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.
Hpai class 11 - online potpourri - 032320melendez321
The document appears to be notes from a university class on the human perspective in artificial intelligence. It includes the class agenda, topics discussed such as memory and the HSI model, and a student's requested future topics including challenges, modeling, influence tactics, and programming. The notes provide an overview of concepts covered in the class and indicate a focus on understanding human factors in AI development.
This document provides an overview of an artificial intelligence course taught at Jahan University in Kabul, Afghanistan. The course covers topics such as the history of AI, knowledge representation, machine learning, and robotics. It aims to help students understand different approaches to AI and implications for cognitive science. Learning outcomes include expanding knowledge of search techniques, planning algorithms, knowledge representation, and machine learning programming. Required materials include an AI textbook and reference books. The lecture discusses definitions of intelligence and AI, modern successes in the field, and the state of technologies like speech recognition, computer vision, and planning.
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 summarizes research on understanding opinions and attitudes towards situated technology use. It describes conducting interviews in various public settings to understand perceptions of appropriateness and norms around device use. Key findings include people differentiating places based on focus and privacy expectations, having different standards of appropriateness for themselves versus others, and limited experience with and views of technologies like Siri and Kinect. Future work involves analyzing the data to identify themes and reconsidering target audiences. Lessons learned highlight challenges in research design, data collection and analysis.
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.
This document provides an overview of an AI course titled "Human Perspective in Artificial Intelligence". It includes the course professor's information, upcoming class topics such as linguistics and inner voice, exam and assignment details, and summaries of class content. The document outlines an upcoming class discussion on inner voice that will involve analyzing a letter from Albert Einstein describing his thought processes without speaking out loud as primarily visual and some muscular in nature. It also announces an open question and answer session to help prepare for an upcoming exam.
HPAI Class 2 - human aspects and computing systems in ai - 012920melendez321
This document outlines the topics that will be covered in a course on Human Perspective in Artificial Intelligence. It includes a reading from Herbert Simon on finding satisfactory solutions for realistic worlds. It then lists additional required readings on modeling human thought and behavior. The next sections will cover modeling human intelligence and decision making, assessing whether current AI has reached human-level intelligence, and discussing artificial general intelligence. The document provides an overview of the Human Systems Interconnection model for understanding human thought and behavior. It outlines upcoming homework assignments applying this model. Finally, it previews a discussion on the anthropic robot Sophia and whether current AI exhibits human-like characteristics.
This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.
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What is Artificial Intelligence(AI)? , Evolution , Applications of AI? , Features of AI , What is Intelligence and its types?,
What are Agents and Environment? , Fear of AI , Machine Learning , Difference between AI, ML and Deep Learning ,
Applications of ML , Algorithms of AL and ML , Future of AI
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This document provides an overview of an online course on human perspectives in artificial intelligence. It discusses approaches to artificial general intelligence including biological, cognitive, psychological, and social approaches. It lists upcoming topics on cognitive architectures in AGI, models of intelligence presented by various professors, principles of synthetic intelligence, and using Google Colab and text classification demonstrations. The homework assignment asks students to modify a Google Colab text classification example to classify Amazon reviews of a product into 1 to 5 star categories based on a CSV file of reviews created by the student. It explains what a user must do to input a review and have the code predict its star category.
Developing 21st-Century Skills with Multimodal Learning AnalyticsXavier Ochoa
Collaboration, communication, creativity, critical thinking and problem-solving are among the skills that are needed to study and work in this 21st century. As important as they are, evaluating, assessing and teaching them in a practical, scalable and efficient way is still a challenge not fully met by current pedagogical-technological practices. Multimodal Learning Analytics (MmLA), the processing and analysis of multiple sources of data to better understand and improve learning processes, has been posed as a possible solution to augment the natural capabilities of both instructors and students to provide and receive feedback to support the development of those skills. During this session, we will have a hands-on demo of two systems to automatically generate feedback for communication and collaboration skills; then, we will explore the affordances that low-cost sensors and current advances in artificial intelligence provide to automatically record and analyze face-to-face, complex learning processes as those involved for the development of 21st-Century Skills. Finally, we will discuss and ideate practical MmLA tools that could be built to augment your current teaching and learning practices.
Presentation at NYU - November 2019.
Supporting the Acquisition of 21st Century Skills through Multimodal Learning...Xavier Ochoa
Collaboration, communication, creativity, critical thinking and problem-solving are among the skills that are needed to study and work in this 21st century. As important as they are, evaluating, assessing and teaching them in a practical, scalable and efficient way is still a challenge not fully met by current pedagogical-technological practices. Multimodal Learning Analytics (MmLA), the processing and analysis of multiple sources of data to better understand and improve learning processes, has been posed as a possible solution to augment the natural capabilities of both instructors and students to provide and receive feedback to support the development of those skills. During this session, we will explore the affordances that low-cost sensors and current advances in artificial intelligence provide to automatically record and analyze face-to-face, complex learning processes as those involved for the development of 21st-Century Skills. Finally, we will discuss and ideate practical MmLA tools that could be built to augment your current teaching and learning practices.
Artificial intelligence (AI) aims to build intelligent machines that can perform tasks requiring human intelligence. The goals of AI are to better understand human intelligence by modeling it in computer programs, and to create useful programs that can perform expert tasks. Many disciplines contribute to AI including computer science, psychology, philosophy, linguistics, and biology. Typical AI problems involve both mundane tasks like shopping and expert tasks like medical diagnosis. Philosophical issues in AI include what intelligence is, whether machines can truly be intelligent, and if human intelligence can be reduced to rules and calculations. This module will cover AI programming, knowledge representation, search techniques, natural language processing, machine learning, intelligent agents, and knowledge engineering.
The document discusses a class on human perspective in artificial intelligence taught by Professor José Meléndez. It includes a quote from Professor Marvin Minsky about the difficulty of using language to describe non-linguistic cognitive functions. The document lists required readings from Minsky's book "Society of Mind" and announces upcoming class topics like language, learning, and mental models. It also provides information on exams, homework, and videos related to early language development in children.
This document provides an overview and introduction to the topic of artificial intelligence from the textbook by Russell and Norvig. It defines AI as using computational methods to automate tasks that require human intelligence such as reasoning, problem-solving, and learning. The document discusses different definitions of AI and how its goal is to create computer systems that can perform intelligent tasks rationally rather than replicating human imperfections. It also outlines some of the major areas and achievements of AI as well as open questions regarding whether machines can truly exhibit human-like intelligence.
RING panel discussion, Coling 2010 ( E. Hovy + M. Zock)Michael Zock
This document discusses the state of computational linguistics (CL) and potential directions for the field to pursue. It argues that CL has become focused on solving challenge problems and exploring new techniques without developing an underlying theory. Large problems like machine translation, speech recognition, and question answering remain unsolved. The document suggests CL could benefit from drawing on fields like linguistics, psycholinguistics, cognitive science, and information theory, but outlines reasons why linguistics and psycholinguistics may not provide useful theories for CL, such as studying different phenomena and lacking methodology to verify claims. It leaves open which fields may offer insights to help develop a theoretical foundation for CL.
Introduction to Artificial IntelligenceSanjay Kumar
This presentation talks about what is Artificial Intelligence, what are key Algorithms (CNN, RNN, Reinforcement Learning), their applications. AI use cases such as detecting fish species and Spoting Distracted Driver
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The tech landscape is changing faster than ever, and this trend is only accelerating. How do technical people grow when the world is changing faster than you can finish your next cup of coffee? How does one keep up? What should you learn? Are there things we can focus our learning efforts on besides just the next tech flavour of the month? This talk will explore some areas of learning that technical people might not intentionally consider. These areas focus on higher-level concerns, with slower rates of change, which can help technical people develop into more well-rounded individuals.
Software development is not exactly the same as computer programming. When it comes to a career, development for productization introduces many more things than simply coding. It is important to learn how to accomplish tasks, sharpen skills, develop the career and enjoy it. And last but not the least, how to start?
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.
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.
AILABS - Lecture Series - Is AI the New Electricity? - Advances In Machine Le...AILABS Academy
Prof. Garain discusses in brief on the backgrounds of learning algorithms & major breakthroughs that have been made in the field of machine perception in the last 50 yrs. He also discusses the role of statistical algorithms like artificial neural network, support vector machines, and other concepts related to Deep Learning algorithms.
Along with the above, Prof. Garain touched upon the basics of CNN & RNN, Long Short-Term Memory Networks (LSTM) & attention network & illustrate all of these using real-life problems. Several state-of-the-art problems like image captioning, visual question answering, medical image analysis etc. were discussed to make the potential of deep learning algorithms understandable.
Prof. Utpal Garain is one of the leading minds in Kolkata in the field of Neural Networks & Artificial Intelligence. His research interest is now focused on AI research, especially exploring deep learning methods for language, image and video analysis including NLP tools, OCRs, handwriting analysis, computational forensics and the like.
Artificial intelligence- The science of intelligent programsDerak Davis
Artificial intelligence (AI) involves creating intelligent computer programs and machines that can interact with the real world similarly to humans. AI uses techniques like machine learning, deep learning, and neural networks to allow programs to learn from data and experience without being explicitly programmed. While AI has potential benefits, some experts warn that advanced AI could pose risks if not developed carefully due to concerns it could become difficult for humans to control once a certain level of intelligence is achieved.
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
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]
Artificial intelligence (AI) involves developing intelligent machines that can perform tasks normally requiring human intelligence. This document provides an overview of key concepts in AI including definitions of intelligence, AI, and artificial intelligence. It discusses different approaches to AI such as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally using logical reasoning, and systems that act rationally as intelligent agents. The document also briefly outlines the history and foundations of the AI field.
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.
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.
Artificial intelligence or AI in short is the latest technology on which the whole world is working today. We at myassignmenthelp.net are providing help with all the assignments and projects. So when ever you need help with any work related to AI feel free to get in touch
The document discusses artificial intelligence and its history. It defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. It describes how AI works using approaches like machine learning, deep learning, and artificial neural networks. The document traces the origins and development of AI from its coining in 1956 to modern advances in algorithms, machine learning, and integrating statistical analysis. It discusses landmark concepts like the Turing Test and the development of expert systems using programming languages like LISP and PROLOG. The document also notes some limitations of current AI technologies like software interoperability, knowledge acquisition, and handling uncertainty.
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
This document discusses artificial intelligence and whether machines can think. It notes that computers have advantages like calculation, communication, and processing information systematically, while humans have advantages like perception, reasoning with ambiguity, and applying knowledge. The document discusses the Turing Test for determining intelligence and notes that no computer has passed. It examines whether Deep Blue's chess playing constituted intelligence and discusses ongoing challenges with language translation. It also outlines techniques used in AI like heuristics, pattern recognition, and machine learning.
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 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.
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
While artificial intelligence (AI) is often referred to in popular culture, in reality AI encompasses a broad range of technologies and applications. Some common examples of AI that are already widely used include search algorithms, personalized recommendations, and computer vision technologies. However, these applications do not necessarily constitute strong or general human-level AI. There is no consensus on how to define AI, and its potential capabilities and limitations are actively debated. Overall, AI is an evolving field with many existing real-world uses today, even if more advanced visions of superintelligence remain hypothetical.
Artificial intelligence (AI) is software that allows computers and robots to perform tasks in a way that mimics human intelligence. John McCarthy first proposed the term "artificial intelligence" in 1956. AI uses techniques like machine learning, natural language processing, and computer vision to perform tasks previously only done by humans, such as playing games, recognizing speech, and understanding language. While AI has advantages like efficiency, reliability, and ability to handle complex tasks, it also has drawbacks like limited ability and lack of complete human traits. The ultimate goal of AI research is to solve problems humans cannot.
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.
Literature Reivew of Student Center DesignPriyankaKarn3
It was back in 2020, during the COVID-19 lockdown Period when we were introduced to an Online learning system and had to carry out our Design studio work. The students of the Institute of Engineering, Purwanchal Campus, Dharan did the literature study and research. The team was of Prakash Roka Magar, Priyanka Karn (me), Riwaz Upreti, Sandip Seth, and Ujjwal Dev from the Department of Architecture. It was just a scratch draft made out of the initial phase of study just after the topic was introduced. It was one of the best teams I had worked with, shared lots of memories, and learned a lot.
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In May 2024, globally renowned natural diamond crafting company Shree Ramkrishna Exports Pvt. Ltd. (SRK) became the first company in the world to achieve GNFZ’s final net zero certification for existing buildings, for its two two flagship crafting facilities SRK House and SRK Empire. Initially targeting 2030 to reach net zero, SRK joined forces with the Global Network for Zero (GNFZ) to accelerate its target to 2024 — a trailblazing achievement toward emissions elimination.
A brand new catalog for the 2024 edition of IWISS. We have enriched our product range and have more innovations in electrician tools, plumbing tools, wire rope tools and banding tools. Let's explore together!
20CDE09- INFORMATION DESIGN
UNIT I INCEPTION OF INFORMATION DESIGN
Introduction and Definition
History of Information Design
Need of Information Design
Types of Information Design
Identifying audience
Defining the audience and their needs
Inclusivity and Visual impairment
Case study.
3. Who is this course for?
• An introductory survey of AI techniques for
students who have not previously had an
exposure to this subject
• Juniors, seniors, beginning graduate students
• Prerequisites: solid programming skills,
algorithms, calculus
• Exposure to linear algebra and probability a plus
• Credit: 3 units
4. Basic Info
• Instructor: Svetlana Lazebnik (lazebnik@cs.unc.edu)
Office hours: email me
• Textbook: S. Russell and P. Norvig, Artificial
Intelligence: A Modern Approach, Prentice Hall, 2nd or
3rd ed. http://aima.cs.berkeley.edu/
• Class webpage:
http://www.cs.unc.edu/~lazebnik/fall10
5. Course Requirements
• Participation: 20%
• Come to class!
• Ask questions
• Answer questions
• Participate in discussions
• Assignments: 50%
• Written and programming
• Programming assignments: you can use whatever language
you wish. The focus is on problem solving, not specific
programming skills.
• Midterm/final: 30%
• No book, no notes, no calculator, no collaboration
• Not meant to be scary
• Mainly straightforward questions testing comprehension
6. Academic integrity policy
• Feel free to discuss assignments with each
other, but coding must be done individually
• Feel free to incorporate code or tips you find
on the Web, provided this doesn’t make the
assignment trivial and you explicitly
acknowledge your sources
• Remember: I can Google as well as you can
7. What is AI?
Some possible definitions from the textbook:
• Thinking humanly
• Acting humanly
• Thinking rationally
• Acting rationally
8. Thinking humanly
• Cognitive science: the brain as an information
processing machine
• Requires scientific theories of how the brain works
• How to understand cognition as a
computational process?
• Introspection: try to think about how we think
• Predict and test behavior of human subjects
• Image the brain, examine neurological data
• The latter two methodologies are the domains
of cognitive science and cognitive neuroscience
9. • Turing (1950) "Computing machinery and intelligence"
• The Turing Test
• What capabilities would a computer need to have to pass
the Turing Test?
• Natural language processing
• Knowledge representation
• Automated reasoning
• Machine learning
• Turing predicted that by the year 2000, machines would
be able to fool 30% of human judges for five minutes
Acting humanly
10. • What are some potential problems with the Turing Test?
• Some human behavior is not intelligent
• Some intelligent behavior may not be human
• Human observers may be easy to fool
• A lot depends on expectations
• Anthropomorphic fallacy
• Chatbots, e.g., ELIZA
• Chinese room argument: one may simulate intelligence without
having true intelligence (more of a philosophical objection)
• Is passing the Turing test a good scientific goal?
• Not a good way to solve practical problems
• Can create intelligent agents without trying to imitate humans
Turing Test: Criticism
11. Thinking rationally
• Idealized or “right” way of thinking
• Logic: patterns of argument that always yield correct
conclusions when supplied with correct premises
• “Socrates is a man; all men are mortal; therefore Socrates is mortal.”
• Beginning with Aristotle, philosophers and mathematicians
have attempted to formalize the rules of logical thought
• Logicist approach to AI: describe problem in formal logical
notation and apply general deduction procedures to solve it
• Problems with the logicist approach
• Computational complexity of finding the solution
• Describing real-world problems and knowledge in logical notation
• A lot of intelligent or “rational” behavior has nothing to do with logic
12. Acting rationally: Rational agent
• A rational agent is one that acts to achieve the best
expected outcome
• Goals are application-dependent and are expressed in terms
of the utility of outcomes
• Being rational means maximizing your expected utility
• In practice, utility optimization is subject to the agent’s
computational constraints (bounded rationality or bounded
optimality)
• This definition of rationality only concerns the
decisions/actions that are made, not the cognitive
process behind them
13. Acting rationally: Rational agent
• Advantages of the “utility maximization” formulation
• Generality: goes beyond explicit reasoning, and even human
cognition altogether
• Practicality: can be adapted to many real-world problems
• Amenable to good scientific and engineering methodology
• Avoids philosophy and psychology
• Any disadvantages?
14. AI Connections
Philosophy logic, methods of reasoning, mind vs. matter,
foundations of learning and knowledge
Mathematics logic, probability, optimization
Economics utility, decision theory
Neuroscience biological basis of intelligence
Cognitive science computational models of human intelligence
Linguistics rules of language, language acquisition
Machine learning design of systems that use experience to
improve performance
Control theory design of dynamical systems that use a
controller to achieve desired behavior
Computer engineering, mechanical engineering, robotics, …
16. Logistics, scheduling, planning
• During the 1991 Gulf War, US forces
deployed an AI logistics planning and
scheduling program that involved up to
50,000 vehicles, cargo, and people
• NASA’s Remote Agent software operated the
Deep Space 1 spacecraft during two
experiments in May 1999
• In 2004, NASA introduced the MAPGEN
system to plan the daily operations for the
Mars Exploration Rovers
17. Math, games, puzzles
• In 1996, a computer program written by researchers
at Argonne National Laboratory proved a
mathematical conjecture (Robbins conjecture)
unsolved for decades
• NY Times story: “[The proof] would have been called
creative if a human had thought of it”
• IBM’s Deep Blue defeated the reigning world chess
champion Garry Kasparov in 1997
• 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.”
• In 2007, checkers was “solved” --- a computer
system that never loses was developed
• Science article
18. Natural Language
• Speech technologies
• Automatic speech recognition
• Google voice search
• Text-to-speech synthesis
• Dialog systems
• Machine translation
• translate.google.com
• Comparison of several translation systems
19. Question answering: IBM Watson
• http://www.research.ibm.com/deepqa/
• NY Times article
• Trivia demo
• YouTube video
20. Information agents
• Search engines
• Recommendation systems
• Spam filtering
• Automated helpdesks
• Medical diagnosis systems
• Fraud detection
• Automated trading
21. Vision
• OCR, handwriting recognition
• Face detection/recognition: many consumer
cameras, Apple iPhoto
• Visual search: Google Goggles
• Vehicle safety systems: Mobileye
22. Robotics
• Mars rovers
• Autonomous vehicles
• DARPA Grand Challenge
• Autonomous helicopters
• Robot soccer
• RoboCup
• Personal robotics
• Humanoid robots
• Robotic pets
• Personal assistants?
23. Towel-folding robot
J. Maitin-Shepard, M. Cusumano-Towner, J. Lei and P. Abbeel,
“Cloth Grasp Point Detection based on Multiple-View Geometric
Cues with Application to Robotic Towel Folding,” ICRA 2010
YouTube Video
24. Course Topics
• Search
• Uninformed search, informed search
• Adversarial search: minimax
• Constraint satisfaction problems
• Planning
• Logic
• Probability
• Basic laws of probability
• Bayes networks
• Hidden Markov Models
• Learning
• Decision trees
• Linear classifiers: neural nets, support vector machines
• Reinforcement learning
25. Course Topics (cont.)
• Applications (depending on time and interest)
• Natural language
• Speech
• Vision
• Robotics