (1-11)
+ Fa13 live videos
Course technology:
New infrastructure
Autograded projects, interactive
homeworks (unlimited submissions!) +
regular homework
Help us make it awesome!
Sign up at: inst.eecs.berkeley.edu/~cs188
4. Course Information
Prerequisites:
(CS 61A or B) and (Math 55 or CS 70)
Strongly recommended: CS61A, CS61B and CS70
There will be a lot of math (and programming)
Work and Grading:
5 programming projects: Python, groups of 1 or 2
5 late days for semester, maximum 2 per project
~9 homework assignments:
Part 1: interactive, solve together, submit alone
Part 2: written, solve together, write up alone, electronic submission through
pandagrader [these problems will be questions from past exams]
Two midterms, one final
Participation can help on margins
Fixed scale
Academic integrity policy
Contests!
5. Textbook
Not required, but for students who want to
read more we recommend
Russell & Norvig, AI: A Modern Approach, 3rd Ed.
Warning: Not a course textbook, so our
presentation does not necessarily follow the
presentation in the book.
6. Important This Week
• Important this week:
• Register for the class on edx
• Register for the class on piazza --- our main resource for discussion and communication
• P0: Python tutorial is out (due on Friday 1/24 at 5pm)
• One-time (optional) P0 lab hours this week
• Wed 2-3pm, Thu 4-5pm --- all in 330 Soda
• Get (optional) account forms in front after class
• Math self-diagnostic up on web page --- important to check your preparedness for second half
• Also important:
• Sections start next week. You are free to attend any section, priority in section you signed up for if among
first 35 to sign up. Sign-up first come first served on Friday at 2pm on piazza poll.
• If you are wait-listed, you might or might not get in depending on how many students drop. Contact
Michael-David Sasson (msasson@cs.berkeley.edu) with any questions on the process.
• Office Hours start next week, this week there are the P0 labs and you can catch the professors after lecture
7. Today
What is artificial intelligence?
What can AI do?
What is this course?
9. What is AI?
The science of making machines that:
Think like people
Act like people
Think rationally
Act rationally
10. Rational Decisions
We’ll use the term rational in a very specific, technical way:
Rational: maximally achieving pre-defined goals
Rationality only concerns what decisions are made
(not the thought process behind them)
Goals are expressed in terms of the utility of outcomes
Being rational means maximizing your expected utility
A better title for this course would be:
Computational Rationality
12. What About the Brain?
Brains (human minds) are very good
at making rational decisions, but not
perfect
Brains aren’t as modular as software,
so hard to reverse engineer!
“Brains are to intelligence as wings
are to flight”
Lessons learned from the brain:
memory and simulation are key to
decision making
14. A (Short) History of AI
1940-1950: Early days
1943: McCulloch & Pitts: Boolean circuit model of brain
1950: Turing's “Computing Machinery and Intelligence”
1950—70: Excitement: Look, Ma, no hands!
1950s: Early AI programs, including Samuel's checkers program,
Newell & Simon's Logic Theorist, Gelernter's Geometry Engine
1956: Dartmouth meeting: “Artificial Intelligence” adopted
1965: Robinson's complete algorithm for logical reasoning
1970—90: Knowledge-based approaches
1969—79: Early development of knowledge-based systems
1980—88: Expert systems industry booms
1988—93: Expert systems industry busts: “AI Winter”
1990—: Statistical approaches
Resurgence of probability, focus on uncertainty
General increase in technical depth
Agents and learning systems… “AI Spring”?
2000—: Where are we now?
15. What Can AI Do?
Quiz: Which of the following can be done at present?
Play a decent game of table tennis?
Play a decent game of Jeopardy?
Drive safely along a curving mountain road?
Drive safely along Telegraph Avenue?
Buy a week's worth of groceries on the web?
Buy a week's worth of groceries at Berkeley Bowl?
Discover and prove a new mathematical theorem?
Converse successfully with another person for an hour?
Perform a surgical operation?
Put away the dishes and fold the laundry?
Translate spoken Chinese into spoken English in real time?
Write an intentionally funny story?
16. Unintentionally Funny Stories
One day Joe Bear was hungry. He asked his friend
Irving Bird where some honey was. Irving told him
there was a beehive in the oak tree. Joe walked to
the oak tree. He ate the beehive. The End.
Henry Squirrel was thirsty. He walked over to the
river bank where his good friend Bill Bird was sitting.
Henryslipped and fell in the river. Gravity drowned.
The End.
Once upon a time there was a dishonest fox and a vain crow. One day the
crow was sitting in his tree, holding a piece of cheese in his mouth. He noticed
that he was holding the piece of cheese. He became hungry, and swallowed
the cheese. The fox walked over to the crow. The End.
[Shank, Tale-Spin System, 1984]
17. Natural Language
Speech technologies (e.g. Siri)
Automatic speech recognition (ASR)
Text-to-speech synthesis (TTS)
Dialog systems
Demo: NLP – ASR tvsample.avi
18. Natural Language
Speech technologies (e.g. Siri)
Automatic speech recognition (ASR)
Text-to-speech synthesis (TTS)
Dialog systems
Language processing technologies
Question answering
Machine translation
Web search
Text classification, spam filtering, etc…
19. Vision (Perception)
Images from Erik Sudderth (left), wikipedia (right)
Object and face recognition
Scene segmentation
Image classification
Demo1: VISION – lec_1_t2_video.flv
Demo2: VISION – lec_1_obj_rec_0.mpg
20. Robotics
Robotics
Part mech. eng.
Part AI
Reality much
harder than
simulations!
Technologies
Vehicles
Rescue
Soccer!
Lots of automation…
In this class:
We ignore mechanical aspects
Methods for planning
Methods for control
Images from UC Berkeley, Boston Dynamics, RoboCup, Google
Demo 1: ROBOTICS – soccer.avi
Demo 2: ROBOTICS – soccer2.avi
Demo 3: ROBOTICS – gcar.avi
Demo 4: ROBOTICS – laundry.avi
Demo 5: ROBOTICS – petman.avi
21. Logic
Logical systems
Theorem provers
NASA fault diagnosis
Question answering
Methods:
Deduction systems
Constraint satisfaction
Satisfiability solvers (huge advances!)
Image from Bart Selman
22. Game Playing
Classic Moment: May, '97: Deep Blue vs. Kasparov
First match won against world champion
“Intelligent creative” play
200 million board positions per second
Humans understood 99.9 of Deep Blue's moves
Can do about the same now with a PC cluster
Open question:
How does human cognition deal with the
search space explosion of chess?
Or: how can humans compete with computers at all??
1996: Kasparov Beats Deep Blue
“I could feel --- I could smell --- a new kind of intelligence across the table.”
1997: Deep Blue Beats Kasparov
“Deep Blue hasn't proven anything.”
Huge game-playing advances recently, e.g. in Go!
Text from Bart Selman, image from IBM’s Deep Blue pages
23. Decision Making
Applied AI involves many kinds of automation
Scheduling, e.g. airline routing, military
Route planning, e.g. Google maps
Medical diagnosis
Web search engines
Spam classifiers
Automated help desks
Fraud detection
Product recommendations
… Lots more!
24. Designing Rational Agents
An agent is an entity that perceives and acts.
A rational agent selects actions that maximize its
(expected) utility.
Characteristics of the percepts, environment, and
action space dictate techniques for selecting
rational actions
This course is about:
General AI techniques for a variety of problem
types
Learning to recognize when and how a new
problem can be solved with an existing
technique
Agent
?
Sensors
Actuators
Environment
Percepts
Actions
25. Pac-Man as an Agent
Agent
?
Sensors
Actuators
Environment
Percepts
Actions
Pac-Man is a registered trademark of Namco-Bandai Games, used here for educational purposes Demo1: pacman-l1.mp4 or L1D2
26. Course Topics
Part I: Making Decisions
Fast search / planning
Constraint satisfaction
Adversarial and uncertain search
Part II: Reasoning under Uncertainty
Bayes’ nets
Decision theory
Machine learning
Throughout: Applications
Natural language, vision, robotics, games, …
Editor's Notes
Please retain proper attribution, including the reference to ai.berkeley.edu. Thanks!
Who are these? C3PO, what does he do? Essentially google translate, (but with anxiety disorder!)
Smal guy? R2D2 – what does he do, yeah, not so sure
Things got darker: machines come back from the future – to kill us!
90’s : software is scary
Basic fear about what technology might do ?
What if we can’t even tell technology apart from ourselves?
OR maybe it’ll look really different and snarky
Some exceptions like wall-E, positive view of technology (but maybe not of us humans!)
But mostly a worry
[not very worried myself, at least at present]
Top left: Think like people --- cognitive science, neuroscience
Bottom left: act like people --- actually very early definition, dating back to Alan Turing --- Turing test; problem to do really well you start focusing on things like don’t answer too quickly what the square root of 1412 is, don’t spell too well, and make sure you have a favorite movie etc. So it wasn’t really leading us to build intelligence
Think rationally – long tradition dating back to Aristotle --- but not a winner, because difficult to encode how to think, and in the end it’s not about how you think, it’s about how you end up acting
Example of utilities. 10 for A, 1 for each Friday with friends
Thinking machines video --- interviews from back when computers were in the very early years; starting to realize can do something else than arithmetic ; asking where things are headed; many famous people
Million dollar computer with less computation than your phone
MT was
1 – physically wrong, shouldn’t eat beehive
2 – forgot that in drowning not the force is most important but the drownee
3 – not physically wrong, not wrong in a language way, but wrong in the sense that it’s not aware of what is relevant to communicate and what is not
Siri – is that progress?
NLP – ASR tvsample.avi
Yeah, Kasparov comment probably says more about humans than about computers
All applications can be thought of as decision making or useful sub-components of decision making