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.
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, Learning, Planning and Problem solving - [Source: https://www.techopedia.com/definition/190/artificial-intelligence-ai]
This document provides an overview of the CS3243 Foundations of Artificial Intelligence course from NUS for the 2003/2004 semester. It outlines the course details including the textbook, instructor, grading breakdown, and course topics. The course will cover introduction to AI concepts like agents, search, logic, planning, uncertainty, learning, and natural language processing. It also provides background on the history and state of the art in AI, including definitions of what AI is from different perspectives.
Module-1.1.pdf of aiml engineering mod 1fariyaPatel
This document provides an overview of the history and foundations of artificial intelligence (AI). It discusses early definitions and approaches to AI, including the Turing Test. The document also outlines some of the key developments in the early years of AI research, including the work of McCulloch and Pitts on artificial neurons in 1943, the first neural network computer built by Minsky and Edmonds in 1950, and the pivotal 1956 Dartmouth workshop organized by McCarthy that is considered the official birth of the field of AI.
This 3-sentence summary provides an overview of the document:
The document outlines the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2, including the course homepage, textbook, grading breakdown, and a tentative schedule covering topics like search, logic, planning, and learning. It also briefly discusses different views of what constitutes artificial intelligence and provides an abridged history of the field from its philosophical roots to recent successes in games, mathematics, logistics, and spacecraft planning.
Artificial intelligence and machine learning are discussed. AI is defined as making computers intelligent like humans through understanding, reasoning, planning, communication and perception. Machine learning is a subset of AI that allows machines to learn from experience without being explicitly programmed. The document provides background on AI and ML, including definitions, history, and discussions of intelligence and applications.
This document provides an introduction to the course "Lecture 1: Introduction to Artificial Intelligence". The key points covered include:
- The course aims to provide knowledge and understanding of AI concepts like search, game playing, knowledge-based systems, planning and machine learning. Students will learn to use these concepts to solve AI tasks and critically evaluate solutions.
- The document discusses different definitions of artificial intelligence and what it means for a system to be intelligent based on its ability to be flexible, make sense of ambiguous messages, recognize importance, find similarities and differences, and learn from experience.
- The Turing test is introduced as a way to measure machine intelligence by having a human evaluator determine if they are interacting with a computer
Artificial Intelligent introduction or historyArslan Sattar
- Begging for small things can become a habit over time as one comes to rely on getting others to provide even minor things.
- It is better to be self-sufficient as much as possible and only ask for help when truly needed, to avoid forming a dependent mindset.
- Developing independence over small matters builds confidence and strength of character.
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, Learning, Planning and Problem solving - [Source: https://www.techopedia.com/definition/190/artificial-intelligence-ai]
This document provides an overview of the CS3243 Foundations of Artificial Intelligence course from NUS for the 2003/2004 semester. It outlines the course details including the textbook, instructor, grading breakdown, and course topics. The course will cover introduction to AI concepts like agents, search, logic, planning, uncertainty, learning, and natural language processing. It also provides background on the history and state of the art in AI, including definitions of what AI is from different perspectives.
Module-1.1.pdf of aiml engineering mod 1fariyaPatel
This document provides an overview of the history and foundations of artificial intelligence (AI). It discusses early definitions and approaches to AI, including the Turing Test. The document also outlines some of the key developments in the early years of AI research, including the work of McCulloch and Pitts on artificial neurons in 1943, the first neural network computer built by Minsky and Edmonds in 1950, and the pivotal 1956 Dartmouth workshop organized by McCarthy that is considered the official birth of the field of AI.
This 3-sentence summary provides an overview of the document:
The document outlines the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2, including the course homepage, textbook, grading breakdown, and a tentative schedule covering topics like search, logic, planning, and learning. It also briefly discusses different views of what constitutes artificial intelligence and provides an abridged history of the field from its philosophical roots to recent successes in games, mathematics, logistics, and spacecraft planning.
Artificial intelligence and machine learning are discussed. AI is defined as making computers intelligent like humans through understanding, reasoning, planning, communication and perception. Machine learning is a subset of AI that allows machines to learn from experience without being explicitly programmed. The document provides background on AI and ML, including definitions, history, and discussions of intelligence and applications.
This document provides an introduction to the course "Lecture 1: Introduction to Artificial Intelligence". The key points covered include:
- The course aims to provide knowledge and understanding of AI concepts like search, game playing, knowledge-based systems, planning and machine learning. Students will learn to use these concepts to solve AI tasks and critically evaluate solutions.
- The document discusses different definitions of artificial intelligence and what it means for a system to be intelligent based on its ability to be flexible, make sense of ambiguous messages, recognize importance, find similarities and differences, and learn from experience.
- The Turing test is introduced as a way to measure machine intelligence by having a human evaluator determine if they are interacting with a computer
Artificial Intelligent introduction or historyArslan Sattar
- Begging for small things can become a habit over time as one comes to rely on getting others to provide even minor things.
- It is better to be self-sufficient as much as possible and only ask for help when truly needed, to avoid forming a dependent mindset.
- Developing independence over small matters builds confidence and strength of character.
This document provides an overview of artificial intelligence including:
- It defines four approaches to AI: acting humanly through the Turing test, thinking humanly through cognitive modeling, thinking rationally through symbolic logic, and acting rationally as intelligent agents.
- It then summarizes the history of AI from its origins in the 1940s through developments in knowledge-based systems, connectionism, and the emergence of intelligent agents using large datasets.
- Finally, it briefly discusses recent applications of AI such as algorithms used by Facebook, TikTok, and noise cancellation in Zoom calls.
This document provides an overview of an introductory artificial intelligence course. It describes the course topics which include search, logic, probability, and learning techniques. It also summarizes the current state of AI, highlighting successes in logistics, games, natural language processing, vision, robotics, and question answering. The course is intended for juniors and seniors and requires programming skills and exposure to algorithms, calculus, and probability.
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.
This document provides an overview of an introductory course on artificial intelligence. It discusses four views of AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. The textbook advocates the view of "acting rationally" by designing agents that maximize goal achievement given available information. A brief history of AI is also provided, from early work in philosophy, mathematics, and the sciences to landmark developments like Turing's 1950 paper posing the question "Can machines think?". The state of the art in AI is summarized with examples like Deep Blue defeating Kasparov at chess in 1997 and autonomous vehicles driving 98% of the time across the US.
This document provides an overview of the CSC384 Intro to Artificial Intelligence course. It discusses what AI is, including modeling intelligence through computation. It describes different approaches like mimicking humans versus achieving rational behavior. The document outlines key topics that will be covered in the course like search, knowledge representation, planning and probabilistic reasoning. It also provides examples of successes in AI and discusses degrees of intelligence in systems.
This document provides an overview of an introduction to artificial intelligence course. It discusses course administration details like the instructor, TAs, meeting times, grading, and textbook. It then covers topics that will be discussed in the course like what AI is, the ingredients of intelligence, history of AI, applications of AI, and goals of AI. Key problems in AI like representation, search, inference, learning, and planning are also summarized. Different design methodologies like thinking rationally to formalize inference and thinking like humans from a cognitive science perspective are contrasted.
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 document provides an overview of an artificial intelligence course, including:
1) Course mechanics like assignments, quizzes, and policies on cheating.
2) Today's lecture will cover the goals of AI, a brief history, the current state of the art, and three key ideas: search, representation/modeling, and learning.
3) Questions are posed about how to measure intelligence and which tasks, like chess or picking up eggs, are more difficult for robots.
This document provides an overview of artificial intelligence. It defines intelligence as the ability to plan, solve problems, reason, learn, understand new situations, and apply knowledge. AI is described as building intelligent systems that can think and act like humans or rationally. The history of AI is discussed, from its origins in the 1950s to current applications. Key concepts to be learned in the semester include problem solving, machine learning, evolutionary computation, robotics, and intelligent agents. Python and NetLogo will be used as tools.
The document provides an introduction to artificial intelligence, including definitions of AI, a brief history of the field, and the current state of the art. It discusses four views of AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. The textbook advocates the view of "acting rationally" by designing agents that perceive and act to maximize goals. The document also outlines some of the key topics that will be covered in the course, including search, logic, planning, and learning.
- 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.
Artificial intelligence (AI) is concerned with designing intelligent systems, with two key aspects - intelligence and an artificial device. There are different views on what constitutes intelligence - emulating human thought processes, passing the Turing test of indistinguishability from humans, focusing on logical reasoning abilities, or acting rationally to achieve goals. AI research involves problems like perception, reasoning, learning, language understanding, problem-solving and robotics. While today's AI can achieve limited success in tasks like computer vision, robotics, translation and games, it still cannot match human-level abilities in areas like language understanding, planning, learning or exhibiting true autonomy. The history of AI began in the 1950s and has progressed through periods focused on games
Introduction to Artificial Intelligence.pdfgqgy2nsf5x
Artificial intelligence (AI) is the study of intelligent agents that act rationally to maximize their chances of success. The document discusses several key aspects of AI including definitions, goals, foundations, topics, history and applications. Some of the major topics in AI are search, knowledge representation and reasoning, planning, learning, natural language processing, expert systems, and interacting with the environment through vision, speech recognition and robotics.
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.
Lecture 1. Introduction to AI and it's applications.pptDebabrataPain1
This document provides an introduction to artificial intelligence, including definitions of AI, its goals, approaches, and applications. It defines AI as the science and engineering of making intelligent machines, and discusses goals like replicating human intelligence and developing systems that think and act rationally. The document outlines different approaches to AI like hard/strong AI, soft/weak AI, applied AI, and cognitive AI. It also discusses major components and applications of AI like perception, robotics, natural language processing, planning, and machine learning.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
This document provides an overview of artificial intelligence including:
- It defines four approaches to AI: acting humanly through the Turing test, thinking humanly through cognitive modeling, thinking rationally through symbolic logic, and acting rationally as intelligent agents.
- It then summarizes the history of AI from its origins in the 1940s through developments in knowledge-based systems, connectionism, and the emergence of intelligent agents using large datasets.
- Finally, it briefly discusses recent applications of AI such as algorithms used by Facebook, TikTok, and noise cancellation in Zoom calls.
This document provides an overview of an introductory artificial intelligence course. It describes the course topics which include search, logic, probability, and learning techniques. It also summarizes the current state of AI, highlighting successes in logistics, games, natural language processing, vision, robotics, and question answering. The course is intended for juniors and seniors and requires programming skills and exposure to algorithms, calculus, and probability.
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.
This document provides an overview of an introductory course on artificial intelligence. It discusses four views of AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. The textbook advocates the view of "acting rationally" by designing agents that maximize goal achievement given available information. A brief history of AI is also provided, from early work in philosophy, mathematics, and the sciences to landmark developments like Turing's 1950 paper posing the question "Can machines think?". The state of the art in AI is summarized with examples like Deep Blue defeating Kasparov at chess in 1997 and autonomous vehicles driving 98% of the time across the US.
This document provides an overview of the CSC384 Intro to Artificial Intelligence course. It discusses what AI is, including modeling intelligence through computation. It describes different approaches like mimicking humans versus achieving rational behavior. The document outlines key topics that will be covered in the course like search, knowledge representation, planning and probabilistic reasoning. It also provides examples of successes in AI and discusses degrees of intelligence in systems.
This document provides an overview of an introduction to artificial intelligence course. It discusses course administration details like the instructor, TAs, meeting times, grading, and textbook. It then covers topics that will be discussed in the course like what AI is, the ingredients of intelligence, history of AI, applications of AI, and goals of AI. Key problems in AI like representation, search, inference, learning, and planning are also summarized. Different design methodologies like thinking rationally to formalize inference and thinking like humans from a cognitive science perspective are contrasted.
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 document provides an overview of an artificial intelligence course, including:
1) Course mechanics like assignments, quizzes, and policies on cheating.
2) Today's lecture will cover the goals of AI, a brief history, the current state of the art, and three key ideas: search, representation/modeling, and learning.
3) Questions are posed about how to measure intelligence and which tasks, like chess or picking up eggs, are more difficult for robots.
This document provides an overview of artificial intelligence. It defines intelligence as the ability to plan, solve problems, reason, learn, understand new situations, and apply knowledge. AI is described as building intelligent systems that can think and act like humans or rationally. The history of AI is discussed, from its origins in the 1950s to current applications. Key concepts to be learned in the semester include problem solving, machine learning, evolutionary computation, robotics, and intelligent agents. Python and NetLogo will be used as tools.
The document provides an introduction to artificial intelligence, including definitions of AI, a brief history of the field, and the current state of the art. It discusses four views of AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. The textbook advocates the view of "acting rationally" by designing agents that perceive and act to maximize goals. The document also outlines some of the key topics that will be covered in the course, including search, logic, planning, and learning.
- 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.
Artificial intelligence (AI) is concerned with designing intelligent systems, with two key aspects - intelligence and an artificial device. There are different views on what constitutes intelligence - emulating human thought processes, passing the Turing test of indistinguishability from humans, focusing on logical reasoning abilities, or acting rationally to achieve goals. AI research involves problems like perception, reasoning, learning, language understanding, problem-solving and robotics. While today's AI can achieve limited success in tasks like computer vision, robotics, translation and games, it still cannot match human-level abilities in areas like language understanding, planning, learning or exhibiting true autonomy. The history of AI began in the 1950s and has progressed through periods focused on games
Introduction to Artificial Intelligence.pdfgqgy2nsf5x
Artificial intelligence (AI) is the study of intelligent agents that act rationally to maximize their chances of success. The document discusses several key aspects of AI including definitions, goals, foundations, topics, history and applications. Some of the major topics in AI are search, knowledge representation and reasoning, planning, learning, natural language processing, expert systems, and interacting with the environment through vision, speech recognition and robotics.
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.
Lecture 1. Introduction to AI and it's applications.pptDebabrataPain1
This document provides an introduction to artificial intelligence, including definitions of AI, its goals, approaches, and applications. It defines AI as the science and engineering of making intelligent machines, and discusses goals like replicating human intelligence and developing systems that think and act rationally. The document outlines different approaches to AI like hard/strong AI, soft/weak AI, applied AI, and cognitive AI. It also discusses major components and applications of AI like perception, robotics, natural language processing, planning, and machine learning.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
Nucleophilic Addition of carbonyl compounds.pptxSSR02
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
3. Grading
• Grade Distribution
– Midterm 1 - 20
– Midterm 2 – 20
– Project – 20
– Final Exam – 40
• Midterm 1 Date
– Mod 3/1/1435
• Midterm 2 Date
– Mod 3/3/1435
• Project
– Due in Last Week
4. Warning!!!
Any form of cheating is not tolerated and can result in getting an F
in the class
5. Important Notes
• No class next week - Week of Sep 8
• Tutorials may not be held on its scheduled time
• We may have lectures on the tutorial sessions
or tutorials on lecture sessions
6. AI in Fiction
An intelligent killing robot
Smart machines that took over
the human race and made
them live in a simulated world
7. What’s interesting with AI
Search engines
Labor
Science
Medicine/
Diagnosis
Appliances
slide mostly borrowed from Laurent Itti
Movies Recommendation
8. What’s interesting with AI
• Honda AISMO
• Advanced Step in Innovation MObility
• Humanoid Robot
• Capable of recognizing:
• Moving objects
• Postures
• Gestures
• Handshake
• Sounds
• Capable of walking and running
http://en.wikipedia.org/wiki/ASIMO
9. What’s interesting with AI
Darpa Grand Challenge
• To nurture the development of autonomous ground vehicles
• Competition of Driverless vehicles
• 2004
• 1 million
• Mojave Desert
• Follows a route of 240 km
• No one won: best completed 12 km
• 2005
• 2 million dollar prize
• 3 narrow tunnels, 100 sharp turns
• Twisted pass with a drop-off one one side
• Five succeeded
• Winner: 6:54 hours, Stanford Racing Team – Stanely
Urban Grand Challenge
• 2007
• 2 million dollar
• AirForce Base
• To obey to all traffic rules
• 96 km within less than 6 hours
• CMU team won – with 4:10
http://en.wikipedia.org/wiki/DARPA_Grand_Challenge
stanely
10. What’s interesting with AI
• 1996, Deep Blue first machine to beat chess world champion
• But lost in the series – 4 to 2
• 1997, won the series 3.5 to 2.5
• Search 6 to 8 moves a head
• The evaluation function is set by the system after examining thousands of master
games
http://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)
11. Syllabus - Tentative
1. Introduction (Chapter.1)
2. Intelligent Agents (Chapter.2)
3. Solving Problems by Search (Chapter.3 and chapter.4)
4. Constraint satisfaction Problems (Chapter.6).
5. Game Playing(Chapter.5)
6. Logical Agents (Chapter.7)
7. First Order Logic (Chapter.8)
8. Inference in logic (Chapter.9)
9. Classification
13. AI Definition
• The exciting new effort to make computers thinks …
machine with minds, in the full and literal sense”
(Haugeland 1985)
• The automation of activities that we associate with human
thinking, activities such as decision-making, problem
solving, learning,…(Bellman, 1978)
Think Like Humans
14. AI Defintion
• “The art of creating machines that perform functions that
require intelligence when performed by people” (Kurzweil,
1990)
• “The study of how to make computers do things at which, at
the moment, people do better”, (Rich and Knight, 1991)
Act Like Humans
15. AI Definition
• “The study of mental faculties through the use of
computational models”,(Charniak et al. 1985)
• “The study of the computations that make it possible to
perceive, reason and act”,(Winston, 1992)
Think Rationally
16. AI Definition
• “Computational Intelligence is the study of the design of
intelligent agents” (Poole et al, 1998)
• “AI….is concerned with intelligent behavior in artifact”,
(Nilsson, 1998)
Act Rationally
17. How to Achieve AI?
AI
Acting
humanly
Thinking
rationally
Acting
rationally
Thinking
humanly
17
18. Acting Humanly: The Turing Test
CSC 361 Artificial Intelligence 18
• To be intelligent, a program should simply act like a human
Alan Turing
1912-1954
http://en.wikipedia.org/wiki/Turing_test
19. The Turing Test - Example
http://www.ai.mit.edu/projects/infolab/
http://aimovie.warnerbros.com
slide mostly borrowed from Laurent Itti
20. The Turing Test - Example
http://www.ai.mit.edu/projects/infolab/
http://aimovie.warnerbros.com
slide mostly borrowed from Laurent Itti
21. The Turing Test - Example
http://www.ai.mit.edu/projects/infolab/
http://aimovie.warnerbros.com
slide mostly borrowed from Laurent Itti
22. The Turing Test - Example
http://www.ai.mit.edu/projects/infolab/
http://aimovie.warnerbros.com
slide mostly borrowed from Laurent Itti
23. The Turing Test - Example
http://www.ai.mit.edu/projects/infolab/
http://aimovie.warnerbros.com
slide mostly borrowed from Laurent Itti
24. Acting Humanly
24
• To pass the Turing test, the computer/robot needs:
– Natural language processing to communicate successfully.
– Knowledge representation to store what it knows or hears.
– Automated reasoning to answer questions and draw conclusions using
stored information.
– Machine learning to adapt to new circumstances and to detect and
extrapolate patterns.
– These are the main branches of AI.
25. Acting Humanly: The Turing Test
CSC 361 Artificial Intelligence 25
• To be intelligent, a program should simply act like a human
Alan Turing
1912-1954
http://en.wikipedia.org/wiki/Turing_test
+ physical interaction =>
Total Turing Test
- Recognize objects and
gestures
- Move objects
26. Acting Humanly – for Total Turing
• To pass the Turing test, the computer/robot needs:
– Natural language processing to communicate successfully.
– Knowledge representation to store what it knows or hears.
– Automated reasoning to answer questions and draw conclusions using stored
information.
– Machine learning to adapt to new circumstances and to detect and extrapolate
patterns.
– Computer vision to perceive objects. (Total Turing test)
– Robotics to manipulate objects and move. (Total Turing test)
– These are the main branches of AI.
27. Thinking Humanly
27
• Real intelligence requires thinking think like a
human !
• First, we should know how a human think
– Introspect ones thoughts
– Physiological experiment to understand how someone
thinks
– Brain imaging – MRI…
• Then, we can build programs and models that
think like humans
– Resulted in the field of cognitive science: a merger
between AI and psychology.
28. Problems with Imitating Humans
28
• The human thinking process is difficult to
understand: how does the mind raises from
the brain ? Think also about unconscious tasks
such as vision and speech understanding.
• Humans are not perfect ! We make a lot of
systemic mistakes:
29. Thinking Rationally
29
• Instead of thinking like a human : think rationally.
• Find out how correct thinking must proceed: the laws
of thought.
• Aristotle syllogism: “Socrates is a man; all men are
mortal, therefore Socrates is mortal.”
• This initiated logic: a traditional and important branch
of mathematics and computer science.
• Problem: it is not always possible to model thought as
a set of rules; sometimes there uncertainty.
• Even when a modeling is available, the complexity of
the problem may be too large to allow for a solution.
30. Acting Rationally
30
• Rational agent: acts as to achieve the best outcome
• Logical thinking is only one aspect of appropriate behavior:
reactions like getting your hand out of a hot place is not the
result of a careful deliberation, yet it is clearly rational.
• Sometimes there is no correct way to do, yet something
must be done.
• Instead of insisting on how the program should think, we
insist on how the program should act: we care only about
the final result.
• Advantages:
– more general than “thinking rationally” and more
– Mathematically principled; proven to achieve rationality unlike
human behavior or thought
31. Acting Rationally
31
This is how birds fly Humans tried to mimic
birds for centuries
This is how we finally
achieved “artificial flight”
32. Relations to Other Fields
CSC 361 Artificial Intelligence 32
• Philosophy
– Logic, methods of reasoning and rationality.
• Mathematics
– Formal representation and proof, algorithms, computation, (un)decidability, (in)tractability,
probability.
• Economics
– utility, decision theory (decide under uncertainty)
• Neuroscience
– neurons as information processing units.
• Psychology/Cognitive Science
– how do people behave, perceive, process information, represent knowledge.
• Computer engineering
– building fast computers
• Control theory
– design systems that maximize an objective function over time
• Linguistics
– knowledge representation, grammar
slide mostly borrowed from Max Welling
33. AI History
• Gestation of AI (1934 - 1955)
– In 1943, proposed a binary-based model of neurons
– Any computable function can be modeled by a set of neurons
– A serious attempt to model brain
– 1950, Turing’s “Computing Machinery and Intelligence ”: turing test,
reinforcement learning and machine learning
• The Inception of AI (1956)
– Dartmouth meeting to study AI
– an AI program ”Logic Theorist” to prove many theorems
• Early Enthusiasm and great Expectation (1952-1969)
– General Problem Solver imitates the human way of thinking
– LISP (AI programming language) was defined
– 1965, Robinson discovered the resolution method – logical reasoning
• AI Winter (1966-1973)
– Computational intractability of many AI problems
– Neural Network starts to disappear
34. AI History
• Knowledge-based systems (1969-1979)
– Use domain knowledge to allow for stronger reasoning
• Becomes an Industry (1980-now)
– Digital Equipment Corporation selling R1 “expert sytem”
– From few million to billions in 8 years
• The return of neural network (1986-now)
– With the back-propagation algorithm
• AI adopts scientific method (1987-now)
– More common to base theorems on pervious ones or rigorous evidence rather
than intuition
– Speech recognition and HMM
• Emergence of intelligent agent (1995-now)
– search engines, recommender systems,….
• Availability of very large data sets (2001 – now)
– Worry more about the data
35. The State of the Art
• Robotics Vehicle
– DARPA Challenge
• Speech Recognition
– United Airlines
• Autonomous Planning and Scheduling
– Remote Agent: Plan and control spacecraft
– MAPGEN: daily planning of operations on NASA’s exploration Rover
• Game Playing
– IBM Deep Blue
• Spam Fighting
• Logistic Planning
– DART – Dynamic Analysis and Replacing Tool
– Gulf War 1991
– To plan the logistic for transportation of 50k vehicles, cargo and people
– Generated in hour a plan that could take weeks
• Robotics
• Machine Translation
– Statistical models
36. Summary
CSC 361 Artificial Intelligence 36
• This course is concerned with creating rational agents:
artificial rationality.
• AI has passed the era of infancy and is now attacking real
life, complex problems, and it is succeeding in many of
them.
• The history of AI has had a turbulent history with many ups
and downs, phenomenal successes and deep
disappointments resulting in fund cutbacks and economic
losses.
• AI has flourished in the last two decades and it the
researchers mentality shifted towards a rigorous scientific
methodology:
Firm theoretical basis & Serious experiments